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We aimed to develop a data-driven machine learning model for predicting critical deterioration events from routinely collected EHR data in hospitalized children.This retrospective cohort study included all pediatric inpatients hospitalized on a medical or surgical ward between 2014-2018 at a quaternary children's hospital.We developed a large data-driven approach and evaluated three machine learning models to predict pediatric critical deterioration events. We evaluated the models using a nested, stratified 10-fold cross-validation. The evaluation metrics included C-statistic, sensitivity, and positive predictive value. We also compared the machine learning models with patients identified as high-risk Watchers by bedside clinicians.The study included 57,233 inpatient admissions from 34,976 unique patients. 3,943 variables were identified from the EHR data. The XGBoost model performed best (C-statistic=0.951, CI: 0.946 ∼ 0.956).Our data-driven machine learning models accurately predicted patient deterioration. Future sociotechnical analysis will inform deployment within the clinical setting.

Objective: We aimed to develop a data-driven machine learning model for predicting critical deterioration events from routinely collected EHR data in hospitalized children. Materials: This retrospective cohort study included all pediatric inpatients hospitalized on a medical or surgical ward between 2014–2018 at a quaternary children’s hospital. Methods: We developed a large data-driven approach and evaluated three machine learning models to predict pediatric critical deterioration events. We evaluated the models using a nested, stratified 10-fold cross-validation. The evaluation metrics included C-statistic, sensitivity, and positive predictive value. We also compared the machine learning models with patients identified as high-risk Watchers by bedside clinicians. Results: The study included 57,233 inpatient admissions from 34,976 unique patients. 3,943 variables were identified from the EHR data. The XGBoost model performed best (C-statistic=0.951, CI: 0.946 ∼ 0.956). Conclusions: Our data-driven machine learning models accurately predicted patient deterioration. Future sociotechnical analysis will inform deployment within the clinical setting.

Nursing is a high-risk occupation with high exposure to stress. The physical and mental health of nurses is directly related to the quality of medical services. Therefore, the sleep quality of nurses should not be ignored. In this study, the method of cluster random sampling was adopted from May to September 2019, and a questionnaire survey was conducted among 521 surgical nurses from five affiliated hospitals of Xinjiang Medical University. The relationship between mental health and sleep quality was analyzed, and 20% of the participants with sleep disorders were randomly selected. The sleep disorders used 1 : 1 matching, finally providing a sample with 60 cases and 60 controls for measurement of the CLOCK gene (rs1801260, rs6850524), to analyze the effect of the interaction between mental health and the CLOCK gene on sleep. The mental health and sleep quality of the surgical nurses were evaluated using the Symptom Checklist 90 (SCL-90) and Pittsburgh Sleep Quality Index (PSQI). The study found that surgical nurses had poor sleep, and there were differences associated with age, years working, frequency of night shifts, and incidence of sleep disorders under marital status (p<0.05). The PSQI scores of the positive psychological symptoms were higher than those of the negative psychological symptoms. The rank sum test was used to compare the sleep quality scores of different genotypes in CLOCK rs1801260 and rs6850524; the results indicated that the PSQI scores were different among different genotypes at the rs1801260 and rs6850524 loci. The logistic regression results suggested that CLOCK gene rs1801260 (TC) and positive psychological symptoms were influential factors for sleep disorders, and the interaction of positive psychological symptoms∗rs1801260 (TT) was a risk factor for sleep disorders (OR=10.833, 95% CI: 2.987–39.288). The sleep quality of nurses is not only affected by demographic characteristics but also affected by mental health status and the CLOCK gene.

Human cervical cancer is the fourth most common carcinoma in women in the world. The JAK/STAT3 signaling pathways crucially regulate cell growth and apoptosis. It is a significant target signaling pathway for the development of novel antitumor medicine. This study intended to explore whether lycorine could prevent HT-3 proliferation and induce apoptosis by targeting the JAK/STAT3 signaling cascade. The HT-3 cells were treated with various lycorine dosages and we analyzed cell growth, lipid peroxidation, antioxidants, mitochondrial membrane potential (ΔΨm), DNA damage, apoptosis markers by different in vitro methodologies. Our results revealed that lycorine substantially reserved cell growth via decreased antioxidants, augmented reactive oxygen species (ROS) generation which leads to loss of ΔΨm, increased nuclear crumbling and chromatin condensation, thus resulting in representative increased apoptotic cell death. Furthermore, we analyzed that the molecular mechanical action of lycorine considerably repressed JAK1/STAT3 transactional activation and decrease its downstream molecules Bcl-2, and enhances the expressional activity of Bax, cytochrome c, caspase 3 and 9 in HT-3 cells. Finally, the fact that N-acetylcysteine inhibits lycorine-induced ROS-mediated apoptosis was confirmed in HT-3 cells. Thus, the results indicate that lycorine efficiently enhances apoptosis and inhibits HT-3 cell proliferation. These outcomes collectively proposed that lycorine could be a beneficial chemotherapeutic agent for treating and managing human cervical carcinoma.

To develop and evaluate a high-dimensional, data-driven model to identify patients at high risk of clinical deterioration from routinely collected electronic health record (EHR) data.In this single-center, retrospective cohort study, 488 patients with single-ventricle and shunt-dependent congenital heart disease6 months old were admitted to the cardiac intensive care unit before stage 2 palliation between 2014 and 2019. Using machine-learning techniques, we developed the Intensive care Warning Index (I-WIN), which systematically assessed 1028 regularly collected EHR variables (vital signs, medications, laboratory tests, and diagnoses) to identify patients in the cardiac intensive care unit at elevated risk of clinical deterioration. An ensemble of 5 extreme gradient boosting models was developed and validated on 203 cases (130 emergent endotracheal intubations, 34 cardiac arrests requiring cardiopulmonary resuscitation, 10 extracorporeal membrane oxygenation cannulations, and 29 cardiac arrests requiring cardiopulmonary resuscitation onto extracorporeal membrane oxygenation) and 378 control periods from 446 patients.At 4 hours before deterioration, the model achieved an area under the receiver operating characteristic curve of 0.92 (95% confidence interval, 0.84-0.98), 0.881 sensitivity, 0.776 positive predictive value, 0.862 specificity, and 0.571 Brier skill score. Performance remained high at 8 hours before deterioration with 0.815 (0.688-0.921) area under the receiver operating characteristic curve.I-WIN accurately predicted deterioration events in critically-ill infants with high-risk congenital heart disease up to 8 hours before deterioration, potentially allowing clinicians to target interventions. We propose a paradigm shift from conventional expert consensus-based selection of risk factors to a data-driven, machine-learning methodology for risk prediction. With the increased availability of data capture in EHRs, I-WIN can be extended to broader applications in data-rich environments in critical care.

A variety of pipelines are distributed under urban roads. The upgrading of pipelines is bound to occupy certain road resources, compress the driving space of motor vehicles for a long time, aggravate the traffic congestion in the construction section, and then affect the traffic operation of the whole region. A reasonable layout of traffic signs for inducement to guide the traffic flow in the area where the construction section is located is conducive to promoting a balanced distribution of traffic flow in the regional road network, so as to achieve the reduction of automobile exhaust emissions and the sustainable development of traffic. In this paper, the layout optimization method of regional traffic signs for inducement is proposed. Taking the maximum amount of guidance information that the regional traffic signs can provide as the objective function, and taking the traffic volume, the characteristics of intersection nodes and the standard deviation of road saturation as the independent variables, the layout optimization model of guidance facilities is constructed, which can optimize the layout of traffic guidance signs in the area affected by the construction section, and achieve the goal that the minimum number of facilities can provide the maximum amount of guidance information. The results of the case study show that among the 64 alternative locations where traffic guidance signs can be set in the study area, eight optimal locations are finally determined as the setting points of guidance facilities through this model, and the effective increment of guidance information is the largest at this time. The model proposed in this paper can be used for reference to promote the sustainable development of traffic in the area where the construction section is located.

BACKGROUND The aim of the present work was to evaluate FOXA2 expression in ovarian cancer and to use integrated bioinformatics analysis to correlate it with patient prognosis. MATERIAL AND METHODS FOXA2 expression was evaluated in multiple cancers in The Cancer Genome Atlas database. A protein-protein interaction (PPI) network relevant to FOXA2 was constructed using the Search Tool for Retrieval of Interacting Genes/Proteins (STRIN). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed of FOXA2 and relevant genes. Correlations between overall survival (OS), disease-free survival, and FOXA2 expression were evaluated. An immunohistochemical assay (IHC) was used to test for FOXA2 protein expression in 79 ovarian cancer specimens. RESULTS FOXA2 mRNA was upregulated in colorectal, stomach, liver, and endometrial cancers. In the PPI network, 21 protein nodes and 533 edges were constructed with a local clustering coefficient of 0.698, which indicated significant PPI enrichment (P

Objective Limited research exists in predicting first-time suicide attempts that account for two-thirds of suicide decedents. We aimed to predict first-time suicide attempts using a large data-driven approach that applies natural language processing (NLP) and machine learning (ML) to unstructured (narrative) clinical notes and structured electronic health record (EHR) data. Methods This case-control study included patients aged 10–75 years who were seen between 2007 and 2016 from emergency departments and inpatient units. Cases were first-time suicide attempts from coded diagnosis; controls were randomly selected without suicide attempts regardless of demographics, following a ratio of nine controls per case. Four data-driven ML models were evaluated using 2-year historical EHR data prior to suicide attempt or control index visits, with prediction windows from 7 to 730 days. Patients without any historical notes were excluded. Model evaluation on accuracy and robustness was performed on a blind dataset (30% cohort). Results The study cohort included 45 238 patients (5099 cases, 40 139 controls) comprising 54 651 variables from 5.7 million structured records and 798 665 notes. Using both unstructured and structured data resulted in significantly greater accuracy compared to structured data alone (area-under-the-curve [AUC]: 0.932 vs. 0.901 P Conclusions Our large data-driven approach using both structured and unstructured EHR data demonstrated accurate and robust first-time suicide attempt prediction, and has the potential to be deployed across various populations and clinical settings.

BACKGROUND This study investigated the effect of occupational stress and circadian clock gene polymorphism on sleep disorder of oil workers in Xinjiang, China. MATERIAL AND METHODS We enrolled 2300 Xinjiang oil workers who had been working for at least 1 year. The Chinese revised version of the Occupational Stress Questionnaire (OSI-R), the Pittsburgh Sleep Quality Index (PSQI), and General Survey Questionnaire were used. A total of 308 subjects were selected for stress hormone measurements and gene polymorphism analysis of the circadian clock genes CLOCK, PER2, and PER3. RESULTS The occupational stress scores were influenced by sex, smoking, marital status, age, and work type. Different work shift groups and different professional title groups had statistically significant sleep disorder incidences (P

Nursing is a high-risk occupation with high exposure to stress. The physical and mental health of nurses is directly related to the quality of medical services. Therefore, the sleep quality of nurses should not be ignored. In this study, the method of cluster random sampling was adopted from May to September 2019, and a questionnaire survey was conducted among 521 surgical nurses from five affiliated hospitals of Xinjiang Medical University. The relationship between mental health and sleep quality was analyzed, and 20% of the participants with sleep disorders were randomly selected. The sleep disorders used 1 : 1 matching, finally providing a sample with 60 cases and 60 controls for measurement of the CLOCK gene (rs1801260, rs6850524), to analyze the effect of the interaction between mental health and the CLOCK gene on sleep. The mental health and sleep quality of the surgical nurses were evaluated using the Symptom Checklist 90 (SCL-90) and Pittsburgh Sleep Quality Index (PSQI). The study found that surgical nurses had poor sleep, and there were differences associated with age, years working, frequency of night shifts, and incidence of sleep disorders under marital status (p < 0.05). The PSQI scores of the positive psychological symptoms were higher than those of the negative psychological symptoms. The rank sum test was used to compare the sleep quality scores of different genotypes in CLOCK rs1801260 and rs6850524; the results indicated that the PSQI scores were different among different genotypes at the rs1801260 and rs6850524 loci. The logistic regression results suggested that CLOCK gene rs1801260 (TC) and positive psychological symptoms were influential factors for sleep disorders, and the interaction of positive psychological symptoms∗rs1801260 (TT) was a risk factor for sleep disorders (OR = 10.833, 95% CI: 2.987–39.288). The sleep quality of nurses is not only affected by demographic characteristics but also affected by mental health status and the CLOCK gene.

Since the emergence of memristors (or memristive devices), how to integrate them into arrays has been widely investigated. After years of research, memristor crossbar arrays have been proposed and realized with potential applications in nonvolatile memory, logic and neuromorphic computing systems. Despite the promising prospects of memristor crossbar arrays, one of the main obstacles for their development is the so-called sneak-path current causing cross-talk interference between adjacent memory cells and thus may result in misinterpretation which greatly influences the operation of memristor crossbar arrays. Solving the sneak-path current issue, the power consumption of the array will immensely decrease, and the reliability and stability will simultaneously increase. In order to suppress the sneak-path current, various solutions have been provided. So far, some reviews have considered some of these solutions and established a sophisticated classification, including 1D1M, 1T1M, 1S1M (D: diode, M: memristor, T: transistor, S: selector), self-selective and self-rectifying memristors. Recently, a mass of studies have been additionally reported. This review thus attempts to provide a survey on these new findings, by highlighting the latest research progress realized for relieving the sneak-path issue. Here, we first present the concept of the sneak-path current issue and solutions proposed to solve it. Consequently, we select some typical and promising devices, and present their structures and properties in detail. Then, the latest research activities focusing on single-device structures are introduced taking into account the mechanisms underlying these devices. Finally, we summarize the properties and perspectives of these solutions.