Journal of the American Medical Informatics Association最新文献

{"title":"Correction to: Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review.","authors":"","doi":"10.1093/jamia/ocae283","DOIUrl":"10.1093/jamia/ocae283","url":null,"abstract":"","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"260"},"PeriodicalIF":4.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11648702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142583537","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":"Is ChatGPT worthy enough for provisioning clinical decision support?","authors":"Partha Pratim Ray","doi":"10.1093/jamia/ocae282","DOIUrl":"10.1093/jamia/ocae282","url":null,"abstract":"","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"258-259"},"PeriodicalIF":4.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11648701/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142583648","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":"Machine learning-based infection diagnostic and prognostic models in post-acute care settings: a systematic review.","authors":"Zidu Xu, Danielle Scharp, Mollie Hobensack, Jiancheng Ye, Jungang Zou, Sirui Ding, Jingjing Shang, Maxim Topaz","doi":"10.1093/jamia/ocae278","DOIUrl":"10.1093/jamia/ocae278","url":null,"abstract":"<p><strong>Objectives: </strong>This study aims to (1) review machine learning (ML)-based models for early infection diagnostic and prognosis prediction in post-acute care (PAC) settings, (2) identify key risk predictors influencing infection-related outcomes, and (3) examine the quality and limitations of these models.</p><p><strong>Materials and methods: </strong>PubMed, Web of Science, Scopus, IEEE Xplore, CINAHL, and ACM digital library were searched in February 2024. Eligible studies leveraged PAC data to develop and evaluate ML models for infection-related risks. Data extraction followed the CHARMS checklist. Quality appraisal followed the PROBAST tool. Data synthesis was guided by the socio-ecological conceptual framework.</p><p><strong>Results: </strong>Thirteen studies were included, mainly focusing on respiratory infections and nursing homes. Most used regression models with structured electronic health record data. Since 2020, there has been a shift toward advanced ML algorithms and multimodal data, biosensors, and clinical notes being significant sources of unstructured data. Despite these advances, there is insufficient evidence to support performance improvements over traditional models. Individual-level risk predictors, like impaired cognition, declined function, and tachycardia, were commonly used, while contextual-level predictors were barely utilized, consequently limiting model fairness. Major sources of bias included lack of external validation, inadequate model calibration, and insufficient consideration of data complexity.</p><p><strong>Discussion and conclusion: </strong>Despite the growth of advanced modeling approaches in infection-related models in PAC settings, evidence supporting their superiority remains limited. Future research should leverage a socio-ecological lens for predictor selection and model construction, exploring optimal data modalities and ML model usage in PAC, while ensuring rigorous methodologies and fairness considerations.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"241-252"},"PeriodicalIF":4.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11648729/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631465","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":"A dataset and benchmark for hospital course summarization with adapted large language models.","authors":"Asad Aali, Dave Van Veen, Yamin Ishraq Arefeen, Jason Hom, Christian Bluethgen, Eduardo Pontes Reis, Sergios Gatidis, Namuun Clifford, Joseph Daws, Arash S Tehrani, Jangwon Kim, Akshay S Chaudhari","doi":"10.1093/jamia/ocae312","DOIUrl":"https://doi.org/10.1093/jamia/ocae312","url":null,"abstract":"<p><strong>Objective: </strong>Brief hospital course (BHC) summaries are clinical documents that summarize a patient's hospital stay. While large language models (LLMs) depict remarkable capabilities in automating real-world tasks, their capabilities for healthcare applications such as synthesizing BHCs from clinical notes have not been shown. We introduce a novel preprocessed dataset, the MIMIC-IV-BHC, encapsulating clinical note and BHC pairs to adapt LLMs for BHC synthesis. Furthermore, we introduce a benchmark of the summarization performance of 2 general-purpose LLMs and 3 healthcare-adapted LLMs.</p><p><strong>Materials and methods: </strong>Using clinical notes as input, we apply prompting-based (using in-context learning) and fine-tuning-based adaptation strategies to 3 open-source LLMs (Clinical-T5-Large, Llama2-13B, and FLAN-UL2) and 2 proprietary LLMs (Generative Pre-trained Transformer [GPT]-3.5 and GPT-4). We evaluate these LLMs across multiple context-length inputs using natural language similarity metrics. We further conduct a clinical study with 5 clinicians, comparing clinician-written and LLM-generated BHCs across 30 samples, focusing on their potential to enhance clinical decision-making through improved summary quality. We compare reader preferences for the original and LLM-generated summary using Wilcoxon signed-rank tests. We further request optional qualitative feedback from clinicians to gain deeper insights into their preferences, and we present the frequency of common themes arising from these comments.</p><p><strong>Results: </strong>The Llama2-13B fine-tuned LLM outperforms other domain-adapted models given quantitative evaluation metrics of Bilingual Evaluation Understudy (BLEU) and Bidirectional Encoder Representations from Transformers (BERT)-Score. GPT-4 with in-context learning shows more robustness to increasing context lengths of clinical note inputs than fine-tuned Llama2-13B. Despite comparable quantitative metrics, the reader study depicts a significant preference for summaries generated by GPT-4 with in-context learning compared to both Llama2-13B fine-tuned summaries and the original summaries (P<.001), highlighting the need for qualitative clinical evaluation.</p><p><strong>Discussion and conclusion: </strong>We release a foundational clinically relevant dataset, the MIMIC-IV-BHC, and present an open-source benchmark of LLM performance in BHC synthesis from clinical notes. We observe high-quality summarization performance for both in-context proprietary and fine-tuned open-source LLMs using both quantitative metrics and a qualitative clinical reader study. Our research effectively integrates elements from the data assimilation pipeline: our methods use (1) clinical data sources to integrate, (2) data translation, and (3) knowledge creation, while our evaluation strategy paves the way for (4) deployment.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142957970","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 novel generative multi-task representation learning approach for predicting postoperative complications in cardiac surgery patients.","authors":"Junbo Shen, Bing Xue, Thomas Kannampallil, Chenyang Lu, Joanna Abraham","doi":"10.1093/jamia/ocae316","DOIUrl":"https://doi.org/10.1093/jamia/ocae316","url":null,"abstract":"<p><strong>Objective: </strong>Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the effectiveness of predicting postoperative complications using a novel surgical Variational Autoencoder (surgVAE) that uncovers intrinsic patterns via cross-task and cross-cohort presentation learning.</p><p><strong>Materials and methods: </strong>This retrospective cohort study used data from the electronic health records of adult surgical patients over 4 years (2018-2021). Six key postoperative complications for cardiac surgery were assessed: acute kidney injury, atrial fibrillation, cardiac arrest, deep vein thrombosis or pulmonary embolism, blood transfusion, and other intraoperative cardiac events. We compared surgVAE's prediction performance against widely-used ML models and advanced representation learning and generative models under 5-fold cross-validation.</p><p><strong>Results: </strong>89 246 surgeries (49% male, median [IQR] age: 57 [45-69]) were included, with 6502 in the targeted cardiac surgery cohort (61% male, median [IQR] age: 60 [53-70]). surgVAE demonstrated generally superior performance over existing ML solutions across postoperative complications of cardiac surgery patients, achieving macro-averaged AUPRC of 0.409 and macro-averaged AUROC of 0.831, which were 3.4% and 3.7% higher, respectively, than the best alternative method (by AUPRC scores). Model interpretation using Integrated Gradients highlighted key risk factors based on preoperative variable importance.</p><p><strong>Discussion and conclusion: </strong>Our advanced representation learning framework surgVAE showed excellent discriminatory performance for predicting postoperative complications and addressing the challenges of data complexity, small cohort sizes, and low-frequency positive events. surgVAE enables data-driven predictions of patient risks and prognosis while enhancing the interpretability of patient risk profiles.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899994","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":"Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction.","authors":"Xiayuan Huang, Jatin Arora, Abdullah Mesut Erzurumluoglu, Stephen A Stanhope, Daniel Lam, Hongyu Zhao, Zhihao Ding, Zuoheng Wang, Johann de Jong","doi":"10.1093/jamia/ocae297","DOIUrl":"https://doi.org/10.1093/jamia/ocae297","url":null,"abstract":"<p><strong>Background: </strong>Machine learning and deep learning are powerful tools for analyzing electronic health records (EHRs) in healthcare research. Although family health history has been recognized as a major predictor for a wide spectrum of diseases, research has so far adopted a limited view of family relations, essentially treating patients as independent samples in the analysis.</p><p><strong>Methods: </strong>To address this gap, we present ALIGATEHR, which models inferred family relations in a graph attention network augmented with an attention-based medical ontology representation, thus accounting for the complex influence of genetics, shared environmental exposures, and disease dependencies.</p><p><strong>Results: </strong>Taking disease risk prediction as a use case, we demonstrate that explicitly modeling family relations significantly improves predictions across the disease spectrum. We then show how ALIGATEHR's attention mechanism, which links patients' disease risk to their relatives' clinical profiles, successfully captures genetic aspects of diseases using longitudinal EHR diagnosis data. Finally, we use ALIGATEHR to successfully distinguish the 2 main inflammatory bowel disease subtypes with highly shared risk factors and symptoms (Crohn's disease and ulcerative colitis).</p><p><strong>Conclusion: </strong>Overall, our results highlight that family relations should not be overlooked in EHR research and illustrate ALIGATEHR's great potential for enhancing patient representation learning for predictive and interpretable modeling of EHRs.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142900000","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":"Analysis of eligibility criteria clusters based on large language models for clinical trial design.","authors":"Alban Bornet, Philipp Khlebnikov, Florian Meer, Quentin Haas, Anthony Yazdani, Boya Zhang, Poorya Amini, Douglas Teodoro","doi":"10.1093/jamia/ocae311","DOIUrl":"https://doi.org/10.1093/jamia/ocae311","url":null,"abstract":"<p><strong>Objectives: </strong>Clinical trials (CTs) are essential for improving patient care by evaluating new treatments' safety and efficacy. A key component in CT protocols is the study population defined by the eligibility criteria. This study aims to evaluate the effectiveness of large language models (LLMs) in encoding eligibility criterion information to support CT-protocol design.</p><p><strong>Materials and methods: </strong>We extracted eligibility criterion sections, phases, conditions, and interventions from CT protocols available in the ClinicalTrials.gov registry. Eligibility sections were split into individual rules using a criterion tokenizer and embedded using LLMs. The obtained representations were clustered. The quality and relevance of the clusters for protocol design was evaluated through 3 experiments: intrinsic alignment with protocol information and human expert cluster coherence assessment, extrinsic evaluation through CT-level classification tasks, and eligibility section generation.</p><p><strong>Results: </strong>Sentence embeddings fine-tuned using biomedical corpora produce clusters with the highest alignment to CT-level information. Human expert evaluation confirms that clusters are well structured and coherent. Despite the high information compression, clusters retain significant CT information, up to 97% of the classification performance obtained with raw embeddings. Finally, eligibility sections automatically generated using clusters achieve 95% of the ROUGE scores obtained with a generative LLM prompted with CT-protocol details, suggesting that clusters encapsulate information useful to CT-protocol design.</p><p><strong>Discussion: </strong>Clusters derived from sentence-level LLM embeddings effectively summarize complex eligibility criterion data while retaining relevant CT-protocol details. Clustering-based approaches provide a scalable enhancement in CT design that balances information compression with accuracy.</p><p><strong>Conclusions: </strong>Clustering eligibility criteria using LLM embeddings provides a practical and efficient method to summarize critical protocol information. We provide an interactive visualization of the pipeline here.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899996","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":"Descriptive epidemiology demonstrating the All of Us database as a versatile resource for the rare and undiagnosed disease community.","authors":"Drenen J Magee, Sierra Kicker, Aeisha Thomas","doi":"10.1093/jamia/ocae241","DOIUrl":"https://doi.org/10.1093/jamia/ocae241","url":null,"abstract":"<p><strong>Objective: </strong>We aim to demonstrate the versatility of the All of Us database as an important source of rare and undiagnosed disease (RUD) data, because of its large size and range of data types.</p><p><strong>Materials and methods: </strong>We searched the public data browser, electronic health record (EHR), and several surveys to investigate the prevalence, mental health, healthcare access, and other data of select RUDs.</p><p><strong>Results: </strong>Several RUDs have participants in All of Us [eg, 75 of 100 rare infectious diseases (RIDs)]. We generated health-related data for undiagnosed, sickle cell disease (SCD), cystic fibrosis (CF), and infectious (2 diseases) and chronic (4 diseases) disease pools.</p><p><strong>Conclusion: </strong>Our results highlight the potential value of All of Us with both data breadth and depth to help identify possible solutions for shared and disease-specific biomedical and other problems such as healthcare access, thus enhancing diagnosis, treatment, prevention, and support for the RUD community.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142883641","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":"An electronic health record metadata-mining approach to identifying patient-level interprofessional clinician teams in the intensive care unit.","authors":"Olga Yakusheva, Lara Khadr, Kathryn A Lee, Hannah C Ratliff, Deanna J Marriott, Deena Kelly Costa","doi":"10.1093/jamia/ocae275","DOIUrl":"https://doi.org/10.1093/jamia/ocae275","url":null,"abstract":"<p><strong>Objectives: </strong>Advances in health informatics rapidly expanded use of big-data analytics and electronic health records (EHR) by clinical researchers seeking to optimize interprofessional ICU team care. This study developed and validated a program for extracting interprofessional teams assigned to each patient each shift from EHR event logs.</p><p><strong>Materials and methods: </strong>A retrospective analysis of EHR event logs for mechanically-ventilated patients 18 and older from 5 ICUs in an academic medical center during 1/1/2018-12/31/2019. We defined interprofessional teams as all medical providers (physicians, physician assistants, and nurse practitioners), registered nurses, and respiratory therapists assigned to each patient each shift. We created an EHR event logs-mining program that extracts clinicians who interact with each patient's medical record each shift. The algorithm was validated using the Message Understanding Conference (MUC-6) method against manual chart review of a random sample of 200 patient-shifts from each ICU by two independent reviewers.</p><p><strong>Results: </strong>Our sample included 4559 ICU encounters and 72 846 patient-shifts. Our program extracted 3288 medical providers, 2702 registered nurses, and 219 respiratory therapists linked to these encounters. Eighty-three percent of patient-shift teams included medical providers, 99.3% included registered nurses, and 74.1% included respiratory therapists; 63.4% of shift-level teams included clinicians from all three professions. The program demonstrated 95.9% precision, 96.2% recall, and high face validity.</p><p><strong>Discussion: </strong>Our EHR event logs-mining program has high precision, recall, and validity for identifying patient-levelshift interprofessional teams in ICUs.</p><p><strong>Conclusions: </strong>Algorithmic and artificial intelligence approaches have a strong potential for informing research to optimize patient team assignments and improve ICU care and outcomes.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142839957","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":"Variations in digital health literacy for pediatric caregivers of hospitalized children: implications for digital health equity.","authors":"Steven Crook, Glenn Rosenbluth, David V Glidden, Alicia Fernandez, Chuan-Mei Lee, Lizette Avina, Leslie Magana, Kiana Washington, Naomi S Bardach","doi":"10.1093/jamia/ocae305","DOIUrl":"10.1093/jamia/ocae305","url":null,"abstract":"<p><strong>Objectives: </strong>We sought to assess whether race, ethnicity, and preferred language were associated with digital health literacy in pediatric caregivers.</p><p><strong>Materials and methods: </strong>We used linear regression to measure associations between 3 eHealth Literacy Questionnaire (eHLQ) domains (score range: 1-4) and demographic characteristics.</p><p><strong>Results: </strong>Non-Latinx White respondents (n = 230) had highest adjusted mean eHLQ scores: 3.44 (95% confidence interval: 3.36-3.52) in \"Ability to engage,\" 3.39 (3.31 to 3.47) in \"Feel safe and in control,\" and 3.34 (3.25 to 3.41) in \"Motivated.\" By contrast, Spanish-preferring Latinx respondents (n = 246) had lower adjusted mean scores across all 3 eHLQ domains: 2.97 (P < .0001), 3.21 (P = .004), and 3.19 (P = .033), respectively.</p><p><strong>Discussion: </strong>Our study contributes insights in variations across ethnoracial and language preference groups by different eHLQ domains, with implications for addressing digital health inequities.</p><p><strong>Conclusion: </strong>Digital health literacy was lower in Spanish-preferring Latinx pediatric caregivers compared to non-Latinx White caregivers across 3 eHLQ domains. It was lower than English-preferring Latinx caregivers in \"Ability.\"</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142839964","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}