Vol 2, No 1 (2023): Jan-Dec

Deep Learning Transformer Models for Building a Comprehensive and Real-time Trauma Observatory: Development and Validation Study

Gabrielle Chenais, Cédric Gil-Jardiné, Hélène Touchais, Marta Avalos Fernandez, Benjamin Contrand, Eric Tellier, Xavier Combes, Loick Bourdois, Philippe Revel, Emmanuel Lagarde

JMIR AI 2023 (Jan 12); 2:e40843

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Developing an Inpatient Electronic Medical Record Phenotype for Hospital-Acquired Pressure Injuries: Case Study Using Natural Language Processing Models

Elvira Nurmambetova, Jie Pan, Zilong Zhang, Guosong Wu, Seungwon Lee, Danielle A Southern, Elliot A Martin, Chester Ho, Yuan Xu, Cathy A Eastwood

JMIR AI 2023 (Mar 08); 2:e41264

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Few-Shot Learning for Clinical Natural Language Processing Using Siamese Neural Networks: Algorithm Development and Validation Study

David Oniani, Premkumar Chandrasekar, Sonish Sivarajkumar, Yanshan Wang

JMIR AI 2023 (May 04); 2:e44293

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Artificial Intelligence Algorithms in Health Care: Is the Current Food and Drug Administration Regulation Sufficient?

Meghavi Mashar, Shreya Chawla, Fangyue Chen, Baker Lubwama, Kyle Patel, Mihir A Kelshiker, Patrik Bachtiger, Nicholas S Peters

JMIR AI 2023 (Jan 16); 2:e42940

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The Application of Artificial Intelligence in Health Care Resource Allocation Before and During the COVID-19 Pandemic: Scoping Review

Hao Wu, Xiaoyu Lu, Hanyu Wang

JMIR AI 2023 (Jan 30); 2:e38397

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Predicting Treatment Interruption Among People Living With HIV in Nigeria: Machine Learning Approach

Matthew-David Ogbechie, Christa Fischer Walker, Mu-Tien Lee, Amina Abba Gana, Abimbola Oduola, Augustine Idemudia, Matthew Edor, Emily Lark Harris, Jessica Stephens, Xiaoming Gao, Pai-Lien Chen, Navindra Etwaroo Persaud

JMIR AI 2023 (May 12); 2:e44432

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Machine Learning–Based Time in Patterns for Blood Glucose Fluctuation Pattern Recognition in Type 1 Diabetes Management: Development and Validation Study

Nicholas Berin Chan, Weizi Li, Theingi Aung, Eghosa Bazuaye, Rosa M Montero

JMIR AI 2023 (May 26); 2:e45450

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An Assessment of How Clinicians and Staff Members Use a Diabetes Artificial Intelligence Prediction Tool: Mixed Methods Study

Winston R Liaw, Yessenia Ramos Silva, Erica G Soltero, Alex Krist, Angela L Stotts

JMIR AI 2023 (May 29); 2:e45032

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Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review

Carl Thomas Berdahl, Lawrence Baker, Sean Mann, Osonde Osoba, Federico Girosi

JMIR AI 2023 (Feb 07); 2:e42936

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Forecasting Artificial Intelligence Trends in Health Care: Systematic International Patent Analysis

Stan Benjamens, Pranavsingh Dhunnoo, Márton Görög, Bertalan Mesko

JMIR AI 2023 (May 26); 2:e47283

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Predicting Patient Mortality for Earlier Palliative Care Identification in Medicare Advantage Plans: Features of a Machine Learning Model

Anne Bowers, Chelsea Drake, Alexi E Makarkin, Robert Monzyk, Biswajit Maity, Andrew Telle

JMIR AI 2023 (Feb 20); 2:e42253

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Deep Learning to Detect Pancreatic Cystic Lesions on Abdominal Computed Tomography Scans: Development and Validation Study

Maria Montserrat Duh, Neus Torra-Ferrer, Meritxell Riera-Marín, Dídac Cumelles, Júlia Rodríguez-Comas, Javier García López, Mª Teresa Fernández Planas

JMIR AI 2023 (Mar 17); 2:e40702

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Patient Embeddings From Diagnosis Codes for Health Care Prediction Tasks: Pat2Vec Machine Learning Framework

Edgar Steiger, Lars Eric Kroll

JMIR AI 2023 (Apr 21); 2:e40755

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Prediction of Chronic Stress and Protective Factors in Adults: Development of an Interpretable Prediction Model Based on XGBoost and SHAP Using National Cross-sectional DEGS1 Data

Arezoo Bozorgmehr, Birgitta Weltermann

JMIR AI 2023 (May 16); 2:e41868

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Application of Artificial Intelligence to the Monitoring of Medication Adherence for Tuberculosis Treatment in Africa: Algorithm Development and Validation

Juliet Nabbuye Sekandi, Weili Shi, Ronghang Zhu, Patrick Kaggwa, Ernest Mwebaze, Sheng Li

JMIR AI 2023 (Feb 23); 2:e40167

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Preparing for an Artificial Intelligence–Enabled Future: Patient Perspectives on Engagement and Health Care Professional Training for Adopting Artificial Intelligence Technologies in Health Care Settings

Tharshini Jeyakumar, Sarah Younus, Melody Zhang, Megan Clare, Rebecca Charow, Inaara Karsan, Azra Dhalla, Dalia Al-Mouaswas, Jillian Scandiffio, Justin Aling, Mohammad Salhia, Nadim Lalani, Scott Overholt, David Wiljer

JMIR AI 2023 (Mar 02); 2:e40973

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Enabling Early Health Care Intervention by Detecting Depression in Users of Web-Based Forums using Language Models: Longitudinal Analysis and Evaluation

David Owen, Dimosthenis Antypas, Athanasios Hassoulas, Antonio F Pardiñas, Luis Espinosa-Anke, Jose Camacho Collados

JMIR AI 2023 (Mar 24); 2:e41205

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A Scalable Radiomics- and Natural Language Processing–Based Machine Learning Pipeline to Distinguish Between Painful and Painless Thoracic Spinal Bone Metastases: Retrospective Algorithm Development and Validation Study

Hossein Naseri, Sonia Skamene, Marwan Tolba, Mame Daro Faye, Paul Ramia, Julia Khriguian, Marc David, John Kildea

JMIR AI 2023 (May 22); 2:e44779

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Detecting Ground Glass Opacity Features in Patients With Lung Cancer: Automated Extraction and Longitudinal Analysis via Deep Learning–Based Natural Language Processing

Kyeryoung Lee, Zongzhi Liu, Urmila Chandran, Iftekhar Kalsekar, Balaji Laxmanan, Mitchell K Higashi, Tomi Jun, Meng Ma, Minghao Li, Yun Mai, Christopher Gilman, Tongyu Wang, Lei Ai, Parag Aggarwal, Qi Pan, William Oh, Gustavo Stolovitzky, Eric Schadt, Xiaoyan Wang

JMIR AI 2023 (Jun 01); 2:e44537

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Reporting and Methodological Observations on Prognostic and Diagnostic Machine Learning Studies

Khaled El Emam, William Klement, Bradley Malin

JMIR AI 2023 (Apr 28); 2:e47995

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