Journal of Biomedical Informatics最新文献

{"title":"DOME: Directional medical embedding vectors from electronic health records.","authors":"Jun Wen, Hao Xue, Everett Rush, Vidul A Panickan, Tianrun Cai, Doudou Zhou, Yuk-Lam Ho, Lauren Costa, Edmon Begoli, Chuan Hong, J Michael Gaziano, Kelly Cho, Katherine P Liao, Junwei Lu, Tianxi Cai","doi":"10.1016/j.jbi.2024.104768","DOIUrl":"https://doi.org/10.1016/j.jbi.2024.104768","url":null,"abstract":"<p><strong>Motivation: </strong>The increasing availability of electronic health record (EHR) systems has created enormous potential for translational research. Recent developments in representation learning techniques have led to effective large-scale representations of EHR concepts along with knowledge graphs that empower downstream EHR studies. However, most existing methods require training with patient-level data, limiting their abilities to expand the training with multi-institutional EHR data. On the other hand, scalable approaches that only require summary-level data do not incorporate temporal dependencies between concepts.</p><p><strong>Methods: </strong>We introduce a DirectiOnal Medical Embedding (DOME) algorithm to encode temporally directional relationships between medical concepts, using summary-level EHR data. Specifically, DOME first aggregates patient-level EHR data into an asymmetric co-occurrence matrix. Then it computes two Positive Pointwise Mutual Information (PPMI) matrices to encode the pairwise prior/posterior dependencies respectively. Following that, a joint matrix factorization is performed on the two PPMI matrices, which results in three vectors for each concept: a semantic embedding and two directional context embeddings. They collectively provide a comprehensive depiction of the temporal relationship between EHR concepts.</p><p><strong>Results: </strong>We highlight the advantages and translational potential of DOME through three sets of validation studies. First, DOME consistently improves existing direction-agnostic embedding vectors for disease risk prediction in several diseases, for example in lung cancer, by 8.1% in the area under the receiver operating characteristic (AUROC). Second, DOME excels in directional drug-disease relationship inference by successfully differentiating between drug side effects and indications, achieving performance improvements over the state-of-the-art methods by 6.2% and 5.5% in AUROC, correspondingly. Finally, DOME effectively constructs directional knowledge graphs, which distinguish disease risk factors from comorbidities, thereby revealing disease progression trajectories. The source codes are provided at https://github.com/celehs/Directional-EHR-embedding.</p>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"104768"},"PeriodicalIF":4.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142926986","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 pipeline for harmonising NHS Scotland laboratory data to enable national-level analyses.","authors":"Chuang Gao, Shahzad Mumtaz, Sophie McCall, Katherine O'Sullivan, Mark McGilchrist, Daniel R Morales, Christopher Hall, Katie Wilde, Charlie Mayor, Pamela Linksted, Kathy Harrison, Christian Cole, Emily Jefferson","doi":"10.1016/j.jbi.2024.104771","DOIUrl":"https://doi.org/10.1016/j.jbi.2024.104771","url":null,"abstract":"<p><strong>Objective: </strong>Medical laboratory data together with prescribing and hospitalisation records are three of the most used electronic health records (EHRs) for data-driven health research. In Scotland, hospitalisation, prescribing and the death register data are available nationally whereas laboratory data is captured, stored and reported from local health board systems with significant heterogeneity. For researchers or other users of this regionally curated data, working on laboratory datasets across regional cohorts requires effort and time. As part of this study, the Scottish Safe Haven Network have developed an open-source software pipeline to generate a harmonised laboratory dataset.</p><p><strong>Methods: </strong>We obtained sample laboratory data from the four regional Safe Havens in Scotland covering people within the SHARE consented cohort. We compared the variables collected by each regional Safe Haven and mapped these to 11 FHIR and 2 Scottish-specific standardised terms (i.e., one to indicate the regional health board and a second to describe the source clinical code description) RESULTS: We compared the laboratory data and found that 180 test codes covered 98.7 % of test records performed across Scotland. Focusing on the 180 test codes, we developed a set of transformations to convert test results captured in different units to the same unit. We included both Read Codes and SNOMED CT to encode the tests within the pipeline.</p><p><strong>Conclusion: </strong>We validated our harmonisation pipeline by comparing the results across the different regional datasets. The pipeline can be reused by researchers and/or Safe Havens to generate clean, harmonised laboratory data at a national level with minimal effort.</p>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"104771"},"PeriodicalIF":4.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142926916","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":"Human intention recognition for trauma resuscitation: An interpretable deep learning approach for medical process data.","authors":"Keyi Li, Mary S Kim, Wenjin Zhang, Sen Yang, Genevieve J Sippel, Aleksandra Sarcevic, Randall S Burd, Ivan Marsic","doi":"10.1016/j.jbi.2024.104767","DOIUrl":"10.1016/j.jbi.2024.104767","url":null,"abstract":"<p><strong>Objective: </strong>Trauma resuscitation is the initial evaluation and management of injured patients in the emergency department. This time-critical process requires the simultaneous pursuit of multiple resuscitation goals. Recognizing whether the required goal is being pursued can reduce errors in goal-related task performance and improve patient outcomes. The intention to pursue a goal can often be inferred from ongoing and completed treatment activities, but monitoring goal pursuit is cognitively demanding and prone to errors. We introduced an interpretable deep learning-based approach to aid decision making by automatically recognizing goal pursuit during trauma resuscitation.</p><p><strong>Methods: </strong>We developed a predictive model to recognize the pursuit of two resuscitation goals: airway stabilization and circulatory support. We used event logs of 381 pediatric trauma resuscitations from August 2014 to November 2022 to train a neural network model with a dual-GRU structure that learns from both time-level and activity-type-level features. Our model makes predictions based on a sequence of activities and corresponding timestamps. To enhance the model and facilitate interpretation of predictions, we used the attention weights assigned by our model to represent the importance of features. These weights identified the critical time points and contributing activities during a goal pursuit.</p><p><strong>Results: </strong>Our model achieved an average area under the receiver operating characteristic curve (AUC) score of 0.84 for recognizing airway stabilization and 0.83 for recognizing circulatory support. The most contributing activities and timestamps were aligned with domain knowledge.</p><p><strong>Conclusion: </strong>Our interpretable predictive model can recognize provider intention based on a limited number of treatment activities. The model outperformed existing predictive models for medical events in accuracy and in interpretability. Integrating our model into a decision-support system would automate the tracking of provider actions, optimizing workflow to ensure timely delivery of care.</p>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"104767"},"PeriodicalIF":4.0,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142921206","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":"Visual-linguistic Diagnostic Semantic Enhancement for medical report generation.","authors":"Jiahong Chen, Guoheng Huang, Xiaochen Yuan, Guo Zhong, Zhe Tan, Chi-Man Pun, Qi Yang","doi":"10.1016/j.jbi.2024.104764","DOIUrl":"https://doi.org/10.1016/j.jbi.2024.104764","url":null,"abstract":"<p><p>Generative methods are currently popular for medical report generation, as they automatically generate professional reports from input images, assisting physicians in making faster and more accurate decisions. However, current methods face significant challenges: 1) Lesion areas in medical images are often difficult for models to capture accurately, and 2) even when captured, these areas are frequently not described using precise clinical diagnostic terms. To address these problems, we propose a Visual-Linguistic Diagnostic Semantic Enhancement model (VLDSE) to generate high-quality reports. Our approach employs supervised contrastive learning in the Image and Report Semantic Consistency (IRSC) module to bridge the semantic gap between visual and linguistic features. Additionally, we design the Visual Semantic Qualification and Quantification (VSQQ) module and the Post-hoc Semantic Correction (PSC) module to enhance visual semantics and inter-word relationships, respectively. Experiments demonstrate that our model achieves promising performance on the publicly available IU X-RAY and MIMIC-MV datasets. Specifically, on the IU X-RAY dataset, our model achieves a BLEU-4 score of 18.6%, improving the baseline by 12.7%. On the MIMIC-MV dataset, our model improves the BLEU-1 score by 10.7% over the baseline. These results demonstrate the ability of our model to generate accurate and fluent descriptions of lesion areas.</p>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"104764"},"PeriodicalIF":4.0,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142921316","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":"Unveiling pathology-related predictive uncertainty of glomerular lesion recognition using prototype learning.","authors":"Qiming He, Yingming Xu, Qiang Huang, Yanxia Wang, Jing Ye, Yonghong He, Jing Li, Lianghui Zhu, Zhe Wang, Tian Guan","doi":"10.1016/j.jbi.2024.104745","DOIUrl":"10.1016/j.jbi.2024.104745","url":null,"abstract":"<p><strong>Objective: </strong>Recognizing glomerular lesions is essential in diagnosing chronic kidney disease. However, deep learning faces challenges due to the lesion heterogeneity, superposition, progression, and tissue incompleteness, leading to uncertainty in model predictions. Therefore, it is crucial to analyze pathology-related predictive uncertainty in glomerular lesion recognition and unveil its relationship with pathological properties and its impact on model performance.</p><p><strong>Methods: </strong>This paper presents a novel framework for pathology-related predictive uncertainty analysis towards glomerular lesion recognition, including prototype learning based predictive uncertainty estimation, pathology-characterized correlation analysis and weight-redistributed prediction rectification. The prototype learning based predictive uncertainty estimation includes deep prototyping, affinity embedding, and multi-dimensional uncertainty fusion. The pathology-characterized correlation analysis is the first to use expert-based and learning- based approach to construct the pathology-related characterization of lesions and tissues. The weight-redistributed prediction rectification module performs reweighting- based lesion recognition.</p><p><strong>Results: </strong>To validate the performance, extensive experiments were conducted. Based on the Spearman and Pearson correlation analysis, the proposed framework enables more efficient correlation analysis, and strong correlation with pathology-related characterization can be achieved (c index > 0.6 and p < 0.01). Furthermore, the prediction rectification module demonstrated improved lesion recognition performance across most metrics, with enhancements of up to 6.36 %.</p><p><strong>Conclusion: </strong>The proposed predictive uncertainty analysis in glomerular lesion recognition offers a valuable approach for assessing computational pathology's predictive uncertainty from a pathology-related perspective.</p><p><strong>Significance: </strong>The paper provides a solution for pathology-related predictive uncertainty estimation in algorithm development and clinical practice.</p>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"104745"},"PeriodicalIF":4.0,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142921240","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":"Repeatable process for extracting health data from HL7 CDA documents.","authors":"Harry-Anton Talvik, Marek Oja, Sirli Tamm, Kerli Mooses, Dage Särg, Marcus Lõo, Õie Renata Siimon, Hendrik Šuvalov, Raivo Kolde, Jaak Vilo, Sulev Reisberg, Sven Laur","doi":"10.1016/j.jbi.2024.104765","DOIUrl":"https://doi.org/10.1016/j.jbi.2024.104765","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to address the gap in the literature on converting real-world Clinical Document Architecture (CDA) data into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), focusing on the initial steps preceding the mapping phase. We highlight the importance of a repeatable Extract-Transform-Load (ETL) pipeline for health data extraction from HL7 CDA documents in Estonia for research purposes.</p><p><strong>Methods: </strong>We developed a repeatable ETL pipeline to facilitate the extraction, cleaning, and restructuring of health data from CDA documents to OMOP CDM, ensuring a high-quality and structured data format. This pipeline was designed to adapt to continuously updated data exchange format changes and handle various CDA document subsets for different scientific studies.</p><p><strong>Results: </strong>We demonstrated via selected use cases that our pipeline successfully transformed a significant portion of diagnosis codes, body weight and eGFR measurements, and PAP test results from CDA documents into OMOP CDM, showing the ease of extracting structured data. However, challenges such as harmonising diverse coding systems and extracting lab results from free-text sections were encountered. The iterative development of the pipeline facilitated swift error detection and correction, enhancing the process's efficiency.</p><p><strong>Conclusion: </strong>After a decade of focused work, our research has led to the development of an ETL pipeline that effectively transforms HL7 CDA documents into OMOP CDM in Estonia, addressing key data extraction and transformation challenges. The pipeline's repeatability and adaptability to various data subsets make it a valuable resource for researchers dealing with health data. While tested on Estonian data, the principles outlined are broadly applicable, potentially aiding in handling health data standards that vary by country. Despite newer health data standards emerging, the relevance of CDA for retrospective health studies ensures the continuing importance of this work.</p>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"104765"},"PeriodicalIF":4.0,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142894645","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":"Modeling repeated measurements data using the multilevel Bayesian network: A case of child morbidity.","authors":"Bezalem Eshetu Yirdaw, Legesse Kassa Debusho","doi":"10.1016/j.jbi.2024.104760","DOIUrl":"10.1016/j.jbi.2024.104760","url":null,"abstract":"<p><strong>Background and objective: </strong>In epidemiological research, studying the long-term dependencies between multiple diseases is important. This study extends the multilevel Bayesian network (MBN) for repeated measures data that can estimate the rate of change in outcomes over time while quantifying the variabilities of these rates across higher-level units through various variance-covariance structures.</p><p><strong>Method: </strong>The performance and reliability of a model are examined through a simulation study, and its practical application is demonstrated using child morbidity data. This data has a hierarchical structure in which children were randomly selected from clusters (villages) and their conditions were assessed quarterly from March 2015 to May 2016. MBN was used to explore the relationship between outcomes weight-for-age (WAZ), height-for-age (HAZ), the number of days a child suffers from diarrhea (NOD), and flu (NOF), and estimate the rate of change of these outcomes over time. Since the outcomes considered were hybrid in nature, the connected three-parent set block Gibbs sampler with a multilevel generalized Poisson regression, multilevel zero inflated Poisson regression, and linear mixed-effects models were considered during the structure and parametric learning of the MBN.</p><p><strong>Result: </strong>The simulation study confirmed that a MBN using the time metric t as a node performed well for repeated measures data. The result from the structure learning of MBN shows a causal relationship between WAZ, HAZ, NOD and NOF. Furthermore, exclusive breastfeeding months and usage of micronutrient powder appeared as a strong predictor for all outcomes considered in this study.</p><p><strong>Conclusion: </strong>This study reveals that MBN is suitable in modeling repeated measures data to study the relationship between outcomes and estimate rate of change of an outcome over time while quantifying the variability due to higher-level clustering variables. Furthermore, the study highlights the importance of focusing on monitoring children with low WAZ and HAZ scores together with good feeding practices against the frequency of getting flu and diarrhea.</p>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"104760"},"PeriodicalIF":4.0,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142894631","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":"Reviewer acknowledgment.","authors":"","doi":"10.1016/j.jbi.2024.104763","DOIUrl":"https://doi.org/10.1016/j.jbi.2024.104763","url":null,"abstract":"","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"104763"},"PeriodicalIF":4.0,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142882210","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":"Novel machine learning model for predicting cancer drugs' susceptibilities and discovering novel treatments.","authors":"Xiaowen Cao, Li Xing, Hao Ding, He Li, Yushan Hu, Yao Dong, Hua He, Junhua Gu, Xuekui Zhang","doi":"10.1016/j.jbi.2024.104762","DOIUrl":"10.1016/j.jbi.2024.104762","url":null,"abstract":"<p><strong>Background and objective: </strong>Timely treatment is crucial for cancer patients, so it's important to administer the appropriate treatment as soon as possible. Because individuals can respond differently to a given drug due to their unique genomic profiles, we aim to use their genomic information to predict how various drugs will affect them and determine the best course of treatment.</p><p><strong>Methods: </strong>We present Kernelized Residual Stacking (KRS), a new multi-task learning approach, and use it to predict the responses to anti-cancer drugs based on genomic data. We demonstrate the superior predictive performance of KRS, outperforming popular competitors, by utilizing the Genomics of Drug Sensitivity in Cancer (GDSC) study and the Cancer Cell Line Encyclopedia (CCLE) study. Downstream analysis of feature genes selected by KRS is conducted to discover novel therapies.</p><p><strong>Results: </strong>We used two genomic studies to show that KRS outperforms a few popular competitors in predicting drugs' susceptibilities. Through downstream analysis of feature genes selected by KRS, we found that the PI3K-Akt pathway could alter drugs' susceptibilities, and its expression correlated positively with the hub gene ERBB2. We discovered eight novel small molecules based on these feature genes, which could be developed into novel combination therapies with anti-cancer drugs.</p><p><strong>Conclusions: </strong>KRS outperforms competitors in prediction performance and selects feature genes highly correlated with drugs' susceptibilities. Novel biological results are found by investigating KRS's feature genes.</p>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"104762"},"PeriodicalIF":4.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142824159","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":"Efficient strabismus diagnosis from small samples: Harnessing spatial features for improved accuracy.","authors":"Renzhong Wu, Shenghui Liao, Yongrong Ji, Xiaoyan Kui, Fuchang Han, Ziyang Hu, Xuefei Song","doi":"10.1016/j.jbi.2024.104759","DOIUrl":"10.1016/j.jbi.2024.104759","url":null,"abstract":"<p><p>Strabismus is a common ophthalmological condition, and early diagnosis is crucial to preventing visual impairment and loss of stereopsis. However, traditional methods for diagnosing strabismus often rely on specialized ophthalmic equipment and trained personnel, limiting the widespread accessibility of strabismus diagnosis. Computer-aided strabismus diagnosis is an effective and widely used technology that assists clinicians in making clinical diagnoses and improving efficiency. To address this, we designed an efficient strabismus diagnosis model, RIS-MLP, based on a small number of samples derived from frontal facial images captured under natural lighting conditions via the Hirschberg test. The RIS-MLP combines light reflex point detection and iris detection modules to accurately extract key spatial features even under noisy and occluded conditions. The optimized spatial feature strategies further enhances the performance of the classification module. To validate the superiority of RIS-MLP, we conducted both direct and indirect comparative experiments. Indirect comparisons demonstrate that the RIS-MLP has advantages in terms of sample efficiency. While direct comparisons show that the RIS-MLP can mitigate overfitting to a certain extent, and the RIS-MLP along with its variants (e.g., RIS-SVM) have outperformed state-of-the-art models on our noisy and imbalanced dataset.</p>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":" ","pages":"104759"},"PeriodicalIF":4.0,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142818178","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}