Journal of the American Medical Informatics Association最新文献

{"title":"Reliable generation of privacy-preserving synthetic electronic health record time series via diffusion models.","authors":"Muhang Tian, Bernie Chen, Allan Guo, Shiyi Jiang, Anru R Zhang","doi":"10.1093/jamia/ocae229","DOIUrl":"https://doi.org/10.1093/jamia/ocae229","url":null,"abstract":"<p><strong>Objective: </strong>Electronic health records (EHRs) are rich sources of patient-level data, offering valuable resources for medical data analysis. However, privacy concerns often restrict access to EHRs, hindering downstream analysis. Current EHR deidentification methods are flawed and can lead to potential privacy leakage. Additionally, existing publicly available EHR databases are limited, preventing the advancement of medical research using EHR. This study aims to overcome these challenges by generating realistic and privacy-preserving synthetic EHRs time series efficiently.</p><p><strong>Materials and methods: </strong>We introduce a new method for generating diverse and realistic synthetic EHR time series data using denoizing diffusion probabilistic models. We conducted experiments on 6 databases: Medical Information Mart for Intensive Care III and IV, the eICU Collaborative Research Database (eICU), and non-EHR datasets on Stocks and Energy. We compared our proposed method with 8 existing methods.</p><p><strong>Results: </strong>Our results demonstrate that our approach significantly outperforms all existing methods in terms of data fidelity while requiring less training effort. Additionally, data generated by our method yield a lower discriminative accuracy compared to other baseline methods, indicating the proposed method can generate data with less privacy risk.</p><p><strong>Discussion: </strong>The proposed model utilizes a mixed diffusion process to generate realistic synthetic EHR samples that protect patient privacy. This method could be useful in tackling data availability issues in the field of healthcare by reducing barrier to EHR access and supporting research in machine learning for health.</p><p><strong>Conclusion: </strong>The proposed diffusion model-based method can reliably and efficiently generate synthetic EHR time series, which facilitates the downstream medical data analysis. Our numerical results show the superiority of the proposed method over all other existing methods.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142121001","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":"Socioeconomic disparities in kidney transplant access for patients with end-stage kidney disease within the All of Us Research Program.","authors":"Jiayuan Wang, Kellie C Cho, Ekamol Tantisattamo","doi":"10.1093/jamia/ocae178","DOIUrl":"https://doi.org/10.1093/jamia/ocae178","url":null,"abstract":"<p><strong>Objectives: </strong>Disparity in kidney transplant access has been demonstrated by a disproportionately low rate of kidney transplantation in socioeconomically disadvantaged patients. However, the information is not from national representative populations with end-stage kidney disease (ESKD). We aim to examine whether socioeconomic disparity for kidney transplant access exists by utilizing data from the All of Us Research Program.</p><p><strong>Materials and methods: </strong>We analyzed data of adult ESKD patients using the All of Us Researcher Workbench. The association of socioeconomic data including types of health insurance, levels of education, and household incomes with kidney transplant access was evaluated by multivariable logistic regression analysis adjusted by baseline demographic, medical comorbidities, and behavioral information.</p><p><strong>Results: </strong>Among 4078 adults with ESKD, mean diagnosis age was 54 and 51.64% were male. The majority had Medicare (39.6%), were non-graduate college (75.79%), and earned $10 000-24 999 annual income (20.16%). After adjusting for potential confounders, insurance status emerged as a significant predictor of kidney transplant access. Individuals covered by Medicaid (adjusted odds ratio [AOR] 0.45; 95% confidence interval [CI], 0.35-0.58; P-value < .001) or uninsured (AOR 0.21; 95% CI, 0.12-0.37; P-value < .001) exhibited lower odds of transplantation compared to those with private insurance.</p><p><strong>Discussion/conclusion: </strong>Our findings reveal the influence of insurance status and socioeconomic factors on access to kidney transplantation among ESKD patients. Addressing these disparities through expanded insurance coverage and improved healthcare access is vital for promoting equitable treatment and enhancing health outcomes in vulnerable populations.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142121002","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":"Large language models in biomedicine and health: current research landscape and future directions.","authors":"Zhiyong Lu, Yifan Peng, Trevor Cohen, Marzyeh Ghassemi, Chunhua Weng, Shubo Tian","doi":"10.1093/jamia/ocae202","DOIUrl":"10.1093/jamia/ocae202","url":null,"abstract":"","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11339542/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142019383","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":"Relation extraction using large language models: a case study on acupuncture point locations.","authors":"Yiming Li, Xueqing Peng, Jianfu Li, Xu Zuo, Suyuan Peng, Donghong Pei, Cui Tao, Hua Xu, Na Hong","doi":"10.1093/jamia/ocae233","DOIUrl":"https://doi.org/10.1093/jamia/ocae233","url":null,"abstract":"<p><strong>Objective: </strong>In acupuncture therapy, the accurate location of acupoints is essential for its effectiveness. The advanced language understanding capabilities of large language models (LLMs) like Generative Pre-trained Transformers (GPTs) and Llama present a significant opportunity for extracting relations related to acupoint locations from textual knowledge sources. This study aims to explore the performance of LLMs in extracting acupoint-related location relations and assess the impact of fine-tuning on GPT's performance.</p><p><strong>Materials and methods: </strong>We utilized the World Health Organization Standard Acupuncture Point Locations in the Western Pacific Region (WHO Standard) as our corpus, which consists of descriptions of 361 acupoints. Five types of relations (\"direction_of\", \"distance_of\", \"part_of\", \"near_acupoint\", and \"located_near\") (n = 3174) between acupoints were annotated. Four models were compared: pre-trained GPT-3.5, fine-tuned GPT-3.5, pre-trained GPT-4, as well as pretrained Llama 3. Performance metrics included micro-average exact match precision, recall, and F1 scores.</p><p><strong>Results: </strong>Our results demonstrate that fine-tuned GPT-3.5 consistently outperformed other models in F1 scores across all relation types. Overall, it achieved the highest micro-average F1 score of 0.92.</p><p><strong>Discussion: </strong>The superior performance of the fine-tuned GPT-3.5 model, as shown by its F1 scores, underscores the importance of domain-specific fine-tuning in enhancing relation extraction capabilities for acupuncture-related tasks. In light of the findings from this study, it offers valuable insights into leveraging LLMs for developing clinical decision support and creating educational modules in acupuncture.</p><p><strong>Conclusion: </strong>This study underscores the effectiveness of LLMs like GPT and Llama in extracting relations related to acupoint locations, with implications for accurately modeling acupuncture knowledge and promoting standard implementation in acupuncture training and practice. The findings also contribute to advancing informatics applications in traditional and complementary medicine, showcasing the potential of LLMs in natural language processing.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142114242","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 review of reinforcement learning for natural language processing and applications in healthcare.","authors":"Ying Liu, Haozhu Wang, Huixue Zhou, Mingchen Li, Yu Hou, Sicheng Zhou, Fang Wang, Rama Hoetzlein, Rui Zhang","doi":"10.1093/jamia/ocae215","DOIUrl":"https://doi.org/10.1093/jamia/ocae215","url":null,"abstract":"<p><strong>Importance: </strong>Reinforcement learning (RL) represents a pivotal avenue within natural language processing (NLP), offering a potent mechanism for acquiring optimal strategies in task completion. This literature review studies various NLP applications where RL has demonstrated efficacy, with notable applications in healthcare settings.</p><p><strong>Objectives: </strong>To systematically explore the applications of RL in NLP, focusing on its effectiveness in acquiring optimal strategies, particularly in healthcare settings, and provide a comprehensive understanding of RL's potential in NLP tasks.</p><p><strong>Materials and methods: </strong>Adhering to the PRISMA guidelines, an exhaustive literature review was conducted to identify instances where RL has exhibited success in NLP applications, encompassing dialogue systems, machine translation, question-answering, text summarization, and information extraction. Our methodological approach involves closely examining the technical aspects of RL methodologies employed in these applications, analyzing algorithms, states, rewards, actions, datasets, and encoder-decoder architectures.</p><p><strong>Results: </strong>The review of 93 papers yields insights into RL algorithms, prevalent techniques, emergent trends, and the fusion of RL methods in NLP healthcare applications. It clarifies the strategic approaches employed, datasets utilized, and the dynamic terrain of RL-NLP systems, thereby offering a roadmap for research and development in RL and machine learning techniques in healthcare. The review also addresses ethical concerns to ensure equity, transparency, and accountability in the evolution and application of RL-based NLP technologies, particularly within sensitive domains such as healthcare.</p><p><strong>Discussion: </strong>The findings underscore the promising role of RL in advancing NLP applications, particularly in healthcare, where its potential to optimize decision-making and enhance patient outcomes is significant. However, the ethical challenges and technical complexities associated with RL demand careful consideration and ongoing research to ensure responsible and effective implementation.</p><p><strong>Conclusions: </strong>By systematically exploring RL's applications in NLP and providing insights into technical analysis, ethical implications, and potential advancements, this review contributes to a deeper understanding of RL's role for language processing.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142114241","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":"User guide for Social Determinants of Health Survey data in the All of Us Research Program.","authors":"Theresa A Koleck, Caitlin Dreisbach, Chen Zhang, Susan Grayson, Maichou Lor, Zhirui Deng, Alex Conway, Peter D R Higgins, Suzanne Bakken","doi":"10.1093/jamia/ocae214","DOIUrl":"https://doi.org/10.1093/jamia/ocae214","url":null,"abstract":"<p><strong>Objectives: </strong>Integration of social determinants of health into health outcomes research will allow researchers to study health inequities. The All of Us Research Program has the potential to be a rich source of social determinants of health data. However, user-friendly recommendations for scoring and interpreting the All of Us Social Determinants of Health Survey are needed to return value to communities through advancing researcher competencies in use of the All of Us Research Hub Researcher Workbench. We created a user guide aimed at providing researchers with an overview of the Social Determinants of Health Survey, recommendations for scoring and interpreting participant responses, and readily executable R and Python functions.</p><p><strong>Target audience: </strong>This user guide targets registered users of the All of Us Research Hub Researcher Workbench, a cloud-based platform that supports analysis of All of Us data, who are currently conducting or planning to conduct analyses using the Social Determinants of Health Survey.</p><p><strong>Scope: </strong>We introduce 14 constructs evaluated as part of the Social Determinants of Health Survey and summarize construct operationalization. We offer 30 literature-informed recommendations for scoring participant responses and interpreting scores, with multiple options available for 8 of the constructs. Then, we walk through example R and Python functions for relabeling responses and scoring constructs that can be directly implemented in Jupyter Notebook or RStudio within the Researcher Workbench. Full source code is available in supplemental files and GitHub. Finally, we discuss psychometric considerations related to the Social Determinants of Health Survey for researchers.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142082352","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 GIS software-based method to identify public health data belonging to address-defined communities.","authors":"Amanda M Lam, Mariana C Singletary, Theresa Cullen","doi":"10.1093/jamia/ocae235","DOIUrl":"https://doi.org/10.1093/jamia/ocae235","url":null,"abstract":"<p><strong>Objective: </strong>This communication presents the results of defining a tribal health jurisdiction by a combination of tribal affiliation and case address.</p><p><strong>Methods: </strong>Through a county-tribal partnership, GIS software and custom code were used to extract tribal data from county data by identifying reservation addresses in county extracts of COVID-19 case records from December 30, 2019, to December 31, 2022 (n = 374,653) and COVID-19 vaccination records from December 1, 2020, to April 18, 2023 (n = 2,355,058).</p><p><strong>Results: </strong>The tool identified 1.91 times as many case records and 3.76 times as many vaccination records as filtering by tribal affiliation alone.</p><p><strong>Discussion and conclusion: </strong>This method of identifying communities by patient address, in combination with tribal affiliation and enrollment, can help tribal health jurisdictions attain equitable access to public health data, when done in partnership with a data sharing agreement. This methodology has potential applications for other populations underrepresented in public health and clinical research.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142057033","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":"BioASQ Synergy: A Dialogue between QA systems and biomedical experts for promoting COVID-19 research.","authors":"Anastasia Krithara, Anastasios Nentidis, Eirini Vandorou, Georgios Katsimpras, Yannis Almirantis, Magda Arnal, Adomas Bunevicius, Eulalia Farre-Maduell, Maya Kassiss, Vasileios Konstantakos, Sherri Matis-Mitchell, Dimitris Polychronopoulos, Jesus Rodriguez-Pascual, Eleftherios G Samaras, Martina Samiotaki, Despina Sanoudou, Aspasia Vozi, Georgios Paliouras","doi":"10.1093/jamia/ocae232","DOIUrl":"https://doi.org/10.1093/jamia/ocae232","url":null,"abstract":"<p><strong>Objective: </strong>This paper presents the novel BioASQ Synergy research process which aims to facilitate the interaction between biomedical experts and automated question answering systems.</p><p><strong>Materials and methods: </strong>The proposed research allows systems to provide answers to emerging questions, which in turn are assessed by experts. The assessment of the experts is fed back to the systems, together with new questions. With this iteration, we aim to facilitate the incremental understanding of a developing problem and contribute to solution discovery.</p><p><strong>Results: </strong>The results suggest that the proposed approach can assist researchers to navigate available resources. The experts seem to be very satisfied with the quality of the ideal answers provided by the systems, suggesting that such systems are already useful in answering open research questions.</p><p><strong>Discussion: </strong>BioASQ Synergy aspire to provide a tool that gives the experts easy and personalised access to the latest findings in a fast growing corpus of material.</p><p><strong>Conclusion: </strong>In this paper we envisioned BioASQ Synergy as a continuous dialogue between experts and systems to issue open questions. We ran an initial proof-of-concept of the approach, in order to evaluate its usefulness, both from the side of the experts, as well as from the side of the participating systems.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142047392","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":"Characterizing apparent treatment resistant hypertension in the United States: insights from the All of Us Research Program.","authors":"Mona Alshahawey, Eissa Jafari, Steven M Smith, Caitrin W McDonough","doi":"10.1093/jamia/ocae227","DOIUrl":"https://doi.org/10.1093/jamia/ocae227","url":null,"abstract":"<p><strong>Background: </strong>Hypertension (HTN) remains a significant public health concern and the primary modifiable risk factor for cardiovascular disease, which is the leading cause of death in the United States. We applied our validated HTN computable phenotypes within the All of Us Research Program to uncover prevalence and characteristics of HTN and apparent treatment-resistant hypertension (aTRH) in United States.</p><p><strong>Methods: </strong>Within the All of Us Researcher Workbench, we built a retrospective cohort (January 1, 2008-July 1, 2023), identifying all adults with available age data, at least one blood pressure (BP) measurement, prescribed at least one antihypertensive medication, and with at least one SNOMED \"Essential hypertension\" diagnosis code.</p><p><strong>Results: </strong>We identified 99 461 participants with HTN who met the eligibility criteria. Following the application of our computable phenotypes, an overall population of 81 462 were further categorized to aTRH (14.4%), stable-controlled HTN (SCH) (39.5%), and Other HTN (46.1%). Compared to participants with SCH, participants with aTRH were older, more likely to be of Black or African American race, had higher levels of social deprivation, and a heightened prevalence of comorbidities such as hyperlipidemia and diabetes. Heart failure, chronic kidney disease, and diabetes were the comorbidities most strongly associated with aTRH. β-blockers were the most prescribed antihypertensive medication. At index date, the overall BP control rate was 62%.</p><p><strong>Discussion and conclusion: </strong>All of Us provides a unique opportunity to characterize HTN in the United States. Consistent findings from this study with our prior research highlight the interoperability of our computable phenotypes.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142057034","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":"Generating colloquial radiology reports with large language models.","authors":"Cynthia Crystal Tang, Supriya Nagesh, David A Fussell, Justin Glavis-Bloom, Nina Mishra, Charles Li, Gillean Cortes, Robert Hill, Jasmine Zhao, Angellica Gordon, Joshua Wright, Hayden Troutt, Rod Tarrago, Daniel S Chow","doi":"10.1093/jamia/ocae223","DOIUrl":"https://doi.org/10.1093/jamia/ocae223","url":null,"abstract":"<p><strong>Objectives: </strong>Patients are increasingly being given direct access to their medical records. However, radiology reports are written for clinicians and typically contain medical jargon, which can be confusing. One solution is for radiologists to provide a \"colloquial\" version that is accessible to the layperson. Because manually generating these colloquial translations would represent a significant burden for radiologists, a way to automatically produce accurate, accessible patient-facing reports is desired. We propose a novel method to produce colloquial translations of radiology reports by providing specialized prompts to a large language model (LLM).</p><p><strong>Materials and methods: </strong>Our method automatically extracts and defines medical terms and includes their definitions in the LLM prompt. Using our method and a naive strategy, translations were generated at 4 different reading levels for 100 de-identified neuroradiology reports from an academic medical center. Translations were evaluated by a panel of radiologists for accuracy, likability, harm potential, and readability.</p><p><strong>Results: </strong>Our approach translated the Findings and Impression sections at the 8th-grade level with accuracies of 88% and 93%, respectively. Across all grade levels, our approach was 20% more accurate than the baseline method. Overall, translations were more readable than the original reports, as evaluated using standard readability indices.</p><p><strong>Conclusion: </strong>We find that our translations at the eighth-grade level strike an optimal balance between accuracy and readability. Notably, this corresponds to nationally recognized recommendations for patient-facing health communication. We believe that using this approach to draft patient-accessible reports will benefit patients without significantly increasing the burden on radiologists.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142044248","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}