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This paper aims to investigate whether Chinese internal control regulation improves reporting quality. After the enactment of US Sarbanes–Oxley Act (SOX), China introduced a quasi-SOX practice (C-SOX). C-SOX stipulates that firms should disclose both management and audit reports on internal control and aims to help firms ensure reporting reliability. Previous studies provide solid evidence of the effectiveness of SOX, but the conclusion cannot simply be generalized to emerging markets. We employ a modified difference-in-difference approach regarding the special batched implementation schedule of C-SOX and observe earnings management during the reform, finding that accrual-based earnings quality is enhanced significantly after compliance with C-SOX without causing more real activity manipulation. Our results thus show that C-SOX has a positive effect on reporting quality and triggers no side-effects harming firms’ long-term value. Our findings suggest that the mandatory disclosure regime of C-SOX contributes to better corporate disclosure even with weak enforcement.

The short beam shear performance of GLARE 3A-3/2 laminates with adhesive layers was investigated by combining the short beam test and the digital image correlation technique. The failure behavior was further analyzed based on finite element simulation and micro failure morphology. The results show an 8% and 58% difference in the short beam strength and bending displacement at failure of laminates along two orthogonal directions; The damage behavior of laminates is determined by the bottom unidirectional glass fiber reinforced plastic (GFRP) layers. The two typical failure modes are matrix and fiber fracture in the GFRP layer caused by local bending deformation, and interlaminar delamination between GFRP layers; The distribution of surface strain [Formula: see text] indicates the damage initiation and evolution process. The simulation result of the finite element model established in ABAQUS/Explicit shows consistency with digital image correlation analysis, which provides an effective method to predict the damage behavior of specimens with different ply structures.

In the field of high-voltage and high-power photovoltaic (PV) power generation, three-phase cascaded H-bridge (CHB) inverter has been attracting more and more attention because of its easy modularization and high output voltage. Compared with other structures, the three-phase CHB inverter based on common dc-bus structure has more advantages, which is capable of avoiding interphase and intermodule power imbalance problems. When the grid voltage is balanced, the total active power transmitted by each phase of the three-phase CHB inverter is the same, and because the number of modules in each phase is designed to be equal, the active power transmitted by all modules is the same. However, if the grid voltage is unbalanced or some modules fail, the module power in different phases may be unequal, which makes heat dissipation requirements and service life of each module significantly different. In this article, a module power balance control strategy is proposed, it ensures that all modules still transmit equal powers when the grid voltage is unbalanced, or some modules fail. Simulation and experimental results all verify the effectiveness of the proposed strategy.

The inhibitory effect of dimethyl methylphosphonate (DMMP) on coal spontaneous combustion was investigated. The influence of DMMP on the thermal mass loss and thermal properties in the coal oxidati...

To pursue higher compensation, the agent’s earnings management behavior may damage the principal’s interests. Can managers whose compensation reaches the expected level seek benefits for the company through earnings management? This study takes China’s A-share listed companies from 2014 to 2018 as samples. The conclusions show that managers with higher compensation levels will carry out earnings management in favor of the company while taking their own interests into consideration. For companies with stronger profitability, the higher the managers’ compensation is, the more they are inclined to reduce accrued earnings in the current period to further reduce taxes and fees. For companies with weaker profitability, managers with higher compensation tend to choose to increase real earnings to further optimize financial indicators. It has been found through further research that high pressure generated by media attention can make well-paid executives restrain the above earnings management behavior, which serves as an effective method to protect investors’ rights and interests.

In order to investigate P-containing flame retardant on coal spontaneous combustion, trisodium phosphate, sodium hexametaphosphate, and ammonium polyphosphate were selected. Raw coal and inhibitor ...

Motivation In most tissue-based biomedical research, the lack of sufficient pathology training images with well-annotated ground truth inevitably limits the performance of deep learning systems. In this study, we propose a convolutional neural network with foveal blur enriching datasets with multiple local nuclei regions of interest derived from original pathology images. We further propose a human-knowledge boosted deep learning system by inclusion to the convolutional neural network new loss function terms capturing shape prior knowledge and imposing smoothness constraints on the predicted probability maps. Results Our proposed system outperforms all state-of-the-art deep learning and non-deep learning methods by Jaccard coefficient, Dice coefficient, Accuracy and Panoptic Quality in three independent datasets. The high segmentation accuracy and execution speed suggest its promising potential for automating histopathology nuclei segmentation in biomedical research and clinical settings. Availability and implementation The codes, the documentation and example data are available on an open source at: https://github.com/HongyiDuanmu26/FovealBoosted. Supplementary information Supplementary data are available at Bioinformatics online.

In this article, the zero voltage switching (ZVS) of the inverter in the electric vehicle wireless charging system is realized through the parameter optimization design of compensation components, which is conducive to the high efficient operation of the system. The effects of the parameter adjustment of each compensation component on the input impedance angle and output current of the double-side LCC compensation topology were analyzed under the conditions of different output voltages and coupling coefficients, which provides a basis for the component optimization design of the inverter ZVS realization. The quantitative relationship between the optimized component parameter and the soft switching implementation conditions was analyzed. Then, a specific parameter design method was proposed, according to the results analyzed earlier. At last, the effectiveness of the proposed method was verified by a 3.3-kW wireless charging system prototype. Experimental results show that the optimized system is always able to achieve soft switching, the system efficiency is up to 95.2% at the rated power, and it is higher than 93% even under the condition where the coupling coefficient and output voltage are both reduced by half.

BackgroundThe United States is in the midst of an opioid overdose epidemic. We evaluated the temporal trends and risk factors of inpatient opioid overdose. Based on the opioid overdose patterns, we further examined the innate properties underlying less overdose events.MethodsWe conducted a retrospective cross-sectional study based a large-scale inpatient electronic health records database, Cerner Health Facts®. We included patients admitted between January 1, 2009 and December 31, 2017. Opioid overdose prevalence by year, demographics and prescription opioid exposures.ResultsA total of 4,720,041 patients with 7,339,480 inpatient encounters were retrieved from Cerner Health Facts®. Among them, 30.2% patients were aged 65+, 57.0% female, 70.1% Caucasian, 42.3% single, 32.0% from South and 80.8% in urban area. From 2009 to 2017, annual opioid overdose prevalence per 1,000 patients significantly increased from 3.7 to 11.9 with an adjusted odds ratio (aOR): 1.16, 95% confidence interval (CI): [1.15-1.16]. Comparing to the major demographic counterparts above, being in 1) age group: 41-50 (overall aOR 1.36, 95% CI: [1.31-1.40]) or 51-64 (overall aOR 1.35, 95% CI: [1.32-1.39]), marital status: divorced (overall aOR 1.19, 95% CI: [1.15-1.23]), 3) census region: West (overall aOR 1.32, 95% CI: [1.28-1.36]), were significantly associated with higher odds of opioid overdose. Prescription opioid exposures were also associated with increased odds of opioid overdose, such as meperidine (overall aOR 1.09, 95% CI: [1.06-1.13]) and tramadol (overall aOR 2.20. 95% CI: [2.14-2.27]). Examination on the relationships between opioid agonists’ properties and their association strengths, aORs, in opioid overdose showed that lower aORs values were significantly associated with 1) high molecular weight, 2) negative interaction with multi-drug resistance protein 1 (MDR1) or positive interaction with cytochrome P450 3A4 (CYP3A4) and 3) negative interaction with delta opioid receptor (DOR) or kappa opioid receptor (KOR).ConclusionsThe significant increasing trends of opioid overdose at the inpatient care setting from 2009 to 2017 indicated an ongoing need of efforts to combat the opioid overdose epidemic in the US. Risk factors associated with opioid overdose included patient demographics and prescription opioid exposures. Different prescription opioids were associated with opioid overdose to different extents, indicating a necessity to better differentiate them during prescribing practice. Moreover, there are physicochemical, pharmacokinetic and pharmacodynamic properties underlying less overdose events, which can be utilized to develop better opioids.Key PointsThere were significant increasing trends of opioid overdose at the US inpatient care setting from 2009 to 2017, showing an ongoing need for opioid overdose prevention.Different prescription opioids were associated with opioid overdose to different extents, indicating a necessity to differentiate prescription opioids during prescribing.The optimal properties underlying less overdose events mined from the large-scale, real-world electronic health records hold high potential to guide the development of better opioids with reduced overdose effects.

The United States is experiencing an opioid epidemic, and there were more than 10 million opioid misusers aged 12 or older each year. Identifying patients at high risk of Opioid Use Disorder (OUD) can help to make early clinical interventions to reduce the risk of OUD. Our goal is to predict OUD patients among opioid prescription users through analyzing electronic health records with machine learning and deep learning methods. This will help us to better understand the diagnoses of OUD, providing new insights on opioid epidemic. Electronic health records of patients who have been prescribed with medications containing active opioid ingredients were extracted from Cerner Health Facts database between January 1, 2008 and December 31, 2017. Long Short-Term Memory (LSTM) models were applied to predict opioid use disorder risk in the future based on recent five encounters, and compared to Logistic Regression, Random Forest, Decision Tree and Dense Neural Network. Prediction performance was assessed using F-1 score, precision, recall, and AUROC. Our temporal deep learning model provided promising prediction results which outperformed other methods, with a F1 score of 0.8023 and AUCROC of 0.9369. The model can identify OUD related medications and vital signs as important features for the prediction. LSTM based temporal deep learning model is effective on predicting opioid use disorder using a patient past history of electronic health records, with minimal domain knowledge. It has potential to improve clinical decision support for early intervention and prevention to combat the opioid epidemic., Comment: 20 pages, 6 figures

Organochlorine pesticides (OCPs) have been used worldwide on an enormous scale over the last century but are banned globally due to environmental persistence and ecotoxicity in recent decades. The long-term effects of OCP ban for agricultural use in China since 1983 on the reproductive health of small terrestrial mammals have never been evaluated in the field. We examined the residue dynamics of OCPs and the reproductive performance of Chinese striped hamsters (Cricetulus barabensis) in North China Plain during 1983-2010 and concluded that the exposure levels of OCPs in hamsters drastically decreased from 2900 ± 740 to 25.2 ± 6.88 ng/g with an average half-life of 5.08 yrs, coinciding with the observed reproductive recovery of hamsters. The population-based reproductive performance of hamsters was significantly and negatively associated with OCP exposure levels after adjusting the contributions from climate and population density factors, indicating that the ban of OCPs has facilitated the reproductive recovery of hamsters by up to 81% contribution. Our findings suggest that the OCP ban is effective to restore reproduction of small terrestrial mammals. Integration of population biology and environmental science is essential to assess the impacts of persistent organic pollutants on ecological safety and biodiversity loss under accelerated global change.

Three-phase photovoltaic (PV) solid-state transformer based on cascaded H-bridge topology has a considerable prospect in high-voltage and high-power applications. Normally, each phase of the three-phase converter is designed to have the same number of modules, each of which is composed of an isolated dc–dc converter in cascade with an HB, and all the modules are able to transmit the same active power. However, if partial modules fail, the number of actual operation modules in three phases may be different, resulting in the active powers of the modules in the fault phase higher than those in the normal phase. The unbalanced active powers cause different heat dissipation requirements, violating the principle of modular design. For this issue, this article proposes a power balance control method which calculates a zero-sequence voltage to be injected into the system based on the number of modules in three phases and is able to ensure that all modules will have almost the same active powers under fault conditions. In addition, combined with redundancy design, it is also analyzed that all modules do not suffer from overmodulation even if the amplitudes of modulation voltages increase after being compensated by the proposed zero-sequence voltage. The validity of the proposed method is verified by simulation and experimental results.

This report describes the development and finalization of the 28th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2020), held virtually online, November 3-6, 2020. The attendance for the 2020 conference was 999, the highest in the history of ACM SIGSPATIAL.

Liver malignancies include primary and metastatic tumors. Limited progress has been achieved in improving the survival rate of patients with advanced stage liver cancer and who are unsuitable for surgery. Apart from surgery, chemoradiotherapy, trans-arterial chemoembolization and radiofrequency ablation, a novel therapeutic modality is needed for the clinical treatment of liver cancer. Photodynamic therapy (PDT) is a novel strategy for treating patients with advanced cancers; it uses a light-triggered cytotoxic photosensitizer and a laser light. PDT provides patients with a potential treatment approach with minimal invasion and low toxicity, that is, the whole course of treatment is painless, harmless, and repeatable. Therefore, PDT has been considered an effective palliative treatment for advanced liver cancers. To date, PDT has been used to treat hepatocellular carcinoma, cholangiocarcinoma, hepatoblastoma and liver metastases. Clinical outcomes reveal that PDT can be considered a promising treatment modality for all liver cancers to improve the quality and quantity of life of patients. Despite the advances achieved with this approach, several challenges still impede the application of PDT to liver malignancies. In this review, we focus on the recent advancements and discuss the future prospects of PDT in treating liver malignancies.

Background: Timely sharing of electronic health records across providers, while ensuring data security and privacy, is essential for prompt care of cancer patients, as well as for the development of medical research and the enhancement of personalized medicine. Yet, it is not trivial to achieve efficient consent management, data exchange, and access-control policy enforcement, in particular, in decentralized settings, and given the gravity of the condition such as cancer. Using blockchain technology (BCT) has been recently advocated by research communities and gained momentum from the industry perspective. However, most of the proposed solutions are at the level of a prototype, and blockchain-based healthcare data management systems are not in place yet. Summary:This paper presents a systematic literature review, aiming to analyze the motivations, advantages, and limitations, as well as barriers and future challenges faced when applying the state-of-the-art distributed ledger technology in oncology. We then discuss its outcomes and propose the direction of the future research that can help to attain integration and adoption of the BCT for data-sharing, medical research, and the pharmaceutical supply chain in oncology, as well as in healthcare in general. Key Messages:BCT has the potential to enhance data-sharing (for primary care and medical research), as well as to attain optimization of the pharmaceutical supply chain by bringing properties such as transparency, traceability, and immutability to the applications. However, BCT itself cannot guarantee data privacy and security. Thus, it is never proposed as a stand-alone technology, but as a combined technology with cryptographic techniques. Regardless of the number of existing prototypes of blockchain-based healthcare systems, due to the existing barriers of the adoption (e.g., legal, social, and technological limitations), there is a lack of evaluation in real-world settings. Aiming to overcome these limitations, we propose future research directions that include design of the privacy-preserving hybrid data storage, interoperable infrastructures and architecture, and are compliant with the international laws and regulations.

Background Cervical kyphosis (CK) has been reported in patients with adolescent idiopathic scoliosis (AIS). The report about the incidence of CK between patients with AIS and normal populations was little. Patients included in previous studies often required scoliosis surgery (Cobb angle ≥40°), which does not represent all patients with AIS. The aims of this study were to compare incidence of CK between patients with AIS (Cobb angle >10°) and an age-matched normal population and to identify risk factors related to CK, especially coronal parameters that have rarely been studied in current literature. Methods Patients with AIS (n = 112) and asymptomatic subjects from the general population (control group; n = 40) were retrospectively analyzed. Radiographic parameters of coronal and sagittal plane were measured on full-length spine x-rays. Patients with AIS were divided into 2 groups based on cervical lordosis angle: cervical lordosis and CK. Results CK was observed in 14 of 40 (35%) subjects in the control group and 68 of 112 (60.7%) patients with AIS. Several parameters were related to CK, including major curve, proximal thoracic, and main thoracic Cobb angle; proximal thoracic kyphosis angle; main thoracic kyphosis angle; T1 slope; sagittal vertical axis; vertical distance between C7 plumb line and center sacral vertical line; apical vertebral translation; T1 coronal tilt; and lumbar pelvic relationship. Logistic regression identified main thoracic Cobb angle, main thoracic kyphosis angle, sagittal vertical axis, lumbar pelvic relationship, and apical vertebral translation as independent risk factors of CK. Conclusions Incidence of CK increases in patients with AIS relative to normal subjects. Coronal parameters of the spine could influence cervical sagittal alignment.

Coal spontaneous combustion is a great threat to mine safety, and gas is the key index to describe coal spontaneous combustion. Taking the coal samples of different kinds of coal as research object, the temperature programmed oxidation experiment was carried out, and the gases produced by coal samples at different temperatures were collected and analyzed by gas chromatography. This research studied the variation characteristics of gas species and gas concentrations in different coal samples during heating oxidation. The experimental results show that different coal samples produce different kinds of gases in the process of heating and oxidation. The order of gas production is CO, C2H6, C2H4, C3H8, and the relationship between gas production and temperature is approximately exponential. With the increase of coal metamorphic degree, the turning point temperature of sharp rise in coal sample gas production rate become higher, the oxidation ability of coal sample decreases, and the quantity of gas production decreases during the same time period. Keywords: Heating and oxidation, Metamorphic degree of coal, Oxidizing ability, Gas chromatography

Background Preterm birth and feeding are the most important pathogenic factors of neonatal necrotizing enterocolitis (NEC). Metabonomic has been widely used in the diagnosis and treatment of other diseases, but there is no research on the related diseases of premature infants. Compared with full-term infants, the metabolism of preterm infants has its own specificity, so it can easily lead to NEC and other digestive tract inflammatory diseases. Metabonomic may be applied to the diagnosis of preterm related diseases, such as NEC. Methods The model was established with premature infant serum samples from 19 premature infants in our hospital, which was compared with the full-term infant control group. Serum was analyzed by gas chromatography-mass spectrometry (GC-MS), coupled with the analysis of serum metabolic characteristics. The variable important in projection, P value and Pearson correlation coefficient of samples were analyzed by using SIMCA, SPSS and other multivariate statistical analysis software. Results Compared to the term infants, premature infants had significantly higher levels of luteolin, and lower levels of xylose, O-succinyl-L-homoserine and lauric acid in the serum. There were some correlations among several different metabolites and clinically related indices (albumin, total bilirubin) for premature birth related diseases. Conclusions There are metabolic alterations in the serum of premature infants, which make contribution to the diagnosis of NEC.

Neural Machine Translation (NMT) models achieve state-of-the-art performance on many translation benchmarks. As an active research field in NMT, knowledge distillation is widely applied to enhance the model's performance by transferring teacher model's knowledge on each training sample. However, previous work rarely discusses the different impacts and connections among these samples, which serve as the medium for transferring teacher knowledge. In this paper, we design a novel protocol that can effectively analyze the different impacts of samples by comparing various samples' partitions. Based on above protocol, we conduct extensive experiments and find that the teacher's knowledge is not the more, the better. Knowledge over specific samples may even hurt the whole performance of knowledge distillation. Finally, to address these issues, we propose two simple yet effective strategies, i.e., batch-level and global-level selections, to pick suitable samples for distillation. We evaluate our approaches on two large-scale machine translation tasks, WMT'14 English->German and WMT'19 Chinese->English. Experimental results show that our approaches yield up to +1.28 and +0.89 BLEU points improvements over the Transformer baseline, respectively., Accepted as a long paper at ACL 2021. Code is available at https://github.com/LeslieOverfitting/selective_distillation

In this paper, we propose a method that optimizes a regularization parameter for the regularized Free Form Deformation (FFD) non-rigid image registration. The developed process utilizes autoencoder generated image representations to assess image data generalization quality by the regularization parameter. Both pixel intensity and learned features are used to improve the overall accuracy and regularity of the resulting inverse problem solution. We implement the new selection criterion with its use in the non-rigid image FFD registration based on multi-level Bspline with L2-regularization, and validate the method with synthetic and real histopathology image datasets. Both qualitative and quantitative results suggest the efficacy of our developed method for fine-tuning histopathology microscope images.

Three-dimensional (3D) digital pathology has been emerging for next-generation tissue based cancer research. To enable such histopathology image volume analysis, serial histopathology slides need to be well aligned. In this paper, we propose a histopathology image registration fine tuning method with integrated landmark evaluations by texture and spatial proximity measures. Representative anatomical structures and image corner features are first detected as landmark candidates. Next, we identify strong and modify weak matched landmarks by leveraging image texture features and landmark spatial proximity measures. Both qualitative and quantitative results of extensive experiments demonstrate that our proposed method is robust and can further enhance registration accuracy of our previously registered image set by 31.15% (correlation), 4.88% (mutual information), and 41.02% (mean squared error), respectively. The promising experimental results suggest that our method can be used as a fine tuning module to further boost registration accuracy, a premise of histology spatial and morphology analysis in an information-lossless 3D tissue space for cancer research.

With the advent of IoT and emerging 5G technology, real-time streaming data are being generated at unprecedented speed and volume, and coming with both temporal and spatial dimensions. Effective analysis at such scale and speed requires support for dynamically adjusting querying capabilities in real-time. In spatio-temporal domain, this warrants for data as well as query optimization strategies especially for objects with changing motion states. Contemporary spatio-temporal data stream management systems in distributed domain are mostly dominated by specified-once-applied-continuously query model. Any modification in query state requires query restart limiting system responsiveness and producing outdated or in worst case erroneous results. In this paper, we propose adaptations of principles from streaming databases, spatial data management and distributed computing to support dynamic spatio-temporal query processing over high velocity big data streams. We first formulate a set of spatio-temporal data types and functions to seamlessly handle changes in distributed query states. We develop a comprehensive set of streaming spatio-temporal querying methods, and propose geohash based dynamic spatial partitioning for effective parallel processing. We implement a prototype on top of Apache Flink, where the in-memory stream processing fits nicely with our spatio-temporal models. Comparative evaluation of our prototype demonstrates the effectiveness our strategy by maintaining high consistent processing rates for both stationary as well as moving queries over high velocity spatio-temporal big data streams.

Opioid overdose related deaths have increased dramatically in recent years. Combating the opioid epidemic requires better understanding of the epidemiology of opioid poisoning (OP). To discover trends and patterns of opioid poisoning and the demographic and regional disparities, we analyzed large scale patient visits data in New York State (NYS). Demographic, spatial, temporal and correlation analyses were performed for all OP patients extracted from the claims data in the New York Statewide Planning and Research Cooperative System (SPARCS) from 2010 to 2016, along with Decennial US Census and American Community Survey zip code level data. 58,481 patients with at least one OP diagnosis and a valid NYS zip code address were included. Main outcome and measures include OP patient counts and rates per 100,000 population, patient level factors (gender, age, race and ethnicity, residential zip code), and zip code level social demographic factors. The results showed that the OP rate increased by 364.6%, and by 741.5% for the age group > 65 years. There were wide disparities among groups by race and ethnicity on rates and age distributions of OP. Heroin and non-heroin based OP rates demonstrated distinct temporal trends as well as major geospatial variation. The findings highlighted strong demographic disparity of OP patients, evolving patterns and substantial geospatial variation.

Artificial intelligence (AI) has been transforming the practice of drug discovery in the past decade. Various AI techniques have been used in a wide range of applications, such as virtual screening and drug design. In this survey, we first give an overview on drug discovery and discuss related applications, which can be reduced to two major tasks, i.e., molecular property prediction and molecule generation. We then discuss common data resources, molecule representations and benchmark platforms. Furthermore, to summarize the progress of AI in drug discovery, we present the relevant AI techniques including model architectures and learning paradigms in the papers surveyed. We expect that this survey will serve as a guide for researchers who are interested in working at the interface of artificial intelligence and drug discovery. We also provide a GitHub repository (https://github.com/dengjianyuan/Survey_AI_Drug_Discovery) with the collection of papers and codes, if applicable, as a learning resource, which is regularly updated., Comment: Accepted to Briefings in Bioinformatics

In triple negative breast cancer (TNBC) treatment, early prediction of pathological complete response (PCR) from chemotherapy before surgical operations is crucial for optimal treatment planning. We propose a novel deep learning-based system to predict PCR to neoadjuvant chemotherapy for TNBC patients with multi-stained histopathology images of serial tissue sections. By first performing tumor cell detection and recognition in a cell detection module, we produce a set of feature maps that capture cell type, shape, and location information. Next, a newly designed spatial attention module integrates such feature maps with original pathology images in multiple stains for enhanced PCR prediction in a dedicated prediction module. We compare it with baseline models that either use a single-stained slide or have no spatial attention module in place. Our proposed system yields 78.3% and 87.5% of accuracy for patch-, and patient-level PCR prediction, respectively, outperforming all other baseline models. Additionally, the heatmaps generated from the spatial attention module can help pathologists in targeting tissue regions important for disease assessment. Our system presents high efficiency and effectiveness and improves interpretability, making it highly promising for immediate clinical and translational impact.

For plug-in hybrid electric vehicle, an optimal energy management strategy can maximize its potential to achieve high efficiency. However, energy management strategy without condition information cannot achieve optimal fuel economy in real-time. In order to obtain higher efficiency and adapt to unexpected situation, we develop an energy management strategy based on velocity prediction using digital map information. The detailed model of the hybrid powertrain system such as engine, battery pack and vehicle model are established. The typical driving cycles are constructed to minimize the fuel consumption with equivalent consumption minimization strategy. To adapt to sudden congestions, a realtime strategy based on velocity prediction is proposed. Results indicates that equivalent consumption minimization strategy with velocity prediction is more efficient than the traditional equivalent consumption minimization strategy.

Background Diabetes affects more than 30 million patients across the United States. With such a large disease burden, even a small error in classification can be significant. Currently billing codes, assigned at the time of a medical encounter, are the “gold standard” reflecting the actual diseases present in an individual, and thus in aggregate reflect disease prevalence in the population. These codes are generated by highly trained coders and by health care providers but are not always accurate. Objective This work provides a scalable deep learning methodology to more accurately classify individuals with diabetes across multiple health care systems. Methods We leveraged a long short-term memory-dense neural network (LSTM-DNN) model to identify patients with or without diabetes using data from 5 acute care facilities with 187,187 patients and 275,407 encounters, incorporating data elements including laboratory test results, diagnostic/procedure codes, medications, demographic data, and admission information. Furthermore, a blinded physician panel reviewed discordant cases, providing an estimate of the total impact on the population. Results When predicting the documented diagnosis of diabetes, our model achieved an 84% F1 score, 96% area under the curve–receiver operating characteristic curve, and 91% average precision on a heterogeneous data set from 5 distinct health facilities. However, in 81% of cases where the model disagreed with the documented phenotype, a blinded physician panel agreed with the model. Taken together, this suggests that 4.3% of our studied population have either missing or improper diabetes diagnosis. Conclusions This study demonstrates that deep learning methods can improve clinical phenotyping even when patient data are noisy, sparse, and heterogeneous.

The correlation between the spontaneous combustion tendency of coal and its properties are of great importance for safety issues, environmental concerns, and economic problems. In this study, the relationship between multiple parameters, different from the previous single parameter, and the spontaneous combustion tendency was analyzed. The comprehensive judgment index (CJI), which indicates the tendency of coal spontaneous combustion, was obtained for samples collected from different mines. The CJI was measured by the cross-point temperature and had a negative correlation with the spontaneous combustion tendency. Physical pore structures and chemical functional groups were characterized based on cryogenic nitrogen adsorption and Fourier transform infrared spectroscopy measurements, respectively. For analyzing the effect of coal properties on the spontaneous combustion tendency, the grey relational grade was determined by the grey relational analysis between the CJI and the pore structures and functional groups of coal. The grey relational grade of the benzene substituent with CJI had a maximum of 0.8642, and the macropores had the minimum, 0.4169. The higher the gray relational grade was, the more relevant the spontaneous combustion tendency was, indicating that the benzene substituent was the most relevant. To better predict the spontaneous combustion tendency, the average pore diameter, hydroxyl, methyl, methylene, and benzene substituent with a high grey relational grade were selected. Finally, the multiple regression prediction model of CJI was established. The R squared coefficient, significance level, F-distribution, t-distribution, collinearity diagnosis, and residual distribution of the model met the requirements. In addition, two coal samples were selected to verify the spontaneous combustion tendency model. The relative errors between the predicted CJI value and the experimental CJI value were 1.42 and 4.25%, respectively. These small relative errors verified the reasonableness and validity of the prediction model.

Liver cancer is one of the leading causes of cancer deaths in Asia and Africa. It is caused by the Hepatocellular carcinoma (HCC) in almost 90% of all cases. HCC is a malignant tumor and the most common histological type of the primary liver cancers. The detection and evaluation of viable tumor regions in HCC present an important clinical significance since it is a key step to assess response of chemoradiotherapy and tumor cell proportion in genetic tests. Recent advances in computer vision, digital pathology and microscopy imaging enable automatic histopathology image analysis for cancer diagnosis. In this paper, we present a multi-resolution deep learning model HistoCAE for viable tumor segmentation in whole-slide liver histopathology images. We propose convolutional autoencoder (CAE) based framework with a customized reconstruction loss function for image reconstruction, followed by a classification module to classify each image patch as tumor versus non-tumor. The resulting patch-based prediction results are spatially combined to generate the final segmentation result for each WSI. Additionally, the spatially organized encoded feature map derived from small image patches is used to compress the gigapixel whole-slide images. Our proposed model presents superior performance to other benchmark models with extensive experiments, suggesting its efficacy for viable tumor area segmentation with liver whole-slide images.

IntroductionTo discover trends and patterns of opioid poisoning and the demographic and regional disparities by analyzing large scale patient visits data in New York State (NYS).MethodsDemographic, spatial, temporal and correlation analyses were performed for all OP patients extracted from the New York Statewide Planning and Research Cooperative System (SPARCS) from 2010 to 2016, along with Decennial US Census and American Community Survey zip code level data. The study is based on claims data. 58,481 patients with at least one OP diagnosis and a valid NYS zip code address were included. OP patient counts and rates per 100,000 population; patient level factors (gender, age, race and ethnicity, residential zip code); zip code level social demographic factors. Analyses were completed between 2017 and 2019.ResultsIn this study of 58,481 patients with opioid poisoning (OP) in New York State from 2010 to 2016, the OP rate increased by 364.6%, and by 741.5% for the age group > 65 years. There were wide disparities among groups by race and ethnicity on rates and age distributions of OP. Heroin and non-Heroin based OP rates show distinct temporal trends as well as major geospatial variation.ConclusionsThe findings highlight strong demographic disparity of OP patients, evolving patterns and substantial geospatial variation.

Background To explore the relationship between the pathological changes of the colon, terminal ileum, lung, liver and kidney, and the changes of Bax, PCNA and PAF in a rat model of NEC. Methods One hundred and forty neonatal SD rats were randomly divided into NEC group and control group (70 in each group). NEC group was given hypoxia, cold stimulation and artificial feeding twice a day for 3 consecutive days. The control group was only fed normally. After modeling, From the 1st day to the 7th day, 10 rats were sampled in each group for pathological examination of colon, terminal ileum, lung, liver and kidney tissue. The levels of Bax, PCNA and PAF were investigated by immunohistochemistry. Results Compared with the normal group, in the NEC group, on the 1st day, the colon, terminal ileum, lung, liver and kidney showed inflammatory damage. On the 5th day, the inflammatory injury was reduced. The inflammation disappeared on the 7th day. There were differences in the time of apoptosis in the intestine. In the intestine, the proliferation of PCNA was weak at first and then strong. Bax in liver and kidney showed marked apoptosis and apoptosis time increased in the lung. The expression of PCNA increased in lung, liver and kidney, and the expression of PAF increased in lung and liver. Conclusions NEC can lead to secondary injury of different degrees in colon, terminal ileum, lung, liver and kidney, and the degree and time of injury and repair were different. In general, organ repair played a leading role on the 4th day after modeling.

3D organ contouring is an essential step in radiation therapy treatment planning for organ dose estimation as well as for optimizing plans to reduce organs-at-risk doses. Manual contouring is time-consuming and its inter-clinician variability adversely affects the outcomes study. Such organs also vary dramatically on sizes - up to two orders of magnitude difference in volumes. In this paper, we present BrainSegNet, a novel 3D fully convolutional neural network (FCNN) based approach for automatic segmentation of brain organs. Brain-SegN et takes a multiple resolution paths approach and uses a weighted loss function to solve the major challenge of the large variability in organ sizes. We evaluated our approach with a dataset of 46 Brain CT image volumes with corresponding expert organ contours as reference. Compared with those of LiviaNet and V-Net, BrainSegNet has a superior performance in segmenting tiny or thin organs, such as chiasm, optic nerves, and cochlea, and outperforms these methods in segmenting large organs as well. BrainSegNet can reduce the manual contouring time of a volume from an hour to less than two minutes, and holds high potential to improve the efficiency of radiation therapy workflow.

BackgroundIncreasing attention has been focused on (modic changes and end plate defect) MC&ED as a potential role in the etiopathogenesis of lumbar degeneration. The precise mechanism leading to progression of MC&ED is lacking. Some scholars thought lumbar sagittal alignment might be a key influence factor, but without enough clinical evidence. Hence, we investigated how lumbar sagittal alignment influences MC&ED.MethodsNinety-six asymptomatic or symptomatic patients with Modic changes or end plate defect were retrospectively recruited in this study from August 2016 to December 2018. MC&ED were observed in 76 patients and not observed in 20 patients, representing two groups for comparison. The lumbar sagittal parameters were measured, including lumbar lordosis (LL), pelvic incidence (PI), sacral slope (SS), and pelvic tilt (PT). The lumbar lordosis index(LLI) and idea LL were then calculated. Clinical outcomes were assessed using a visual analog scale(VAS) and an Oswestry Disability Index (ODI) before and after an operation.ResultsThere were no significant difference in the distribution of demographic and baseline clinical variables between both groups. Mean age and BMI showed significant difference between both groups (P P P P

Digital imaging plays a critical role for image guided diagnosis and clinical trials, and the amount of image data is fast growing. There are two major requirements for image data management: scalability for massive scales and support of comprehensive queries. Traditional Picture Archiving and Communication Systems (PACS for short) are based on relational data management systems and suffer from limited scalability and query support. Therefore, new systems that support fast, scalable and comprehensive queries on image data are highly demanded. In this paper, we introduce two alternative approaches: DCMRL/XMLStore (RL/XML for short)-a parallel, hybrid relational and XML data management approach, and DCMDocStore (DOC for short)-a NoSQL document store approach. DCMRL/XMLStore manages DICOM images as binary large objects and metadata as relational tables and XML documents based on IBM DB2, which is parallelized through data partitioning. DCMDocStore manages DICOM metadata as JSON objects, and DICOM images as encoded attachments in MongoDB running on multiple nodes. We have delivered two open source systems DCMRL/XMLStore and DCMDocStore. Both systems support scalable data management and comprehensive queries. We also evaluated them with nearly one million DICOM images from National Biomedical Imaging Archive. The results show that, DCMDocStore demonstrates high data loading speed, high scalability and fault tolerance. DCMRL/XMLStore provides efficient queries, but comes with slower data loading. Traditional PACS systems have inherent limitations on flexible queries and scalability for massive amount of images.

SummaryBackgroundThe novel coronavirus (CoV) severe acute respiratory syndrome (SARS)-CoV-2 outbreak started at the end of 2019 in Wuhan, China, and spread over 100 countries. SARS-CoV-2 uses the membrane protein Angiotensin I converting enzyme 2(ACE2) as a cell entry receptor. Indeed, it was reported that the balance of Renin-Angiotensin System (RAS), regulated by both ACE and ACE2, was altered in COVID-19 patients. It is controversial, however, whether commonly used anti-hypertensive drugs Angiotensin I converting enzyme inhibitor (ACEI) and Angiotensin II receptor blocker (ARB) shall be continued in the confirmed COVID-19 patients. This study was designed to investigate any difference in disease severity between COVID-19 patients with hypertension comorbidity. The included COVID-19 patients used ACEI, ARB, calcium channel blockers (CCB), beta blockers (BB), or thiazide to treat preexisting hypertension prior to the hospital were compared to patients who did not take any of those drugs.MethodsIn this multicentre retrospective study, clinical data of 511 COVID-19 patients were analyzed. Patients were categorized into six sub-groups of hypertension comorbidity based on treatment using one of anti-hypertension drugs (ACEI, ARB, CCB, BB, thiazide), or none. A meta-analysis was performed to evaluate the use of ACEI and ARB associated with pneumonia using published studies.FindingsAmong the elderly (age>65) COVID-19 patients with hypertension comorbidity, the risk of COVID-19-S (severe disease) was significantly decreased in patients who took ARB drugs prior to hospitalization compared to patients who took no drugs (OR=0·343, 95% CI 0·128-0·916, p=0·025). The meta-analysis showed that ARB use has positive effects associated with morbidity and mortality of pneumonia.InterpretationElderly (age>65) COVID-19 patients with hypertension comorbidity who are taking ARB anti-hypertension drugs may be less likely to develop severe lung disease compared to patients who take no anti-hypertension drugs.FundingNational Natural Science Foundation of China, Chinese Academy of Medical SciencesResearch in contextEvidence before this studyWe searched PubMed for articles published up to March 15, 2020 using keywords “2019-nCoV”, “SARS-CoV-2”, “novel coronavirus”, and COVID-19 AND “ARB”, and “angiotensin II receptor blocker” for papers published in both English and Chinese. We found three papers: one from our group, published in Science China Life Science that demonstrated an elevated Angiotensin II level in blood samples from COVID-19 patients; another a perspective article in Chinese recommending ACEI and ARBs as potential remedies for SARS-CoV-2 infections; the third a retrospective study in Chinese identifying no significant difference between ACEI/ARB associated with outcomes in 112 COVID-19 patients with CVD comorbidity. The International society of Hypertension stated on March 16th, 2020: “there are no clinical data in human to show that ACE-inhibitors or ARBs either improve or worsen susceptibility to COVID-19 infection nor do they affect the outcomes of those infected”.Added value of this studyWe retrospectively reviewed different types of anti-hypertensive drugs taken by COVID-19 patients with hypertension comorbidity prior to entering the hospital. We discovered that ARB hypertensive drugs were associated with a decreased risk of severe disease in elderly (age>65) COVID-19 patients (OR=0·343, 95% CI 0·128-0·916, p=0·025), the first evidence of ARBs association to COVID-19 infections in human. We conducted a meta-analysis in the literature and found that ARB has positive effects associated with morbidity and mortality of pneumonia.Implications of all the available evidenceARB drugs are widely used in the population with hypertension. Treatments with ACEI and ARBs should be continuous according to medical guidelines. RCT trials of ARB associated with morbidity and mortality of SARS-CoV-2 infection are recommended in the future.

Hepatic steatosis droplet quantification with histology biopsies has high clinical significance for risk stratification and management of patients with fatty liver diseases and in the decision to use donor livers for transplantation. However, pathology reviewing processes, when conducted manually, are subject to a high inter- and intra-reader variability, due to the overwhelmingly large number and significantly varying appearance of steatosis instances. This process is challenging as there is a large number of overlapped steatosis droplets with either missing or weak boundaries. In this study, we propose a deep-learning-based region-boundary integrated network for precise steatosis quantification with whole slide liver histopathology images. The proposed model consists of two sequential steps: a region extraction and a boundary prediction module for foreground regions and steatosis boundary prediction, followed by an integrated prediction map generation. Missing steatosis boundaries are next recovered from the predicted map and assembled from adjacent image patches to generate results for the whole slide histopathology image. The resulting steatosis measures both at the pixel level and steatosis object-level present strong correlation with pathologist annotations, radiology readouts and clinical data. In addition, the segregated steatosis object count is shown as a promising alternative measure to the traditional metrics at the pixel level. These results suggest a high potential of artificial intelligence-assisted technology to enhance liver disease decision support using whole slide images.

Neoadjuvant chemotherapy is widely used to reduce tumor size to make surgical excision manageable and to minimize distant metastasis. Assessing and accurately predicting pathological complete response is important in treatment planing for breast cancer patients. In this study, we propose a novel approach integrating 3D MRI imaging data, molecular data and demographic data using convolutional neural network to predict the likelihood of pathological complete response to neoadjuvant chemotherapy in breast cancer. We take post-contrast T1-weighted 3D MRI images without the need of tumor segmentation, and incorporate molecular subtypes and demographic data. In our predictive model, MRI data and non-imaging data are convolved to inform each other through interactions, instead of a concatenation of multiple data type channels. This is achieved by channel-wise multiplication of the intermediate results of imaging and non-imaging data. We use a subset of curated data from the I-SPY-1 TRIAL of 112 patients with stage 2 or 3 breast cancer with breast tumors underwent standard neoadjuvant chemotherapy. Our method yielded an accuracy of 0.83, AUC of 0.80, sensitivity of 0.68 and specificity of 0.88. Our model significantly outperforms models using imaging data only or traditional concatenation models. Our approach has the potential to aid physicians to identify patients who are likely to respond to neoadjuvant chemotherapy at diagnosis or early treatment, thus facilitate treatment planning, treatment execution, or mid-treatment adjustment.

Background: The US is experiencing an opioid epidemic, such that opioid overdose is causing more than 100 deaths per day. Identifying patients at high risk of Opioid Overdose (OD) can help to make early clinical interventions to reduce the risk of OD. We aim to build a deep learning model that can predict the patients at high risk for opioid overdose and identify most relevant features. Method: The study included the information of 5,231,614 patients from the Health Facts database with at least one opioid prescription between January 1, 2008 and December 31, 2017. Potential predictors (n = 1185) were extracted to build a feature matrix for prediction. A Long Short-Term Memory (LSTM) based model was built to predict overdose risk in the next hospital visit. Prediction performance was compared with other machine learning methods assessed using machine learning metrics. Findings: Our temporal deep learning model built upon LSTM outperformed the other methods on opioid overdose prediction. It achieved the highest F-1 score (F-1 score: 0.8065, AUCROC: 0.8885). The model is also able to reveal top ranked predictive features, including medications and vital signs. Interpretation: This study demonstrates that a temporal deep learning based predictive model can achieve promising results on identifying risk of opioid overdose of patients using the history of electronic health records. It provides an alternative informatics-based approach to improving clinical decision support for possible early detection and intervention to reduce opioid overdose. Funding: This work was funded partially by the Stony Brook University OVPR Seed Grant 1158484-63845-6. Declaration of Interests: The authors do not have any conflicts of interest to disclose. Ethics Approval Statement: All the data used in the study are de-identified, and the study is approved by Stony Brook University.

Generative adversarial networks (GANs) have been highly successful for generating realistic synthetic data. In healthcare, synthetic data generation can be helpful for producing annotated data and improving data-driven research without worries on data privacy. However, electronic health records (EHRs) are noisy, incomplete and complex, and existing work on EHR data is mainly devoted to generating discrete elements such as diagnosis codes and medications or frequent laboratory values. In this work, we propose SMOOTH-GAN, a novel approach for generating reliable EHR data such as laboratory values and medications given diagnosis codes. SMOOTH-GAN takes advantage of a conditional GAN architecture with WGAN-GP loss, and is able to learn transitions between disease stages with high flexibility over data customization. Our experiments demonstrate the model’s effectiveness in terms of both statistical similarity and accuracy on machine learning based prediction. To further demonstrate the usage of our model, we apply counterfactual reasoning and generate data with occurrence of multiple diseases, which can provide unique datasets for artificial intelligence driven healthcare research.

Mechanical properties of aircraft composite laminate after lightning strike are predicted and verified based on the combination of experiment and numerical simulation. Axial compression experiment of composite laminate containing lightning strike damage is carried out and failure modes of experimental samples under three kinds of electrical current waveforms are evaluated. The main failure modes include fiber fracture, matrix cracking and delamination. The final failure load decreases with the increase of actual integral action of lightning strike. Compression failure process of composite laminate is simulated using progressive damage analysis methods such as Hashin criterion, Maximum stress criterion and TSERPES criterion, respectively. Numerical simulation and experiment results are compared and coincided well. Percentage error of Hashin criterion is the minimum compared with those of TSERPES criterion and Maximum stress criterion. Stress concentration mainly appears in both angle sides on the fixed end and lightning damage regions of composite laminate.

Intelligent photovoltaic (PV) module monitoring can automatically locate the position of panels and monitor the status of PV modules, which is of great significance for PV plants operation and maintenance. As a new communication method in the field of PV monitoring, the monitoring of PV modules based on power line communication is the future trend of PV power plants monitoring due to its low maintenance cost and strong anti-interference ability. This paper presents an intelligent PV module monitoring scheme based on the parallel resonant coupling unit, which uses the dc bus as the communication channel and modula-tes the monitoring data into a high-frequency form to carry out the carrier communication. By using the parallel resonant coupling, the high-frequency carrier wave is injected into the dc bus, which improves the system efficiency and anti-interference ability. In addition, an adaptive level decision circuit is proposed to improve the flexibility and scalability of the communication system by adaptively changing the reference level of the decision circuit. Experimental results achieved by a developed power line transceiver demonstrate both feasibility and validity of the proposed scheme.

This study aims to evaluate the applicability of the degraded stiffness matrix and the redefined characteristic length models in the finite element analysis of composite laminates under low-velocity impact. Implemented by the user-defined VUMAT subroutine in ABAQUS, various damage models are used to predict the damage behavior in the composite laminates, meanwhile, the user-defined VUCHARLENGTH subroutine is applied to define the characteristic length to match the damage mode. Cohesive elements are inserted between the adjacent piles to model the delamination. The applicability of different damage models is investigated by comparing the global mechanical response and local damage behavior. A new characteristic length calculation method is proposed to apply in the low-velocity impact simulation for laminates. The numerical results show that the degraded stiffness matrix proposed by both Maimi and Matzenmiller can accurately predict the global mechanical responses in the complicated damage accumulation case, while the model of PEE (the principle of energy equivalence) and Linde aren’t suitable for the low-velocity impact simulation. As for the characteristic length model, the redefined characteristic length model can obtain more accurate global mechanical responses with the impact energy increasing compared with the predictions of default characteristic length model.

The development and application of high-throughput genomics technologies has resulted in massive quantities of diverse omics data that continue to accumulate rapidly. These rich datasets offer unprecedented and exciting opportunities to address long standing questions in biomedical research. However, our ability to explore and query the content of diverse omics data is very limited. Existing dataset search tools rely almost exclusively on the metadata. A text-based query for gene name(s) does not work well on datasets wherein the vast majority of their content is numeric. To overcome this barrier, we have developed Omicseq, a novel web-based platform that facilitates the easy interrogation of omics datasets holistically to improve ‘findability’ of relevant data. The core component of Omicseq is trackRank, a novel algorithm for ranking omics datasets that fully uses the numerical content of the dataset to determine relevance to the query entity. The Omicseq system is supported by a scalable and elastic, NoSQL database that hosts a large collection of processed omics datasets. In the front end, a simple, web-based interface allows users to enter queries and instantly receive search results as a list of ranked datasets deemed to be the most relevant. Omicseq is freely available at http://www.omicseq.org.

The modulation strategy LMZVM (large, middle, and zero vector modulation) can be widely adopted in the three-level 7 photovoltaic (PV) inverter, because it reduces the ground current and produces little neutral-point (NP) voltage in normal operation. However, in low-voltage ride-through (LVRT) operation, the NP voltage fluctuation becomes large and has not been researched nowadays. In this paper, according to this problem, four critical weighing factors about the NP voltage based on symmetrical component method are proposed. We can see that the maximum peak-to-peak value among all dip types happens when the single-phase grid voltage dips to 0. Then, an original balancing strategy, which replaces the zero vector with two opposite P-type or N-type small vectors of equal duty cycle, is put forward. Experimental results are provided to validate that the proposed strategy can reduce the NP voltage fluctuation in LVRT operation.

Background Axillary lymph node status is important for breast cancer staging and treatment planning as the majority of breast cancer metastasis spreads through the axillary lymph nodes. There is currently no reliable noninvasive imaging method to detect nodal metastasis associated with breast cancer. Materials and Methods Magnetic resonance imaging (MRI) data were those from the peak contrast dynamic image from 1.5 Tesla MRI scanners at the pre-neoadjuvant chemotherapy stage. Data consisted of 66 abnormal nodes from 38 patients and 193 normal nodes from 61 patients. Abnormal nodes were those determined by expert radiologist based on 18Fluorodeoxyglucose positron emission tomography images. Normal nodes were those with negative diagnosis of breast cancer. The convolutional neural network consisted of 5 convolutional layers with filters from 16 to 128. Receiver operating characteristic analysis was performed to evaluate prediction performance. For comparison, an expert radiologist also scored the same nodes as normal or abnormal. Results The convolutional neural network model yielded a specificity of 79.3% ± 5.1%, sensitivity of 92.1% ± 2.9%, positive predictive value of 76.9% ± 4.0%, negative predictive value of 93.3% ± 1.9%, accuracy of 84.8% ± 2.4%, and receiver operating characteristic area under the curve of 0.91 ± 0.02 for the validation data set. These results compared favorably with scoring by radiologists (accuracy of 78%). Conclusion The results are encouraging and suggest that this approach may prove useful for classifying lymph node status on MRI in clinical settings in patients with breast cancer, although additional studies are needed before routine clinical use can be realized. This approach has the potential to ultimately be a noninvasive alternative to lymph node biopsy.

In this paper, a kind of L-shaped absorbing metamaterial for X-band, Ku-band and K-band is proposed. By fusing the natural resonance and the coupling resonance between the units, the absorbing metamaterial characteristic of broadband response is realized. The measured results show that the -10dB bandwidth of the absorbing metamaterial is 11.4GHz-18.9GHz, the absorbing rate is higher than 90%, and covering X-band (11.4-12GHz), Ku-band (12-18GHz) and K-band (18-18.9GHz), and the visible light transmittance is 90%. Therefore, the L-shaped absorbing metamaterial can achieve high light transmission and broadband absorbing characteristics.

Background In recent years, both suicide and overdose rates have been increasing. Many individuals who struggle with opioid use disorder are prone to suicidal ideation; this may often result in overdose. However, these fatal overdoses are difficult to classify as intentional or unintentional. Intentional overdose is difficult to detect, partially due to the lack of predictors and social stigmas that push individuals away from seeking help. These individuals may instead use web-based means to articulate their concerns. Objective This study aimed to extract posts of suicidality among opioid users on Reddit using machine learning methods. The performance of the models is derivative of the data purity, and the results will help us to better understand the rationale of these users, providing new insights into individuals who are part of the opioid epidemic. Methods Reddit posts between June 2017 and June 2018 were collected from r/suicidewatch, r/depression, a set of opioid-related subreddits, and a control subreddit set. We first classified suicidal versus nonsuicidal languages and then classified users with opioid usage versus those without opioid usage. Several traditional baselines and neural network (NN) text classifiers were trained using subreddit names as the labels and combinations of semantic inputs. We then attempted to extract out-of-sample data belonging to the intersection of suicide ideation and opioid abuse. Amazon Mechanical Turk was used to provide labels for the out-of-sample data. Results Classification results were at least 90% across all models for at least one combination of input; the best classifier was convolutional neural network, which obtained an F1 score of 96.6%. When predicting out-of-sample data for posts containing both suicidal ideation and signs of opioid addiction, NN classifiers produced more false positives and traditional methods produced more false negatives, which is less desirable for predicting suicidal sentiments. Conclusions Opioid abuse is linked to the risk of unintentional overdose and suicide risk. Social media platforms such as Reddit contain metadata that can aid machine learning and provide information at a personal level that cannot be obtained elsewhere. We demonstrate that it is possible to use NNs as a tool to predict an out-of-sample target with a model built from data sets labeled by characteristics we wish to distinguish in the out-of-sample target.