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Abstract BackgroundSystematic literature review (SLR), a robust method to identify and summarize evidence from published sources, is considered to be a complex, time-consuming, labor-intensive, and expensive task. ObjectiveThis study aimed to present a solution based on natural language processing (NLP) that accelerates and streamlines the SLR process for observational studies using real-world data. MethodsWe followed an agile software development and iterative software engineering methodology to build a customized intelligent end-to-end living NLP-assisted solution for observational SLR tasks. Multiple machine learning–based NLP algorithms were adopted to automate article screening and data element extraction processes. The NLP prediction results can be further reviewed and verified by domain experts, following the human-in-the-loop design. The system integrates explainable articificial intelligence to provide evidence for NLP algorithms and add transparency to extracted literature data elements. The system was developed based on 3 existing SLR projects of observational studies, including the epidemiology studies of human papillomavirus–associated diseases, the disease burden of pneumococcal diseases, and cost-effectiveness studies on pneumococcal vaccines. ResultsOur Intelligent SLR Platform covers major SLR steps, including study protocol setting, literature retrieval, abstract screening, full-text screening, data element extraction from full-text articles, results summary, and data visualization. The NLP algorithms achieved accuracy scores of 0.86-0.90 on article screening tasks (framed as text classification tasks) and macroaverage F1 scores of 0.57-0.89 on data element extraction tasks (framed as named entity recognition tasks). ConclusionsCutting-edge NLP algorithms expedite SLR for observational studies, thus allowing scientists to have more time to focus on the quality of data and the synthesis of evidence in observational studies. Aligning the living SLR concept, the system has the potential to update literature data and enable scientists to easily stay current with the literature related to observational studies prospectively and continuously.

Abstract Objective Systematic literature reviews (SLRs) are critical for life-science research. However, the manual selection and retrieval of relevant publications can be a time-consuming process. This study aims to (1) develop two disease-specific annotated corpora, one for human papillomavirus (HPV) associated diseases and the other for pneumococcal-associated pediatric diseases (PAPD), and (2) optimize machine- and deep-learning models to facilitate automation of the SLR abstract screening. Methods This study constructed two disease-specific SLR screening corpora for HPV and PAPD, which contained citation metadata and corresponding abstracts. Performance was evaluated using precision, recall, accuracy, and F1-score of multiple combinations of machine- and deep-learning algorithms and features such as keywords and MeSH terms. Results and conclusions The HPV corpus contained 1697 entries, with 538 relevant and 1159 irrelevant articles. The PAPD corpus included 2865 entries, with 711 relevant and 2154 irrelevant articles. Adding additional features beyond title and abstract improved the performance (measured in Accuracy) of machine learning models by 3% for HPV corpus and 2% for PAPD corpus. Transformer-based deep learning models that consistently outperformed conventional machine learning algorithms, highlighting the strength of domain-specific pre-trained language models for SLR abstract screening. This study provides a foundation for the development of more intelligent SLR systems.

Background This study provides an updated and expanded analysis of the impact of the COVID-19 pandemic on routine vaccinations across the life-course in the United States. Research design and methods Routine wellness visits and vaccination rates were calculated using structured claims data for each month during the impact period (January 2020 to August 2022) and compared to the respective baseline period (January 2018 to December 2019). Monthly rates were aggregated as annual accumulated and cumulative percent changes. Results The complete monthly rate interactive dataset can be viewed at https://vaccinationtrends.com. The greatest decrease in annual accumulated administration rates in the 0–2 and 4–6 years age groups was for the measles, mumps, and rubella vaccine; for adolescents and older adults, it was for human papillomavirus and pneumococcal vaccines, respectively. Routine in-person wellness visit rates recovered faster and more completely than vaccination rates in all age groups, indicating potential missed opportunities to administer vaccines during visits. Conclusions This updated analysis reveals that the negative impact of the COVID-19 pandemic on routine vaccination continued through 2021 and into 2022. Proactive efforts to reverse this decline are needed to increase individual- and population-level vaccination coverage and avoid the associated preventable morbidity, mortality, and health care costs.

BackgroundVaccines serve as a crucial public health tool, although vaccine hesitancy continues to pose a significant threat to full vaccine uptake and, consequently, community health. Understanding and tracking vaccine hesitancy is essential for effective public health interventions; however, traditional survey methods present various limitations. ObjectiveThis study aimed to create a real-time, natural language processing (NLP)–based tool to assess vaccine sentiment and hesitancy across 3 prominent social media platforms. MethodsWe mined and curated discussions in English from Twitter (subsequently rebranded as X), Reddit, and YouTube social media platforms posted between January 1, 2011, and October 31, 2021, concerning human papillomavirus; measles, mumps, and rubella; and unspecified vaccines. We tested multiple NLP algorithms to classify vaccine sentiment into positive, neutral, or negative and to classify vaccine hesitancy using the World Health Organization’s (WHO) 3Cs (confidence, complacency, and convenience) hesitancy model, conceptualizing an online dashboard to illustrate and contextualize trends. ResultsWe compiled over 86 million discussions. Our top-performing NLP models displayed accuracies ranging from 0.51 to 0.78 for sentiment classification and from 0.69 to 0.91 for hesitancy classification. Explorative analysis on our platform highlighted variations in online activity about vaccine sentiment and hesitancy, suggesting unique patterns for different vaccines. ConclusionsOur innovative system performs real-time analysis of sentiment and hesitancy on 3 vaccine topics across major social networks, providing crucial trend insights to assist campaigns aimed at enhancing vaccine uptake and public health.

BackgroundThe importance of real-world evidence is widely recognized in observational oncology studies. However, the lack of interoperable data quality standards in the fragmented health information technology landscape represents an important challenge. Therefore, adopting validated systematic methods for evaluating data quality is important for oncology outcomes research leveraging real-world data (RWD). ObjectiveThis study aims to implement real-world time to treatment discontinuation (rwTTD) for a systemic anticancer therapy (SACT) as a new use case for the Use Case Specific Relevance and Quality Assessment, a framework linking data quality and relevance in fit-for-purpose RWD assessment. MethodsTo define the rwTTD use case, we mapped the operational definition of rwTTD to RWD elements commonly available from oncology electronic health record–derived data sets. We identified 20 tasks to check the completeness and plausibility of data elements concerning SACT use, line of therapy (LOT), death date, and length of follow-up. Using descriptive statistics, we illustrated how to implement the Use Case Specific Relevance and Quality Assessment on 2 oncology databases (Data sets A and B) to estimate the rwTTD of an SACT drug (target SACT) for patients with advanced head and neck cancer diagnosed on or after January 1, 2015. ResultsA total of 1200 (24.96%) of 4808 patients in Data set A and 237 (5.92%) of 4003 patients in Data set B received the target SACT, suggesting better relevance of the former in estimating the rwTTD of the target SACT. The 2 data sets differed with regard to the terminology used for SACT drugs, LOT format, and target SACT LOT distribution over time. Data set B appeared to have less complete SACT records, longer lags in incorporating the latest data, and incomplete mortality data, suggesting a lack of fitness for estimating rwTTD. ConclusionsThe fit-for-purpose data quality assessment demonstrated substantial variability in the quality of the 2 real-world data sets. The data quality specifications applied for rwTTD estimation can be expanded to support a broad spectrum of oncology use cases.

Kaposi’s sarcoma-associated herpesvirus (KSHV) can infect a variety of cells and cause malignant tumors. At present, the use of microRNA (miRNA) for anti-KSHV is a promising treatment strategy, but the instability and non-specific uptake of miRNA still limit its use in the treatment of KSHV. In the present study, we constructed a nano-drug delivery system employing chemical grafting and electrostatic adsorption to solve the problems of easy degradation and low cell uptake of miRNA during direct administration. This nano-drug delivery system is to graft 4-carboxyphenylboric acid (PBA) and lauric acid (LA) onto polyethylenimine (PEI) through amidation reaction, and then prepare cationic copolymer nanocarriers (LA-PEI-PBA). The drug-carrying nanocomplex LA-PEI-PBA/miR-34a-5p was formed after further electrostatic adsorption of miR-34a-5p on the carrier and could protect miR-34a-5p from nuclease and serum degradation. Modification of the drug-carrying nanocomplex LA-PEI-PBA/miR-34a-5p by targeted molecule PBA showed effective uptake, increase in the level of miR-34a-5p, and inhibition of cell proliferation and migration in KSHV-infected cells. In addition, the drug-carrying nanocomplex could also significantly reduce the expression of KSHV lytic and latent genes, achieving the purpose of anti-KSHV treatment. In conclusion, these cationic copolymer nanocarriers with PBA targeting possess potential applications in nucleic acid delivery and anti-KSHV therapy.

Abnormal translate regulation is an important phenomenon in cancer initiation and progression. Eukaryotic translation initiation factor 4A1 (eIF4A1) protein is an ATP-dependent Ribonucleic Acid (RNA) helicase, which is essential for translation and has bidirectional RNA unwinders function. In this review, we discuss the levels of expression, regulatory mechanisms and protein functions of eIF4A1 in different human tumors. eIF4A1 is often involved as a target of microRNAs or long non-coding RNAs during the epithelial-mesenchymal transition, associating with the proliferation and metastasis of tumor cells. eIF4A1 protein exhibits the promising biomarker for rapid diagnosis of pre-cancer lesions, histological phenotypes, clinical staging diagnosis and outcome prediction, which provides a novel strategy for precise medical care and target therapy for patients with tumors at the same time, relevant small molecule inhibitors have also been applied in clinical practice, providing reliable theoretical support and clinical basis for the development of this gene target.

Abstract The study of histopathological phenotypes is vital for cancer research and medicine as it links molecular mechanisms to disease prognosis. It typically involves integration of heterogenous histopathological features in whole-slide images (WSI) to objectively characterize a histopathological phenotype. However, the large-scale implementation of phenotype characterization has been hindered by the fragmentation of histopathological features, resulting from the lack of a standardized format and a controlled vocabulary for structured and unambiguous representation of semantics in WSIs. To fill this gap, we propose the Histopathology Markup Language (HistoML), a representation language along with a controlled vocabulary (Histopathology Ontology) based on Semantic Web technologies. Multiscale features within a WSI, from single-cell features to mesoscopic features, could be represented using HistoML which is a crucial step towards the goal of making WSIs findable, accessible, interoperable and reusable (FAIR). We pilot HistoML in representing WSIs of kidney cancer as well as thyroid carcinoma and exemplify the uses of HistoML representations in semantic queries to demonstrate the potential of HistoML-powered applications for phenotype characterization.

Abstract Radiofrequency catheter ablation (RFCA) is widely used to treat atrial fibrillation (AF), but its effect on left atrial (LA) remodeling in patients with AF is not completely clarified. Few studies have reported the changes in structure and function of the left atrium in patients with different types of AF after RFCA. To analyze the effect of RFCA on the LA structure and function in patients with nonvalvular paroxysmal AF, persistent AF and long-standing persistent AF (LSPAF). RFCA was performed in 180 patients with paroxysmal AF, persistent AF and LSPAF. The changes of LA structure and function in echocardiogram and speckle-tracking echocardiography findings were compared before the procedure, and at 1, 2, 3, 4 weeks, and 2, 3, 6, and 9–12 months after the procedure. There were 60 patients in the paroxysmal AF group, 60 in the persistent AF group and 60 patients in LSPAF group. The pre-procedure LA diameter and volume were smaller in the paroxysmal AF group than persistent AF and LSPAF group. There was no significant change of in the LA structure and function in the paroxysmal AF group within 1 year. In the persistent AF and LSPAF groups, LA structure (anteroposterior diameter, LA volume) significantly decreased, but remained larger than that in paroxysmal AF group. In persistent and LSPAF, function (LA ejection fraction, strain, strain rate) increased significantly within 1 week, then gradually increased. RFCA improved the LA structure and function and resulted in heart reverse remodeling, especially for persistent AF and LSPAF.

The US Advisory Committee on Immunization Practice recommends routine human papillomavirus (HPV) vaccination at 11–12 years of age, but states that vaccination may be initiated as early as 9 years. Our primary goal was to assess whether initiating HPV vaccination at 9–10 years of age, compared to 11–12, was associated with a higher rate of series completion by 13 years of age, and to identify factors associated with series completion by age 13. The study used vaccine claims and other data from the IBM MarketScan Commercial Claims and Encounters (privately insured) and IBM MarketScan Multi-State Medicaid (publicly insured) databases. Participants were 9–12 years of age and initiated HPV vaccination between January 2006 and December 2018 (publicly insured) or February 2019 (privately insured). Among 100,117 privately insured individuals, those initiating the HPV vaccination series at 9–10 years of age had a significantly higher series completion rate by 13 years of age than did those initiating at 11–12 years of age (76.2% versus 48.1%; p

Objective: To understand the perspectives of persons’ living with diabetes about the increasing cost of diabetes management through an analysis of online health communities (OHCs) and the impact of persons’ participation in OHCs on their capacity and treatment burden. Patients and Methods: A qualitative study of 556 blog posts submitted between January 1, 2007 and December 31, 2017 to 4 diabetes social networking sites was conducted between March 2018 and July 2019. All posts were coded inductively using thematic analysis procedures. Eton’s Burden of Treatment Framework and Boehmer’s Theory of Patient Capacity directed triangulation of themes with existing theory. Results: Three themes were identified: (1) cost barriers to care: participants describe individual and systemic cost barriers that inhibit prescribed therapy goals; (2) impact of financial cost on health: participants describe the financial effects of care on their physical and emotional health; and (3) saving strategies to overcome cost impact: participants discuss practical strategies that help them achieve therapy goals. Finally, we also identify that the use of OHCs serves to increase persons’ capacity with the potential to decrease treatment burden, ultimately improving mental and physical health. Conclusion: High cost for diabetes care generated barriers that negatively affected physical health and emotional states. Participant-shared experiences in OHCs increased participants’ capacity to manage the burden. Potential solutions include cost-based shared decision-making tools and advocacy for policy change.

Topic modeling is an active research area with several unanswered questions. The focus of recent research in this area is on the use of a vector embedding representation of the input text with both generative and evolutionary topic modeling techniques. Unfortunately, it is hard to compare different techniques when the underlying data and preprocessing steps that were used to develop the models are not available. This paper presents two secondary datasets that can help address this gap. These datasets are derived from two primary datasets. The first consists of 8145 posts from the r/Cancer health forum and the second consists of 18,294 messages submitted to 20 different news groups. The same preprocessing procedure is applied to both datasets by removing punctuation, stop words and high frequency words. Each dataset is then clustered using three different topic modeling techniques: pPSO, ETM and NVDM and three topic numbers: 10, 20, 30. In addition, for pPSO two text embeddings representation are considered: sBERT and Skipgram. The secondary datasets were originally developed in support of a comparative analysis of the aforementioned topic modeling techniques in a study titled “Comparing PSO-based Clustering over Contextual Vector Embeddings to Modern Topic Modeling” submitted to the Journal of Information Processing and Management. The present paper provides a detailed description of the two secondary datasets including the unique identifier that can be used to retrieve the original documents, the pre-processing scripts, the topic keywords generated by the three topic modeling techniques with varying topic numbers and embedding representations. As such, the datasets allow direct comparison with other topic modeling techniques. To further facilitate this process, the algorithm underlying the evolutionary topic modeling technique, pPSO, proposed by the authors is also provided.

PurposeGiven the digital transformation of service businesses by providing online food services and the influence of online reviews on consumers’ purchasing decisions, this study examines how service recovery attributes in different stages influence relationship marketing strategies, i.e., relationship quality and customer loyalty after service failure. This study is built upon a revised service recovery cycle model by accounting for three stages and their corresponding attributes; whereon a conceptual stage model of service recovery is proposed. This conceptual stage model incorporates stages of service recovery, their respective attributes, and how they influence relationship marketing strategies.Design/methodology/approachAn online marketing company was employed for data collection in 2019, which resulted in 301 valid responses. A Structural Equation Model (SEM) was conducted with all the data to test the relationships between the constructs. The individual measurement model was tested using the Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). A structural model was estimated using AMOS to test all the hypotheses.FindingsThe findings demonstrate that the attributes (i.e., response speed, compensation) paired with the first two stages of service recovery can significantly influence consumer loyalty in a positive state. The findings also manifest the intermediary role that relationship quality has played in the association of service recovery and consumer loyalty, which implies that the food delivery businesses could attain a more comprehended relationship quality with consumers through active and timely compensatory service recovery consumer loyalty to the food businesses.Originality/valueThis study examines how these different stages of the service recovery cycle influence the decision-making of relationship marketing strategies (i.e., relationship quality, customer loyalty) on the prerequisite of service failure. This study aspires to expand the service recovery research by objectifying a conceptual stage model of service recovery, incorporating stages’ recovery attributes and how these recovery attributes reciprocally influence relationship quality and customer loyalty.

BackgroundPatient-clinician secure messaging is an important function in patient portals and enables patients and clinicians to communicate on a wide spectrum of issues in a timely manner. With its growing adoption and patient engagement, it is time to comprehensively study the secure messages and user behaviors in order to improve patient-centered care. ObjectiveThe aim of this paper was to analyze the secure messages sent by patients and clinicians in a large multispecialty health system at Mayo Clinic, Rochester. MethodsWe performed message-based, sender-based, and thread-based analyses of more than 5 million secure messages between 2010 and 2017. We summarized the message volumes, patient and clinician population sizes, message counts per patient or clinician, as well as the trends of message volumes and user counts over the years. In addition, we calculated the time distribution of clinician-sent messages to understand their workloads at different times of a day. We also analyzed the time delay in clinician responses to patient messages to assess their communication efficiency and the back-and-forth rounds to estimate the communication complexity. ResultsDuring 2010-2017, the patient portal at Mayo Clinic, Rochester experienced a significant growth in terms of the count of patient users and the total number of secure messages sent by patients and clinicians. Three clinician categories, namely “physician—primary care,” “registered nurse—specialty,” and “physician—specialty,” bore the majority of message volume increase. The patient portal also demonstrated growing trends in message counts per patient and clinician. The “nurse practitioner or physician assistant—primary care” and “physician—primary care” categories had the heaviest per-clinician workload each year. Most messages by the clinicians were sent from 7 AM to 5 PM during a day. Yet, between 5 PM and 7 PM, the physicians sent 7.0% (95,785/1,377,006) of their daily messages, and the nurse practitioner or physician assistant sent 5.4% (22,121/408,526) of their daily messages. The clinicians replied to 72.2% (1,272,069/1,761,739) patient messages within 1 day and 90.6% (1,595,702/1,761,739) within 3 days. In 95.1% (1,499,316/1,576,205) of the message threads, the patients communicated with their clinicians back and forth for no more than 4 rounds. ConclusionsOur study found steady increases in patient adoption of the secure messaging system and the average workload per clinician over 8 years. However, most clinicians responded timely to meet the patients’ needs. Our study also revealed differential patient-clinician communication patterns across different practice roles and care settings. These findings suggest opportunities for care teams to optimize messaging tasks and to balance the workload for optimal efficiency.

Abstract Background Sequence alignment is a way of arranging sequences (e.g., DNA, RNA, protein, natural language, financial data, or medical events) to identify the relatedness between two or more sequences and regions of similarity. For Electronic Health Records (EHR) data, sequence alignment helps to identify patients of similar disease trajectory for more relevant and precise prognosis, diagnosis and treatment of patients. Methods We tested two cutting-edge global sequence alignment methods, namely dynamic time warping (DTW) and Needleman-Wunsch algorithm (NWA), together with their local modifications, DTW for Local alignment (DTWL) and Smith-Waterman algorithm (SWA), for aligning patient medical records. We also used 4 sets of synthetic patient medical records generated from a large real-world EHR database as gold standard data, to objectively evaluate these sequence alignment algorithms. Results For global sequence alignments, 47 out of 80 DTW alignments and 11 out of 80 NWA alignments had superior similarity scores than reference alignments while the rest 33 DTW alignments and 69 NWA alignments had the same similarity scores as reference alignments. Forty-six out of 80 DTW alignments had better similarity scores than NWA alignments with the rest 34 cases having the equal similarity scores from both algorithms. For local sequence alignments, 70 out of 80 DTWL alignments and 68 out of 80 SWA alignments had larger coverage and higher similarity scores than reference alignments while the rest DTWL alignments and SWA alignments received the same coverage and similarity scores as reference alignments. Six out of 80 DTWL alignments showed larger coverage and higher similarity scores than SWA alignments. Thirty DTWL alignments had the equal coverage but better similarity scores than SWA. DTWL and SWA received the equal coverage and similarity scores for the rest 44 cases. Conclusions DTW, NWA, DTWL and SWA outperformed the reference alignments. DTW (or DTWL) seems to align better than NWA (or SWA) by inserting new daily events and identifying more similarities between patient medical records. The evaluation results could provide valuable information on the strengths and weakness of these sequence alignment methods for future development of sequence alignment methods and patient similarity-based studies.

ASEAN is an interesting case study of regional power connectivity in Asia and the Pacific due to its geographic location and ongoing power connectivity within and beyond ASEAN. This paper reviews ASEAN’s power connectivity within ASEAN and between ASEAN and its neighbours (hereafter ASEAN connectivity). Through literature survey, it identifies challenges to the ASEAN connectivity from political, legal, economic and technical perspectives. Based on these analyses, it then explores what, how and when regional cooperation may be able to facilitate ASEAN power connectivity. Keywords: Power connectivity, ASEAN, China, ASEAN Power Grid, GMS

China's Belt and Road Initiative (BRI) has attracted the world's attention since it was proposed. This study focuses on the BRI in the energy sector in ASEAN. It examines the current status of China's investment into ASEAN's energy sector. It then discusses the problems of China's investment into the ASEAN energy sector with two cases in Myanmar. In addressing the problems and challenges, the study adopts a ‘sense of ownership’ framework to analyze how to improve the acceptability of the Chinese investment. It argues that the BRI priorities, including ‘people-to-people bond’, provide an opportunity to bring a sense of ownership to the community networking that is to be built as a key part of BRI and more profoundly, an opportunity to establish a good image and to make investment projects successful. Keywords: Belt and Road Initiative, ASEAN, Energy investment, Sense of ownership

Abstract Background Accurate information in provider directories are vital in health care including health information exchange, health benefits exchange, quality reporting, and in the reimbursement and delivery of care. Maintaining provider directory data and keeping it up to date is challenging. The objective of this study is to determine the feasibility of using natural language processing (NLP) techniques to combine disparate resources and acquire accurate information on health providers. Methods Publically available state licensure lists in Connecticut were obtained along with National Plan and Provider Enumeration System (NPPES) public use files. Connecticut licensure lists textual information of each health professional who is licensed to practice within the state. A NLP-based system was developed based on healthcare provider taxonomy code, location, name and address information to identify textual data within the state and federal records. Qualitative and quantitative evaluation were performed, and the recall and precision were calculated. Results We identified nurse midwives, nurse practitioners, and dentists in the State of Connecticut. The recall and precision were 0.95 and 0.93 respectively. Using the system, we were able to accurately acquire 6849 of the 7177 records of health provider directory information. Conclusions The authors demonstrated that the NLP- based approach was effective at acquiring health provider information. Furthermore, the NLP-based system can always be applied to update information further reducing processing burdens as data changes.

BackgroundGenealogical information, such as that found in family trees, is imperative for biomedical research such as disease heritability and risk prediction. Researchers have used policyholder and their dependent information in medical claims data and emergency contacts in electronic health records (EHRs) to infer family relationships at a large scale. We have previously demonstrated that online obituaries can be a novel data source for building more complete and accurate family trees. ObjectiveAiming at supplementing EHR data with family relationships for biomedical research, we built an end-to-end information extraction system using a multitask-based artificial neural network model to construct genealogical knowledge graphs (GKGs) from online obituaries. GKGs are enriched family trees with detailed information including age, gender, death and birth dates, and residence. MethodsBuilt on a predefined family relationship map consisting of 4 types of entities (eg, people’s name, residence, birth date, and death date) and 71 types of relationships, we curated a corpus containing 1700 online obituaries from the metropolitan area of Minneapolis and St Paul in Minnesota. We also adopted data augmentation technology to generate additional synthetic data to alleviate the issue of data scarcity for rare family relationships. A multitask-based artificial neural network model was then built to simultaneously detect names, extract relationships between them, and assign attributes (eg, birth dates and death dates, residence, age, and gender) to each individual. In the end, we assemble related GKGs into larger ones by identifying people appearing in multiple obituaries. ResultsOur system achieved satisfying precision (94.79%), recall (91.45%), and F-1 measures (93.09%) on 10-fold cross-validation. We also constructed 12,407 GKGs, with the largest one made up of 4 generations and 30 people. ConclusionsIn this work, we discussed the meaning of GKGs for biomedical research, presented a new version of a corpus with a predefined family relationship map and augmented training data, and proposed a multitask deep neural system to construct and assemble GKGs. The results show our system can extract and demonstrate the potential of enriching EHR data for more genetic research. We share the source codes and system with the entire scientific community on GitHub without the corpus for privacy protection.

BackgroundPatient portals tethered to electronic health records systems have become attractive web platforms since the enacting of the Medicare Access and Children’s Health Insurance Program Reauthorization Act and the introduction of the Meaningful Use program in the United States. Patients can conveniently access their health records and seek consultation from providers through secure web portals. With increasing adoption and patient engagement, the volume of patient secure messages has risen substantially, which opens up new research and development opportunities for patient-centered care. ObjectiveThis study aims to develop a data model for patient secure messages based on the Fast Healthcare Interoperability Resources (FHIR) standard to identify and extract significant information. MethodsWe initiated the first draft of the data model by analyzing FHIR and manually reviewing 100 sentences randomly sampled from more than 2 million patient-generated secure messages obtained from the online patient portal at the Mayo Clinic Rochester between February 18, 2010, and December 31, 2017. We then annotated additional sets of 100 randomly selected sentences using the Multi-purpose Annotation Environment tool and updated the data model and annotation guideline iteratively until the interannotator agreement was satisfactory. We then created a larger corpus by annotating 1200 randomly selected sentences and calculated the frequency of the identified medical concepts in these sentences. Finally, we performed topic modeling analysis to learn the hidden topics of patient secure messages related to 3 highly mentioned microconcepts, namely, fatigue, prednisone, and patient visit, and to evaluate the proposed data model independently. ResultsThe proposed data model has a 3-level hierarchical structure of health system concepts, including 3 macroconcepts, 28 mesoconcepts, and 85 microconcepts. Foundation and base macroconcepts comprise 33.99% (841/2474), clinical macroconcepts comprise 64.38% (1593/2474), and financial macroconcepts comprise 1.61% (40/2474) of the annotated corpus. The top 3 mesoconcepts among the 28 mesoconcepts are condition (505/2474, 20.41%), medication (424/2474, 17.13%), and practitioner (243/2474, 9.82%). Topic modeling identified hidden topics of patient secure messages related to fatigue, prednisone, and patient visit. A total of 89.2% (107/120) of the top-ranked topic keywords are actually the health concepts of the data model. ConclusionsOur data model and annotated corpus enable us to identify and understand important medical concepts in patient secure messages and prepare us for further natural language processing analysis of such free texts. The data model could be potentially used to automatically identify other types of patient narratives, such as those in various social media and patient forums. In the future, we plan to develop a machine learning and natural language processing solution to enable automatic triaging solutions to reduce the workload of clinicians and perform more granular content analysis to understand patients’ needs and improve patient-centered care.

Abstract Introduction: News media play an important role in raising public awareness, framing public opinions, affecting policy formulation, and acknowledgment of public health issues. Traditional qualitative content analysis for news sentiments and focuses are time-consuming and may not efficiently convey sentiments nor the focuses of news media. Methods: We used descriptive statistics and state-of-art text mining to conduct sentiment analysis and topic modeling, to efficiently analyze over 3 million Reuters news articles during 2007–2017 for identifying their coverage, sentiments, and focuses for public health issues. Based on the top keywords from public health scientific journals, we identified 10 major public health issues (i.e., “air pollution,” “alcohol drinking,” “asthma,” “depression,” “diet,” “exercise,” “obesity,” “pregnancy,” “sexual behavior,” and “smoking”). Results: The news coverage for seven public health issues, “Smoking,” “Exercise,” “Alcohol drinking,” “Diet,” “Obesity,” “Depression,” and “Asthma” decreased over time. The news coverage for “Sexual behavior,” “Pregnancy,” and “Air pollution” fluctuated during 2007–2017. The sentiments of the news articles for three of the public health issues, “exercise,” “alcohol drinking,” and “diet” were predominately positive and associated such as “energy.” Sentiments for the remaining seven public health issues were mainly negative, linked to negative terms, e.g., diseases. The results of topic modeling reflected the media’s focus on public health issues. Conclusions: Text mining methods may address the limitations of traditional qualitative approaches. Using big data to understand public health needs is a novel approach that could help clinical and translational science awards programs focus on community-engaged research efforts to address community priorities.

The object of the study was to develop a quick and reproducible accelerated in vitro release method to predict and deduce the function of the real time (37 °C) release for long acting PLGA microspheres. The method could be described in several steps. First, the release of the microspheres were studied using the sample and separate method at 37 °C with normal orbital shaking and elevated temperatures with magnetic stirring to further accelerate the release. Second, the most similar profile at elevated temperatures with the real time release was chosen with the help of the n value in the fitted Korsmeyer-Peppas Function. Third, the Weibull function and conversion ratio were used to deduce the function of real time release according to the chosen profile at elevated temperatures. The key point in this study was to provide a quick and precise method to predict the real time release for long acting progesterone PLGA microspheres. So the elevated temperatures coupled with magnetic stirring were used to accelerate the release further, and when there have many similar release profiles with the real time release at elevated temperatures, releasing time at elevated temperatures and the R2 of the final deduced function will be used to help choosing the most similar release profile with the real time release. Four different types of progesterone PLGA microspheres were used to verify the method, and all the deduced function correlated well with the real time releases, for R2 = 0.9912, 0.9781, 0.9918 and 0.9972, respectively. Keywords: Long acting PLGA, Microspheres, Elevated temperatures, Korsmeyer-Peppas equation, Weibull function

Abstract Background The right dataset is essential to obtain the right insights in data science; therefore, it is important for data scientists to have a good understanding of the availability of relevant datasets as well as the content, structure, and existing analyses of these datasets. While a number of efforts are underway to integrate the large amount and variety of datasets, the lack of an information resource that focuses on specific needs of target users of datasets has existed as a problem for years. To address this gap, we have developed a Dataset Information Resource (DIR), using a user-oriented approach, which gathers relevant dataset knowledge for specific user types. In the present version, we specifically address the challenges of entry-level data scientists in learning to identify, understand, and analyze major datasets in healthcare. We emphasize that the DIR does not contain actual data from the datasets but aims to provide comprehensive knowledge about the datasets and their analyses. Methods The DIR leverages Semantic Web technologies and the W3C Dataset Description Profile as the standard for knowledge integration and representation. To extract tailored knowledge for target users, we have developed methods for manual extractions from dataset documentations as well as semi-automatic extractions from related publications, using natural language processing (NLP)-based approaches. A semantic query component is available for knowledge retrieval, and a parameterized question-answering functionality is provided to facilitate the ease of search. Results The DIR prototype is composed of four major components—dataset metadata and related knowledge, search modules, question answering for frequently-asked questions, and blogs. The current implementation includes information on 12 commonly used large and complex healthcare datasets. The initial usage evaluation based on health informatics novices indicates that the DIR is helpful and beginner-friendly. Conclusions We have developed a novel user-oriented DIR that provides dataset knowledge specialized for target user groups. Knowledge about datasets is effectively represented in the Semantic Web. At this initial stage, the DIR has already been able to provide sophisticated and relevant knowledge of 12 datasets to help entry health informacians learn healthcare data analysis using suitable datasets. Further development of both content and function levels is underway.

Abstract Background To deliver evidence-based medicine, clinicians often reference resources that are useful to their respective medical practices. Owing to their busy schedules, however, clinicians typically find it challenging to locate these relevant resources out of the rapidly growing number of journals and articles currently being published. The literature-recommender system may provide a possible solution to this issue if the individual needs of clinicians can be identified and applied. Methods We thus collected from the CiteULike website a sample of 96 clinicians and 6,221 scientific articles that they read. We examined the journal distributions, publication types, reading times, and geographic locations. We then compared the distributions of MeSH terms associated with these articles with those of randomly sampled MEDLINE articles using two-sample Z-test and multiple comparison correction, in order to identify the important topics relevant to clinicians. Results We determined that the sampled clinicians followed the latest literature in a timely manner and read papers that are considered landmarks in medical research history. They preferred to read scientific discoveries from human experiments instead of molecular-, cellular- or animal-model-based experiments. Furthermore, the country of publication may impact reading preferences, particularly for clinicians from Egypt, India, Norway, Senegal, and South Africa. Conclusion These findings provide useful guidance for developing personalized literature-recommender systems for clinicians.

Background and objectives: It is unclear why many patients with hypothyroidism prefer the use of desiccated thyroid extract (DTE) as a thyroid hormone replacement formulation over levothyroxine (LT4) treatment, as recommended by clinical practice guidelines. We analyzed patient-reported information from patient online forums to better understand patient preferences for and attitudes toward the use of DTE to treat hypothyroidism. Materials and Methods: We conducted a mixed-methods study by evaluating the content of online posts from three popular hypothyroidism forums from patients currently taking DTE (n = 673). From these posts, we extracted descriptive information on patient demographics and clinical characteristics and qualitatively analyzed posts’ content to explore patient perceptions on DTE and other therapies further. Results: Nearly half (46%) of the patients reported that a clinician initially drove their interest in trying DTE. Patients described many reasons for switching from a previous therapeutic approach to DTE, including lack of improvement in hypothyroidism-related symptoms (58%) and the development of side effects (22%). The majority of patients described DTE as moderately to majorly effective overall (81%) and more effective than the previous therapy (77%). The most frequently described benefits associated with DTE use were an improvement in symptoms (56%) and a change in overall well-being (34%). One-fifth of patients described side effects related to the use of DTE. Qualitative analysis of posts’ content supported these findings and raised additional issues around the need for individualizing therapy approaches for hypothyroidism (e.g., a sense of each patient has different needs), as well as difficulties obtaining DTE (e.g., issues with pharmacy availability). Conclusions: Lack of individualized treatment and a feeling of not been listened to were recurrent themes among DTE users. A subset of patients may prefer DTE to LT4 for many reasons, including perceived better effectiveness and improved overall well-being, despite the risks associated with DTE.

Multiple data sources are preferred in adverse drug event (ADEs) surveillance owing to inadequacies of single source. However, analytic methods to monitor potential ADEs after prolonged drug exposure are still lacking. In this study we propose a method aiming to screen potential ADEs by combining FDA Adverse Event Reporting System (FAERS) and Electronic Medical Record (EMR). The proposed method uses natural language processing (NLP) techniques to extract treatment outcome information captured in unstructured text and adopts case-crossover design in EMR. Performances were evaluated using two ADE knowledge bases: Adverse Drug Reaction Classification System (ADReCS) and SIDER. We tested our method in ADE signal detection of conventional disease-modifying antirheumatic drugs (DMARDs) in rheumatoid arthritis patients. Findings showed that recall greatly increased when combining FAERS with EMR compared with FAERS alone and EMR alone, especially for flexible mapping strategy. Precision (FAERS + EMR) in detecting ADEs improved using ADReCS as gold standard compared with SIDER. In addition, signals detected from EMR have considerably overlapped with signals detected from FAERS or ADE knowledge bases, implying the importance of EMR for pharmacovigilance. ADE signals detected from EMR and/or FAERS but not in existing knowledge bases provide hypothesis for future study.

The interaction of death-associated protein kinase 1 (DAPK1) with the 2B subunit (GluN2B) C-terminus of N-methyl-D-aspartate receptor (NMDAR) plays a critical role in the pathophysiology of depression and is considered a potential target for the structure-based discovery of new antidepressants. However, the 3D structures of C-terminus residues 1290⁻1310 of GluN2B (GluN2B-CT1290-1310) remain elusive and the interaction between GluN2B-CT1290-1310 and DAPK1 is unknown. In this study, the mechanism of interaction between DAPK1 and GluN2B-CT1290-1310 was predicted by computational simulation methods including protein⁻peptide docking and molecular dynamics (MD) simulation. Based on the equilibrated MD trajectory, the total binding free energy between GluN2B-CT1290-1310 and DAPK1 was computed by the mechanics generalized born surface area (MM/GBSA) approach. The simulation results showed that hydrophobic, van der Waals, and electrostatic interactions are responsible for the binding of GluN2B-CT1290⁻1310/DAPK1. Moreover, through per-residue free energy decomposition and in silico alanine scanning analysis, hotspot residues between GluN2B-CT1290-1310 and DAPK1 interface were identified. In conclusion, this work predicted the binding mode and quantitatively characterized the protein⁻peptide interface, which will aid in the discovery of novel drugs targeting the GluN2B-CT1290-1310 and DAPK1 interface.

A scientific ontology is a formal representation of knowledge within a domain, typically including central concepts, their properties, and relations. With the rise of computers and high-throughput data collection, ontologies have become essential to data mining and sharing across communities in the biomedical sciences. Powerful approaches exist for testing the internal consistency of an ontology, but not for assessing the fidelity of its domain representation. We introduce a family of metrics that describe the breadth and depth with which an ontology represents its knowledge domain. We then test these metrics using (1) four of the most common medical ontologies with respect to a corpus of medical documents and (2) seven of the most popular English thesauri with respect to three corpora that sample language from medicine, news, and novels. Here we show that our approach captures the quality of ontological representation and guides efforts to narrow the breach between ontology and collective discourse within a domain. Our results also demonstrate key features of medical ontologies, English thesauri, and discourse from different domains. Medical ontologies have a small intersection, as do English thesauri. Moreover, dialects characteristic of distinct domains vary strikingly as many of the same words are used quite differently in medicine, news, and novels. As ontologies are intended to mirror the state of knowledge, our methods to tighten the fit between ontology and domain will increase their relevance for new areas of biomedical science and improve the accuracy and power of inferences computed across them.