Journal of Biomedical Informatics最新文献

{"title":"Balancing the efforts of chart review and gains in PRS prediction accuracy: An empirical study","authors":"Yuqing Lei ,&nbsp;Adam Christian Naj ,&nbsp;Hua Xu ,&nbsp;Ruowang Li ,&nbsp;Yong Chen","doi":"10.1016/j.jbi.2024.104705","DOIUrl":"10.1016/j.jbi.2024.104705","url":null,"abstract":"<div><h3>Objective</h3><p>Phenotypic misclassification in genetic association analyses can impact the accuracy of PRS-based prediction models. The bias reduction method proposed by Tong et al. (2019) has demonstrated its efficacy in reducing the effects of bias on the estimation of association parameters between genotype and phenotype while minimizing variance by employing chart reviews on a subset of the data for validating phenotypes, however its improvement of subsequent PRS prediction accuracy remains unclear. Our study aims to fill this gap by assessing the performance of simulated PRS models and estimating the optimal number of chart reviews needed for validation.</p></div><div><h3>Methods</h3><p>To comprehensively assess the efficacy of the bias reduction method proposed by Tong et al. in enhancing the accuracy of PRS-based prediction models, we simulated each phenotype under different correlation structures (an independent model, a weakly correlated model, a strongly correlated model) and introduced error-prone phenotypes using two distinct error mechanisms (differential and non-differential phenotyping errors). To facilitate this, we used genotype and phenotype data from 12 case-control datasets in the Alzheimer’s Disease Genetics Consortium (ADGC) to produce simulated phenotypes. The evaluation included analyses across various misclassification rates of original phenotypes as well as quantities of validation set. Additionally, we determined the median threshold, identifying the minimal validation size required for a meaningful improvement in the accuracy of PRS-based predictions across a broad spectrum.</p></div><div><h3>Results</h3><p>This simulation study demonstrated that incorporating chart review does not universally guarantee enhanced performance of PRS-based prediction models. Specifically, in scenarios with minimal misclassification rates and limited validation sizes, PRS models utilizing debiased regression coefficients demonstrated inferior predictive capabilities compared to models using error-prone phenotypes. Put differently, the effectiveness of the bias reduction method is contingent upon the misclassification rates of phenotypes and the size of the validation set employed during chart reviews. Notably, when dealing with datasets featuring higher misclassification rates, the advantages of utilizing this bias reduction method become more evident, requiring a smaller validation set to achieve better performance.</p></div><div><h3>Conclusion</h3><p>This study highlights the importance of choosing an appropriate validation set size to balance between the efforts of chart review and the gain in PRS prediction accuracy. Consequently, our study establishes a valuable guidance for validation planning, across a diverse array of sensitivity and specificity combinations.</p></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"157 ","pages":"Article 104705"},"PeriodicalIF":4.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141971201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A GPT-based EHR modeling system for unsupervised novel disease detection","authors":"Boran Hao ,&nbsp;Yang Hu ,&nbsp;William G. Adams ,&nbsp;Sabrina A. Assoumou ,&nbsp;Heather E. Hsu ,&nbsp;Nahid Bhadelia ,&nbsp;Ioannis Ch. Paschalidis","doi":"10.1016/j.jbi.2024.104706","DOIUrl":"10.1016/j.jbi.2024.104706","url":null,"abstract":"<div><h3>Objective</h3><p>To develop an <em>Artificial Intelligence (AI)</em>-based anomaly detection model as a complement of an “astute physician” in detecting novel disease cases in a hospital and preventing emerging outbreaks<em>.</em></p></div><div><h3>Methods</h3><p>Data included hospitalized patients (n = 120,714) at a safety-net hospital in Massachusetts. A novel <em>Generative Pre-trained Transformer (GPT)</em>-based clinical anomaly detection system was designed and further trained using <em>Empirical Risk Minimization (ERM)</em>, which can model a hospitalized patient’s <em>Electronic Health Records (EHR)</em> and detect atypical patients. Methods and performance metrics, similar to the ones behind the recent <em>Large Language Models (LLMs)</em>, were leveraged to capture the dynamic evolution of the patient’s clinical variables and compute an <em>Out-Of-Distribution (OOD)</em> anomaly score.</p></div><div><h3>Results</h3><p>In a completely unsupervised setting, hospitalizations for <em>Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)</em> infection could have been predicted by our GPT model at the beginning of the COVID-19 pandemic, with an Area Under the Receiver Operating Characteristic Curve (AUC) of 92.2 %, using 31 extracted clinical variables and a 3-day detection window. Our GPT achieves individual patient-level anomaly detection and mortality prediction AUC of 78.3 % and 94.7 %, outperforming traditional linear models by 6.6 % and 9 %, respectively. Different types of clinical trajectories of a SARS-CoV-2 infection are captured by our model to make interpretable detections, while a trend of over-pessimistic outcome prediction yields a more effective detection pathway. Furthermore, our comprehensive GPT model can potentially assist clinicians with forecasting patient clinical variables and developing personalized treatment plans.</p></div><div><h3>Conclusion</h3><p>This study demonstrates that an emerging outbreak can be accurately detected within a hospital, by using a GPT to model patient EHR time sequences and labeling them as anomalous when actual outcomes are not supported by the model. Such a GPT is also a comprehensive model with the functionality of generating future patient clinical variables, which can potentially assist clinicians in developing personalized treatment plans.</p></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"157 ","pages":"Article 104706"},"PeriodicalIF":4.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141912806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The reuse of electronic health records information models in the oncology domain: Studies with the bioframe framework","authors":"Rodrigo Bonacin ,&nbsp;Elaine Barbosa de Figueiredo ,&nbsp;Ferrucio de Franco Rosa ,&nbsp;Julio Cesar dos Reis ,&nbsp;Mariangela Dametto","doi":"10.1016/j.jbi.2024.104704","DOIUrl":"10.1016/j.jbi.2024.104704","url":null,"abstract":"<div><h3>Objective:</h3><p>The reuse of Electronic Health Records (EHR) information models (<em>e.g.</em>, templates and archetypes) may bring various benefits, including higher standardization, integration, interoperability, increased productivity in developing EHR systems, and unlock potential Artificial Intelligence applications built on top of medical records. The literature presents recent advances in standards for modeling EHR, in Knowledge Organization Systems (KOS) and EHR data reuse. However, methods, development processes, and frameworks to improve the reuse of EHR information models are still scarce. This study proposes a software engineering framework, named BioFrame, and analyzes how the reuse of EHR information models can be improved during the development of EHR systems.</p></div><div><h3>Methods:</h3><p>EHR standards and KOS, including ontologies, identified from systematic reviews were considered in developing the BioFrame framework. We used the structure of the OpenEHR to model templates and archetypes, as well as its relationship to international KOS used in the oncology domain. Our framework was applied in the context of pediatric oncology. Three data entry forms concerning nutrition and one utilized during the first pediatric oncology consultations were analyzed to measure the reuse of information models.</p></div><div><h3>Results:</h3><p>There was an increase in the adherence rate to international KOS of 18% to the original forms. There was an increase in the concepts reused in all 12 scenarios analyzed, with an average reuse of 6.55% in the original forms compared to 17.1% using BioFrame, resulting in significant differences.</p></div><div><h3>Conclusions:</h3><p>Our results point to higher reuse rates achieved due to an engineering process that provided greater adherence to EHR standards combined with semantic artifacts. This reveals the potential to develop new methods and frameworks aimed at EHR information model reuse. Additional research is needed to evaluate the impacts of the reuse of the EHR information model on interoperability, EHR data reuse, and data quality and assess the proposed framework in other health domains.</p></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"157 ","pages":"Article 104704"},"PeriodicalIF":4.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141912807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Call for papers: Special issue on biomedical multimodal large language models − novel approaches and applications","authors":"Jiang Bian (Guest Editors) ,&nbsp;Yifan Peng (Guest Editors) ,&nbsp;Eneida Mendonca (Guest Editors) ,&nbsp;Imon Banerjee (Guest Editors) ,&nbsp;Hua Xu (Guest Editors)","doi":"10.1016/j.jbi.2024.104703","DOIUrl":"10.1016/j.jbi.2024.104703","url":null,"abstract":"","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"157 ","pages":"Article 104703"},"PeriodicalIF":4.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1532046424001217/pdfft?md5=20358de0bd9928185db4a6d57dd5d38a&pid=1-s2.0-S1532046424001217-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141901902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"fmi-ii: Table of Contents","authors":"","doi":"10.1016/S1532-0464(24)00116-3","DOIUrl":"10.1016/S1532-0464(24)00116-3","url":null,"abstract":"","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"156 ","pages":"Article 104698"},"PeriodicalIF":4.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1532046424001163/pdfft?md5=76710ccc769127af9cdd07b59ff00b67&pid=1-s2.0-S1532046424001163-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141960189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cover 1/Spine","authors":"","doi":"10.1016/S1532-0464(24)00115-1","DOIUrl":"10.1016/S1532-0464(24)00115-1","url":null,"abstract":"","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"156 ","pages":"Article 104697"},"PeriodicalIF":4.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1532046424001151/pdfft?md5=67a0eb87b75778096d1ff5ea786d1411&pid=1-s2.0-S1532046424001151-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141960187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cover 2: Editorial Board","authors":"","doi":"10.1016/S1532-0464(24)00112-6","DOIUrl":"10.1016/S1532-0464(24)00112-6","url":null,"abstract":"","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"156 ","pages":"Article 104694"},"PeriodicalIF":4.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1532046424001126/pdfft?md5=ff45d9e644d1947e239f03e4f58fafef&pid=1-s2.0-S1532046424001126-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141960188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rare disease diagnosis using knowledge guided retrieval augmentation for ChatGPT","authors":"Charlotte Zelin ,&nbsp;Wendy K. Chung ,&nbsp;Mederic Jeanne ,&nbsp;Gongbo Zhang ,&nbsp;Chunhua Weng","doi":"10.1016/j.jbi.2024.104702","DOIUrl":"10.1016/j.jbi.2024.104702","url":null,"abstract":"<div><p>Although rare diseases individually have a low prevalence, they collectively affect nearly 400 million individuals around the world. On average, it takes five years for an accurate rare disease diagnosis, but many patients remain undiagnosed or misdiagnosed. As machine learning technologies have been used to aid diagnostics in the past, this study aims to test ChatGPT’s suitability for rare disease diagnostic support with the enhancement provided by Retrieval Augmented Generation (RAG). RareDxGPT, our enhanced ChatGPT model, supplies ChatGPT with information about 717 rare diseases from an external knowledge resource, the RareDis Corpus, through RAG. In RareDxGPT, when a query is entered, the three documents most relevant to the query in the RareDis Corpus are retrieved. Along with the query, they are returned to ChatGPT to provide a diagnosis. Additionally, phenotypes for thirty different diseases were extracted from free text from PubMed’s Case Reports. They were each entered with three different prompt types: “prompt”, “prompt + explanation” and “prompt + role play.” The accuracy of ChatGPT and RareDxGPT with each prompt was then measured. With “Prompt”, RareDxGPT had a 40 % accuracy, while ChatGPT 3.5 got 37 % of the cases correct. With “Prompt + Explanation”, RareDxGPT had a 43 % accuracy, while ChatGPT 3.5 got 23 % of the cases correct. With “Prompt + Role Play”, RareDxGPT had a 40 % accuracy, while ChatGPT 3.5 got 23 % of the cases correct. To conclude, ChatGPT, especially when supplying extra domain specific knowledge, demonstrates early potential for rare disease diagnosis with adjustments.</p></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"157 ","pages":"Article 104702"},"PeriodicalIF":4.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141859869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Desiderata for discoverability and FAIR adoption of health data hubs","authors":"Celia Alvarez-Romero ,&nbsp;Máximo Bernabeu-Wittel ,&nbsp;Carlos Luis Parra-Calderón ,&nbsp;Silvia Rodríguez Mejías ,&nbsp;Alicia Martínez-García","doi":"10.1016/j.jbi.2024.104700","DOIUrl":"10.1016/j.jbi.2024.104700","url":null,"abstract":"<div><h3>Background</h3><p>The future European Health Research and Innovation Cloud (HRIC), as fundamental part of the European Health Data Space (EHDS), will promote the secondary use of data and the capabilities to push the boundaries of health research within an ethical and legally compliant framework that reinforces the trust of patients and citizens.</p></div><div><h3>Objective</h3><p>This study aimed to analyse health data management mechanisms in Europe to determine their alignment with FAIR principles and data discovery generating best.</p><p>practices for new data hubs joining the HRIC ecosystem. In this line, the compliance of health data hubs with FAIR principles and data discovery were assessed, and a set of best practices for health data hubs was concluded.</p></div><div><h3>Methods</h3><p>A survey was conducted in January 2022, involving 99 representative health data hubs from multiple countries, and 42 responses were obtained in June 2022. Stratification methods were employed to cover different levels of granularity. The survey data was analysed to assess compliance with FAIR and data discovery principles. The study started with a general analysis of survey responses, followed by the creation of specific profiles based on three categories: organization type, function, and level of data aggregation.</p></div><div><h3>Results</h3><p>The study produced specific best practices for data hubs regarding the adoption of FAIR principles and data discoverability. It also provided an overview of the survey study and specific profiles derived from category analysis, considering different types of data hubs.</p></div><div><h3>Conclusions</h3><p>The study concluded that a significant number of health data hubs in Europe did not fully comply with FAIR and data discovery principles. However, the study identified specific best practices that can guide new data hubs in adhering to these principles. The study highlighted the importance of aligning health data management mechanisms with FAIR principles to enhance interoperability and reusability in the future HRIC.</p></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"157 ","pages":"Article 104700"},"PeriodicalIF":4.0,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1532046424001187/pdfft?md5=8528674c63bb931855f719c8a92b3d67&pid=1-s2.0-S1532046424001187-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141848605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of hypertension risk based on multiple feature fusion","authors":"Jingdong Yang ,&nbsp;Han Wang ,&nbsp;Peng Liu ,&nbsp;Yuhang Lu ,&nbsp;Minghui Yao ,&nbsp;Haixia Yan","doi":"10.1016/j.jbi.2024.104701","DOIUrl":"10.1016/j.jbi.2024.104701","url":null,"abstract":"<div><h3>Objective</h3><p>In the application of machine learning to the prediction of hypertension, many factors have seriously affected the classification accuracy and generalization performance. We propose a pulse wave classification model based on multi-feature fusion for accuracy prediction of hypertension.</p></div><div><h3>Methods and Materials</h3><p>We propose an ensemble under-sampling model with dynamic weights to decrease the influence of class imbalance on classification, further to automatically classify of hypertension on inquiry diagnosis. We also build a deep learning model based on hybrid attention mechanism, which transforms pulse waves to feature maps for extraction of in-depth features, so as to automatically classify hypertension on pulse diagnosis. We build the multi-feature fusion model based on dynamic Dempster/Shafer (DS) theory combining inquiry diagnosis and pulse diagnosis to enhance fault tolerance of prediction for multiple classifiers. In addition, this study calculates feature importance ranking of scale features on inquiry diagnosis and temporal and frequency-domain features on pulse diagnosis.</p></div><div><h3>Results</h3><p>The accuracy, sensitivity, specificity, F1-score and G-mean after 5-fold cross-validation were 94.08%, 93.43%, 96.86%, 93.45% and 95.12%, respectively, based on the hypertensive samples of 409 cases from Longhua Hospital affiliated to Shanghai University of Traditional Chinese Medicine and Hospital of Integrated Traditional Chinese and Western Medicine. We find the key factors influencing hypertensive classification accuracy, so as to assist in the prevention and clinical diagnosis of hypertension.</p></div><div><h3>Conclusion</h3><p>Compared with the state-of-the-art models, the multi-feature fusion model effectively utilizes the patients’ correlated multimodal features, and has higher classification accuracy and generalization performance.</p></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"157 ","pages":"Article 104701"},"PeriodicalIF":4.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141758975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}