Mayo Clinic Proceedings. Digital health最新文献

{"title":"Application of Federated Learning in Cardiology: Key Challenges and Potential Solutions","authors":"Md Saifur Rahman PhD , Chandan Karmarkar PhD , Sheikh Mohammed Shariful Islam MBBS, PhD","doi":"10.1016/j.mcpdig.2024.09.005","DOIUrl":"10.1016/j.mcpdig.2024.09.005","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 4","pages":"Pages 590-595"},"PeriodicalIF":0.0,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552432","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":"Assessments of Generative Artificial Intelligence as Clinical Decision Support Ought to be Incorporated Into Randomized Controlled Trials of Electronic Alerts for Acute Kidney Injury","authors":"Donal J. Sexton MD, PhD , Conor Judge MB, PhD","doi":"10.1016/j.mcpdig.2024.09.004","DOIUrl":"10.1016/j.mcpdig.2024.09.004","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 4","pages":"Pages 606-610"},"PeriodicalIF":0.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592645","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":"Deep Learning Model for Predicting Neurodevelopmental Outcome in Very Preterm Infants Using Cerebral Ultrasound","authors":"Tahani M. Ahmad MD, ABR ,&nbsp;Alessandro Guida PhD ,&nbsp;Sam Stewart PhD ,&nbsp;Noah Barrett MSc ,&nbsp;Michael J. Vincer MD ,&nbsp;Jehier K. Afifi MD, MSc","doi":"10.1016/j.mcpdig.2024.09.003","DOIUrl":"10.1016/j.mcpdig.2024.09.003","url":null,"abstract":"<div><h3>Objective</h3><div>To develop deep learning (DL) models applied to neonatal cranial ultrasound (CUS) and clinical variables to predict neurodevelopmental impairment (NDI) in very preterm infants (VPIs) at 3 years of corrected age.</div></div><div><h3>Patients and Methods</h3><div>This is a retrospective study of a cohort of VPI (22⁰-30⁶ weeks’ gestation) born between 2004 and 2016 in Nova Scotia, Canada. Clinical data at hospital discharge and CUS images at 3 time points were used to develop DL models using elastic net (EN) and convolutional neural network (CNN). The models’ performances were compared using precision recall area under the curve (PR-AUC) and area under the receiver operation characteristic curve (ROC-AUC) with their 95% ci.</div></div><div><h3>Results</h3><div>Of 665 eligible VPIs, 619 (93%) infants with 4184 CUS images were included. The CNN model combining CUS and clinical variables reported better performance (PR-AUC, 0.75; 95% CI, 072-0.79; ROC-AUC, 0.71; 95% CI, 0.67-0.74) in the prediction of positive NDI outcome compared with the traditional models based solely on clinical predictors (PR-AUC, 0.60; 95% CI, 0.52-0.68; ROC-AUC, 0.72; 95% CI, 0.68-0.75). When analyzed by the CUS plane and acquisition time point, the model using the anterior coronal plane at 6 weeks of age provided the highest predictive accuracy (PR-AUC, 0.81; 95% CI, 0.77-0.91; ROC-AUC, 0.78; 95% CI, 0.66-0.87).</div></div><div><h3>Conclusion</h3><div>We developed and internally validated a DL prognostic model using CUS and clinical predictors to predict NDI in VPIs at 3 years of age. Early and accurate identification of infants at risk for NDI enables referral to targeted interventions, which improves functional outcomes.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 4","pages":"Pages 596-605"},"PeriodicalIF":0.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552431","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":"Medication Extraction and Drug Interaction Chatbot: Generative Pretrained Transformer-Powered Chatbot for Drug-Drug Interaction","authors":"Won Tae Kim MD, PhD ,&nbsp;Jaegwang Shin ,&nbsp;In-Sang Yoo ,&nbsp;Jae-Woo Lee MD, PhD ,&nbsp;Hyun Jeong Jeon MD, PhD ,&nbsp;Hyo-Sun Yoo MD ,&nbsp;Yongwhan Kim MD ,&nbsp;Jeong-Min Jo ,&nbsp;ShinJi Hwang ,&nbsp;Woo-Jeong Lee ,&nbsp;Seung Park PhD ,&nbsp;Yong-June Kim MD, PhD","doi":"10.1016/j.mcpdig.2024.09.001","DOIUrl":"10.1016/j.mcpdig.2024.09.001","url":null,"abstract":"<div><h3>Objective</h3><div>To assist individuals, particularly cancer patients or those with complex comorbidities, in quickly identifying potentially contraindicated medications when taking multiple drugs simultaneously.</div></div><div><h3>Patients and Methods</h3><div>In this study, we introduce the Medication Extraction and Drug Interaction Chatbot (MEDIC), an artificial intelligence system that integrates optical character recognition and Chat generative pretrained transformer through the Langchain framework. Medication Extraction and Drug Interaction Chatbot starts by receiving 2 drug bag images from the patient. It uses optical character recognition and text similarity techniques to extract drug names from the images. The extracted drug names are then processed through Chat generative pretrained transformer and Langchain to provide the user with information about drug contraindications. The MEDIC responds to the user with clear and concise sentences to ensure the information is easily understandable. This research was conducted from July 1, 2022 to April 30, 2024.</div></div><div><h3>Results</h3><div>This streamlined process enhances the accuracy of drug-drug interaction detection, providing a crucial tool for health care professionals and patients to improve medication safety. The proposed system was validated through rigorous evaluation using real-world data, reporting high accuracy in drug-drug interaction identification and highlighting its potential to benefit medication management practices considerably.</div></div><div><h3>Conclusion</h3><div>By implementing MEDIC, contraindicated medications can be identified using only medication packaging, and users can be alerted to potential drug adverse effects, thereby contributing to advancements in patient care in clinical settings.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 4","pages":"Pages 611-619"},"PeriodicalIF":0.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650827","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":"Use of a Head-Mounted Assisted Reality, High-Resolution Telemedicine Camera and Satellite Communication Terminal in an Out-of-Hospital Cardiac Arrest","authors":"Christopher S. Russi DO ,&nbsp;Sarayna S. McGuire MD ,&nbsp;Aaron B. Klassen MD ,&nbsp;Kate M. Skeens MD ,&nbsp;Kate J. Arms NREMT-P ,&nbsp;Lindsey D. Kaczmerick NREMT-P ,&nbsp;Patrick J. Fullerton DO, MHCM ,&nbsp;Louis M. Radnothy DO ,&nbsp;Anuradha Luke MD","doi":"10.1016/j.mcpdig.2024.09.002","DOIUrl":"10.1016/j.mcpdig.2024.09.002","url":null,"abstract":"<div><div>Mayo Clinic Ambulance Service is testing a novel combination of technologies to enhance the ability to provide prehospital telemedicine connecting physicians with paramedics. Mayo Clinic Ambulance Service partnered with start-up company OPTAC-X to field test a novel head-mounted video camera connected with a satellite communications terminal to bring medical control emergency medicine physicians to the patient and paramedic by video. The authors believe this is the first report of a physician providing medical guidance to paramedics resuscitating an out-of-hospital cardiac arrest using these technologies.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 4","pages":"Pages 584-589"},"PeriodicalIF":0.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529549","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":"Challenges and Limitations of Human Oversight in Ethical Artificial Intelligence Implementation in Health Care: Balancing Digital Literacy and Professional Strain","authors":"Roanne van Voorst PhD","doi":"10.1016/j.mcpdig.2024.08.004","DOIUrl":"10.1016/j.mcpdig.2024.08.004","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 4","pages":"Pages 559-563"},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000798/pdfft?md5=b1d9e371d52b9b7e6fc864fafe9eab4c&pid=1-s2.0-S2949761224000798-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311132","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":"Embedding Internal Accountability Into Health Care Institutions for Safe, Effective, and Ethical Implementation of Artificial Intelligence Into Medical Practice: A Mayo Clinic Case Study","authors":"Brenna Loufek MS ,&nbsp;David Vidal JD ,&nbsp;David S. McClintock MD ,&nbsp;Mark Lifson PhD ,&nbsp;Eric Williamson MD ,&nbsp;Shauna Overgaard PhD ,&nbsp;Kathleen McNaughton JD ,&nbsp;Melissa C. Lipford MD ,&nbsp;Darrell S. Pardi MD","doi":"10.1016/j.mcpdig.2024.08.008","DOIUrl":"10.1016/j.mcpdig.2024.08.008","url":null,"abstract":"<div><div>Health care organizations are building, deploying, and self-governing digital health technologies (DHTs), including artificial intelligence, at an increasing rate. This scope necessitates expertise and quality infrastructure to ensure that the technology impacting patient care is safe, effective, and ethical throughout its lifecycle. The objective of this article is to describe Mayo Clinic’s approach for embedding internal accountability as a case study for other health care institutions seeking modalities for responsible implementation of artificial intelligence–enabled DHTs. Mayo Clinic aims to enable and empower innovators by (1) building internal skills and expertise, (2) establishing a centralized review board, and (3) aligning development and deployment processes with regulations, standards, and best practices. In 2022, Mayo Clinic established the Software as a Medical Device Review Board (The Board), an independent body of physicians and domain experts to represent the organization in providing innovators regulatory and risk mitigation recommendations for DHTs. Hundreds of digital health product teams have since benefited from this function, intended to enable responsible innovation in alignment with regulation and state-of-the-art quality management practices. Other health care institutions can adopt similar internal accountability bodies using this framework. Opportunity remains to iterate on Mayo Clinic’s approach in alignment with advancing best practices and enhance representation on The Board as part of standard continuous improvement practices.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 4","pages":"Pages 574-583"},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419546","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":"Echocardiographic Diagnosis of Hypertrophic Cardiomyopathy by Machine Learning","authors":"Nasibeh Zanjirani Farahani PhD ,&nbsp;Mateo Alzate Aguirre MD ,&nbsp;Vanessa Karlinski Vizentin MD ,&nbsp;Moein Enayati PhD ,&nbsp;J. Martijn Bos MD, PhD ,&nbsp;Andredi Pumarejo Medina MD ,&nbsp;Kathryn F. Larson MD ,&nbsp;Kalyan S. Pasupathy PhD ,&nbsp;Christopher G. Scott MS ,&nbsp;April L. Zacher MS ,&nbsp;Eduard Schlechtinger MS ,&nbsp;Bruce K. Daniels RDCS ,&nbsp;Vinod C. Kaggal MS ,&nbsp;Jeffrey B. Geske MD ,&nbsp;Patricia A. Pellikka MD ,&nbsp;Jae K. Oh MD ,&nbsp;Steve R. Ommen MD ,&nbsp;Garvan C. Kane MD ,&nbsp;Michael J. Ackerman MD, PhD ,&nbsp;Adelaide M. Arruda-Olson MD, PhD","doi":"10.1016/j.mcpdig.2024.08.009","DOIUrl":"10.1016/j.mcpdig.2024.08.009","url":null,"abstract":"<div><h3>Objective</h3><div>To develop machine learning tools for automated hypertrophic cardiomyopathy (HCM) case recognition from echocardiographic metrics, aiming to identify HCM from standard echocardiographic data with high performance.</div></div><div><h3>Patients and Methods</h3><div>Four different random forest machine learning models were developed using a case-control cohort composed of 5548 patients with HCM and 16,973 controls without HCM, from January 1, 2004, to March 15, 2019. Each patient with HCM was matched to 3 controls by sex, age, and year of echocardiography. Ten-fold crossvalidation was used to train the models to identify HCM. Variables included in the models were demographic characteristics (age, sex, and body surface area) and 16 standard echocardiographic metrics.</div></div><div><h3>Results</h3><div>The models were differentiated by global, average, individual, or no strain measurements. Area under the receiver operating characteristic curves (area under the curve) ranged from 0.92 to 0.98 for the 4 separate models. Area under the curves of model 2 (using left ventricular global longitudinal strain; 0.97; 95% CI, 0.95-0.98), 3 (using averaged strain; 0.96; 95% CI, 0.94-0.97), and 4 (using 17 individual strains per patient; 0.98; 95% CI, 0.97-0.99) had comparable performance. By comparison, model 1 (no strain data; 0.92; 95% CI, 0.90-0.94) had an inferior area under the curve.</div></div><div><h3>Conclusion</h3><div>Machine learning tools that analyze echocardiographic metrics identified HCM cases with high performance. Detection of HCM cases improved when strain data was combined with standard echocardiographic metrics.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 4","pages":"Pages 564-573"},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419545","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":"Transforming Large Language Models into Superior Clinical Decision Support Tools by Embedding Clinical Practice Guidelines","authors":"Yanshan Wang PhD ,&nbsp;Xiaoxi Yao PhD, MPH ,&nbsp;Xizhi Wu","doi":"10.1016/j.mcpdig.2024.05.018","DOIUrl":"10.1016/j.mcpdig.2024.05.018","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 3","pages":"Pages 491-492"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000531/pdfft?md5=1fdf44c4e47001acd445c32b0cc3c93b&pid=1-s2.0-S2949761224000531-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239433","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":"Validating a Portable, Camera-Based System to Scale the Clinical Gait Assessment as a Tele-Health Solution","authors":"Kevin A. Mazurek Ph.D. ,&nbsp;Leland Barnard Ph.D. ,&nbsp;Hugo Botha M.B., Ch.B. ,&nbsp;Teresa Christianson ,&nbsp;Jonathan Graff-Radford M.D. ,&nbsp;David T. Jones M.D. ,&nbsp;David S. Knopman M.D. ,&nbsp;Ronald C. Petersen M.D., Ph.D. ,&nbsp;Prashanthi Vemuri Ph.D. ,&nbsp;Clifford R. Jack Jr. M.D. ,&nbsp;Farwa Ali M.B.B.S.","doi":"10.1016/j.mcpdig.2024.05.020","DOIUrl":"10.1016/j.mcpdig.2024.05.020","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 3","pages":"Page 491"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000555/pdfft?md5=228cc31f76fd54738eca72fe1a0edf54&pid=1-s2.0-S2949761224000555-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239432","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}