Mayo Clinic Proceedings. Digital health最新文献

{"title":"Deep Learning–Based Prediction Modeling of Major Adverse Cardiovascular Events After Liver Transplantation","authors":"Ahmed Abdelhameed PhD ,&nbsp;Harpreet Bhangu MD ,&nbsp;Jingna Feng MS ,&nbsp;Fang Li PhD ,&nbsp;Xinyue Hu MS ,&nbsp;Parag Patel MD ,&nbsp;Liu Yang MD ,&nbsp;Cui Tao","doi":"10.1016/j.mcpdig.2024.03.005","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.03.005","url":null,"abstract":"<div><h3>Objective</h3><p>To validate deep learning models’ ability to predict post-transplantation major adverse cardiovascular events (MACE) in patients undergoing liver transplantation (LT).</p></div><div><h3>Patients and Methods</h3><p>We used data from Optum’s de-identified Clinformatics Data Mart Database to identify liver transplant recipients between January 2007 and March 2020. To predict post-transplantation MACE risk, we considered patients’ demographics characteristics, diagnoses, medications, and procedural data recorded back to 3 years before the LT procedure date (index date). MACE is predicted using the bidirectional gated recurrent units (BiGRU) deep learning model in different prediction interval lengths up to 5 years after the index date. In total, 18,304 liver transplant recipients (mean age, 57.4 years [SD, 12.76]; 7158 [39.1%] women) were used to develop and test the deep learning model’s performance against other baseline machine learning models. Models were optimized using 5-fold cross-validation on 80% of the cohort, and model performance was evaluated on the remaining 20% using the area under the receiver operating characteristic curve (AUC-ROC) and the area under the precision-recall curve (AUC-PR).</p></div><div><h3>Results</h3><p>Using different prediction intervals after the index date, the top-performing model was the deep learning model, BiGRU, and achieved an AUC-ROC of 0.841 (95% CI, 0.822-0.862) and AUC-PR of 0.578 (95% CI, 0.537-0.621) for a 30-day prediction interval after LT.</p></div><div><h3>Conclusion</h3><p>Using longitudinal claims data, deep learning models can efficiently predict MACE after LT, assisting clinicians in identifying high-risk candidates for further risk stratification or other management strategies to improve transplant outcomes based on important features identified by the model.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 221-230"},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000221/pdfft?md5=93eb32520224a4e9423e1f9cc6e1d49b&pid=1-s2.0-S2949761224000221-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140552567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inherent Bias in Large Language Models: A Random Sampling Analysis","authors":"Noel F. Ayoub MD, MBA ,&nbsp;Karthik Balakrishnan MD, MPH ,&nbsp;Marc S. Ayoub MD ,&nbsp;Thomas F. Barrett MD ,&nbsp;Abel P. David MD ,&nbsp;Stacey T. Gray MD","doi":"10.1016/j.mcpdig.2024.03.003","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.03.003","url":null,"abstract":"<div><p>There are mounting concerns regarding inherent bias, safety, and tendency toward misinformation of large language models (LLMs), which could have significant implications in health care. This study sought to determine whether generative artificial intelligence (AI)-based simulations of physicians making life-and-death decisions in a resource-scarce environment would demonstrate bias. Thirteen questions were developed that simulated physicians treating patients in resource-limited environments. Through a random sampling of simulated physicians using OpenAI’s generative pretrained transformer (GPT-4), physicians were tasked with choosing only 1 patient to save owing to limited resources. This simulation was repeated 1000 times per question, representing 1000 unique physicians and patients each. Patients and physicians spanned a variety of demographic characteristics. All patients had similar a priori likelihood of surviving the acute illness. Overall, simulated physicians consistently demonstrated racial, gender, age, political affiliation, and sexual orientation bias in clinical decision-making. Across all demographic characteristics, physicians most frequently favored patients with similar demographic characteristics as themselves, with most pairwise comparisons showing statistical significance (<em>P</em>&lt;.05). Nondescript physicians favored White, male, and young demographic characteristics. The male doctor gravitated toward the male, White, and young, whereas the female doctor typically preferred female, young, and White patients. In addition to saving patients with their own political affiliation, Democratic physicians favored Black and female patients, whereas Republicans preferred White and male demographic characteristics. Heterosexual and gay/lesbian physicians frequently saved patients of similar sexual orientation. Overall, publicly available chatbot LLMs demonstrate significant biases, which may negatively impact patient outcomes if used to support clinical care decisions without appropriate precautions.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 186-191"},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000208/pdfft?md5=895559f96cdc78e7afbad43c7d8d164a&pid=1-s2.0-S2949761224000208-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140542637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Therapeutic Content of Mobile Phone Applications for Substance Use Disorders: An Umbrella Review","authors":"Tyler S. Oesterle MD, MPH ,&nbsp;Daniel K. Hall-Flavin MD, MS ,&nbsp;Nicholas L. Bormann MD ,&nbsp;Larissa L. Loukianova MD, PhD ,&nbsp;David C. Fipps DO ,&nbsp;Scott A. Breitinger MD ,&nbsp;Wesley P. Gilliam PhD ,&nbsp;Tiffany Wu MD ,&nbsp;Sabrina Correa da Costa MD ,&nbsp;Stephan Arndt PhD ,&nbsp;Victor M. Karpyak MD, PhD","doi":"10.1016/j.mcpdig.2024.03.004","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.03.004","url":null,"abstract":"<div><p>Mobile phone applications (MPAs) for substance use disorder (SUD) treatment are increasingly used by patients. Although pilot studies have shown promising results, multiple previous systematic reviews noted insufficient evidence for MPA use in SUD treatment—many of the previously published reviews evaluated different trials. Subsequently, we aimed to conduct an umbrella review of previously published reviews investigating the efficacy of MPAs for SUD treatment, excluding nicotine/tobacco because umbrella reviews have been done in this population and the nicotine/tobacco MPA approach often differs from SUD-focused MPAs. No previous reviews have included a statistical meta-analysis of clinical trials to quantify an estimated overall effect. Seven reviews met inclusion criteria, and 17 unique studies with available data were taken from those reviews for the meta-analysis. Overall, reviews reported a lack of evidence for recommending MPAs for SUD treatment. However, MPA-delivered recovery support services, cognitive behavioral therapy, and contingency management were identified across multiple reviews as having promising evidence for SUD treatment. Hedges <em>g</em> effect size for an MPA reduction in substance use–related outcomes relative to the control arm was insignificant (0.137; 95% CI, −0.056 to 0.330; <em>P</em>=.16). In subgroup analysis, contingency management (1.29; 95% CI, 1.088-1.482; <em>τ</em>²=0; <em>k</em>=2) and cognitive behavioral therapy (0.02; 95% CI, 0.001-0.030; <em>τ</em>²=0; <em>k</em>=2) were significant. Although contingency management’s effect was large, both trials were small (samples of 40 and 30). This review includes an adapted framework for the American Psychiatric Association’s MPA guidelines that clinicians can implement to review MPAs critically with patients.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 192-206"},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S294976122400021X/pdfft?md5=ec649c717fe9aa97fc59b17250a82fe9&pid=1-s2.0-S294976122400021X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140542636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Command to Care: A Scoping Review on Utilization of Smart Speakers by Patients and Providers","authors":"Rishi Saripalle PhD ,&nbsp;Ravi Patel PharmD, MBA, MS","doi":"10.1016/j.mcpdig.2024.03.002","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.03.002","url":null,"abstract":"<div><p>Smart speakers have gained considerable consumer adoption and research interests. Despite their innovative interaction capabilities, a notable void exists in the literature, with no comprehensive scoping review that scrutinizes and consolidates the usage of smart speakers by providers and patients. This study performed a scoping review to explore the standalone use of smart speakers in health settings, focusing on their potential to support providers and empower patients to manage their health and well-being. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a comprehensive search from January 2014-September 2023, using select keywords, was performed across PubMed, Web of Science, Medline, IEEE, ACM, JAMIA, Embase, CINHAL, EBSCO, and Cochrane. The literature search yielded 1546 articles, of which 59 met the inclusion criteria. The identified studies are categorized into helping patients (n=54) with themes of independent living, reducing loneliness and improving social life, aiding in patient self-care and self-management, promoting physical activity, rethinking health care and service delivery, remote patient monitoring and communication, health information queries and helping providers (n=24) with themes recording and accessing medical information, and reducing provider workload. These research studies, performed in a controlled environment with limited patients, have found smart speakers’ high feasibility, acceptability, and positive reception in patient care and support providers. Furthermore, the findings showcase opportunities to leverage and challenges to address for a future of integrating and using smart speakers seamlessly in health settings.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 207-220"},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000191/pdfft?md5=602e1136c35af4a1a2f450e3a21f6755&pid=1-s2.0-S2949761224000191-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140547118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of Positive Cardiac Remodeling in Hypertrophic Obstructive Cardiomyopathy Using an Artificial Intelligence–Based Electrocardiographic Platform in Patients Treated With Mavacamten","authors":"Mustafa Suppah MD ,&nbsp;Kaitlin Roehl PA-C ,&nbsp;Kathryn Lew APRN, NP, MSN ,&nbsp;Reza Arsanjani MD ,&nbsp;Steven Lester MD ,&nbsp;Steve Ommen MD ,&nbsp;Jeffrey Geske MD ,&nbsp;Konstantinos C. Siontis MD ,&nbsp;Hartzell Schaff MD ,&nbsp;Said Alsidawi MD","doi":"10.1016/j.mcpdig.2024.04.002","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.04.002","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 255-257"},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000294/pdfft?md5=051e61d0b54747fe9d602ac3bd249683&pid=1-s2.0-S2949761224000294-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140822696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Social Media and Artificial Intelligence—Understanding Medical Misinformation Through Snapchat’s New Artificial Intelligence Chatbot","authors":"Clara E. Tandar ,&nbsp;Simar S. Bajaj ,&nbsp;Fatima Cody Stanford MD, MPH, MPA, MBA","doi":"10.1016/j.mcpdig.2024.04.004","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.04.004","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 252-254"},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000324/pdfft?md5=fa80a8039bfb5008a0473d61e26f78b6&pid=1-s2.0-S2949761224000324-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140818294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AImedReport: A Prototype Tool to Facilitate Research Reporting and Translation of Artificial Intelligence Technologies in Health Care","authors":"Tracey A. Brereton MS ,&nbsp;Momin M. Malik PhD, MS, MSc ,&nbsp;Lauren M. Rost PhD, MS ,&nbsp;Joshua W. Ohde PhD ,&nbsp;Lu Zheng PhD, MS ,&nbsp;Kristelle A. Jose MS ,&nbsp;Kevin J. Peterson PhD, MS ,&nbsp;David Vidal JD ,&nbsp;Mark A. Lifson PhD ,&nbsp;Joe Melnick BS ,&nbsp;Bryce Flor BS ,&nbsp;Jason D. Greenwood MD, MS ,&nbsp;Kyle Fisher MPA ,&nbsp;Shauna M. Overgaard PhD","doi":"10.1016/j.mcpdig.2024.03.008","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.03.008","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 246-251"},"PeriodicalIF":0.0,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000312/pdfft?md5=d79b5ee4ecae7f6058302e879e2b8af2&pid=1-s2.0-S2949761224000312-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140806852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Housing Characteristics of Areas With More Falls by Older Adults Living in Single-Family Detached Dwellings: A Cohort Study Using Geospatial Analysis","authors":"Paul Y. Takahashi MD, MPH ,&nbsp;Euijung Ryu PhD ,&nbsp;Katherine S. King MS ,&nbsp;Rachel E. Dixon BA ,&nbsp;Julie C. Porcher MS ,&nbsp;Philip H. Wheeler ,&nbsp;Chung Il Wi MD ,&nbsp;Young J. Juhn MD, MPH","doi":"10.1016/j.mcpdig.2024.04.001","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.04.001","url":null,"abstract":"<div><h3>Objective</h3><p>To identify geographic locations with high numbers of medically attended falls (ie, hotspots) by older adults and to test the associations between fall hotspots and resident/housing characteristics.</p></div><div><h3>Patients and Methods</h3><p>In this cohort study, we retrospectively reviewed adults who were 65 years or older, lived in a single-family detached dwelling, and had a medically attended fall in Olmsted County, MN, between April 1, 2012, and December 31, 2014. We identified medically attended falls by using billing codes and confirmed by manual review of the electronic health records. We performed geospatial analysis to identify fall hotspots and evaluated the association between fall hotspots and resident or housing characteristics with logistic regression models, adjusting for age, sex, socioeconomic status, chronic health conditions, and/or a history of falls.</p></div><div><h3>Results</h3><p>Among 12,888 residents living in single-family detached dwellings in our community, 587 residents (4.6%) had documented accidental falls. Falls were more common in older residents and in women. Residents who had more chronic diseases, lower socioeconomic status, and a history of falls also had higher odds of a fall. Geospatial analysis identified 2061 (16.0%) residents who lived in a fall hotspot. Houses in hotspots were more likely to have more stories with fewer stairs (split level) (odds ratio [OR], 1.75; 95% CI, 1.57-1.94, for split level vs 1-story houses), smaller square feet (OR, 0.29; 95% CI, 0.24-0.35, for largest vs smallest houses), and in the highest quartile for age (OR, 1.46; 95% CI, 1.26-1.70, for oldest built vs newest built houses).</p></div><div><h3>Conclusion</h3><p>Falls were more common in locations in our community that had older, smaller homes and lower housing-based socioeconomic status. These findings can be used by clinicians to identify residents who are at higher risk for falls.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 259-269"},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000282/pdfft?md5=677afcbfea9f9ecd229c0c3ed4369544&pid=1-s2.0-S2949761224000282-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140894928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Use of Artificial Intelligence to Detect Malignant Skin Lesions","authors":"Sofia Haddadin ,&nbsp;Latha Ganti MD, MS, MBA","doi":"10.1016/j.mcpdig.2024.04.003","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.04.003","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 241-245"},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000300/pdfft?md5=10086ff1aae1aa26c957d2e5d87ac957&pid=1-s2.0-S2949761224000300-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140806851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Empowering Patients in the Digital Age: New Framework to Measure and Improve Patient Digital Experiences","authors":"Andrew Kucheriavy BCS, BEc","doi":"10.1016/j.mcpdig.2024.03.001","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.03.001","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 181-185"},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S294976122400018X/pdfft?md5=8a1e0da33153f6f9b359407345dc3538&pid=1-s2.0-S294976122400018X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140342099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}