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Machine Learning–Based Suicide Risk Prediction Model for Suicidal Trajectory on Social Media Following Suicidal Mentions: Independent Algorithm Validation

Machine Learning–Based Suicide Risk Prediction Model for Suicidal Trajectory on Social Media Following Suicidal Mentions: Independent Algorithm Validation

Xu et al [10] demonstrated the power of integrating transfer learning in a targeted fusion approach, potentially applicable to any data source including social media, to generate and demonstrate the efficacy of a predictor of youth’s suicide risk in a hospital setting using a clinical database.

Zachary Kaminsky, Robyn J McQuaid, Kim GC Hellemans, Zachary R Patterson, Mysa Saad, Robert L Gabrys, Tetyana Kendzerska, Alfonso Abizaid, Rebecca Robillard

J Med Internet Res 2024;26:e49927

Evaluating Machine Learning–Based Automated Personalized Daily Step Goals Delivered Through a Mobile Phone App: Randomized Controlled Trial

Evaluating Machine Learning–Based Automated Personalized Daily Step Goals Delivered Through a Mobile Phone App: Randomized Controlled Trial

Although this simple adaptive goal algorithm was modestly effective [28], a computer simulation study [43] for a weight loss intervention involving physical activity goal-setting and in-person counseling sessions found that a more sophisticated algorithm using statistics and machine learning to set goals by learning participants’ responsiveness to goals could provide greater effectiveness (as compared with simple rules such as goal setting using a fixed percentile of steps taken in the past few days) in encouraging

Mo Zhou, Yoshimi Fukuoka, Yonatan Mintz, Ken Goldberg, Philip Kaminsky, Elena Flowers, Anil Aswani

JMIR Mhealth Uhealth 2018;6(1):e28

Venous Access: National Guideline and Registry Development (VANGUARD): Advancing Patient-Centered Venous Access Care Through the Development of a National Coordinated Registry Network

Venous Access: National Guideline and Registry Development (VANGUARD): Advancing Patient-Centered Venous Access Care Through the Development of a National Coordinated Registry Network

Per clinical practice guidelines, accurate and meaningful diagnosis of CRBSI requires (1) a health care provider responsible for and capable of recognizing the signs and symptoms of an infection, (2) microbiologic evidence of a local or bloodstream infection, and (3) exclusion of a noncatheter source of the infection.

Andrea Iorga, Marti J Velezis, Danica Marinac-Dabic, Robert F Lario, Stanley M Huff, Beth Gore, Leonard A Mermel, L Charles Bailey, Julia Skapik, Debi Willis, Robert E Lee, Frank P Hurst, Laura E Gressler, Terrie L Reed, Richard Towbin, Kevin M Baskin

J Med Internet Res 2023;25:e43658

Automated Pathologic TN Classification Prediction and Rationale Generation From Lung Cancer Surgical Pathology Reports Using a Large Language Model Fine-Tuned With Chain-of-Thought: Algorithm Development and Validation Study

Automated Pathologic TN Classification Prediction and Rationale Generation From Lung Cancer Surgical Pathology Reports Using a Large Language Model Fine-Tuned With Chain-of-Thought: Algorithm Development and Validation Study

To establish lung cancer staging, they selected 14 key entities, including tumor shape, density, and invasion, embedded the computed tomography reports using word2vec [14], and performed named entity recognition (NER) for each entity using a combination of BERT and bidirectional long short-term memory. In the study, NER with BERT demonstrated excellent performance in information extraction, achieving a macro F1-score of 0.901 and a micro F1-score of 0.946.

Sanghwan Kim, Sowon Jang, Borham Kim, Leonard Sunwoo, Seok Kim, Jin-Haeng Chung, Sejin Nam, Hyeongmin Cho, Donghyoung Lee, Keehyuck Lee, Sooyoung Yoo

JMIR Med Inform 2024;12:e67056

Development of a Pipeline for Adverse Drug Reaction Identification in Clinical Notes: Word Embedding Models and String Matching

Development of a Pipeline for Adverse Drug Reaction Identification in Clinical Notes: Word Embedding Models and String Matching

One of the widespread topics in NLP is the use of word embeddings—a vector representation of a text, often established through evaluation of the word’s context. The use of word embeddings for the evaluation of clinical free text for research purposes is increasing [11]. Research has shown that training word embedding models on a domain-specific data set generates better results than training on a general data set [12,13].

Klaske R Siegersma, Maxime Evers, Sophie H Bots, Floor Groepenhoff, Yolande Appelman, Leonard Hofstra, Igor I Tulevski, G Aernout Somsen, Hester M den Ruijter, Marco Spruit, N Charlotte Onland-Moret

JMIR Med Inform 2022;10(1):e31063

Recruiting Young People for Digital Mental Health Research: Lessons From an AI-Driven Adaptive Trial

Recruiting Young People for Digital Mental Health Research: Lessons From an AI-Driven Adaptive Trial

A review by Lane et al [3] on online recruitment methods for web-based and m Health studies found that successful recruitment of participants into a trial varied widely depending on the population, budget, intervention, costs, and study design. Of the studies included in their review, less than half (42%) found Facebook advertisements to be an effective method of recruitment and a quarter found Google advertisements to be the most effective method [3].

Wu Yi Zheng, Artur Shvetcov, Aimy Slade, Zoe Jenkins, Leonard Hoon, Alexis Whitton, Rena Logothetis, Smrithi Ravindra, Stefanus Kurniawan, Sunil Gupta, Kit Huckvale, Eileen Stech, Akash Agarwal, Joost Funke Kupper, Stuart Cameron, Jodie Rosenberg, Nicholas Manoglou, Manisha Senadeera, Svetha Venkatesh, Kon Mouzakis, Rajesh Vasa, Helen Christensen, Jill M Newby

J Med Internet Res 2025;27:e60413

Optimizing Technology-Based Prompts for Supporting People Living With Dementia in Completing Activities of Daily Living at Home: Experimental Approach to Prompt Modality, Task Breakdown, and Attentional Support

Optimizing Technology-Based Prompts for Supporting People Living With Dementia in Completing Activities of Daily Living at Home: Experimental Approach to Prompt Modality, Task Breakdown, and Attentional Support

Interestingly, in a recent version of the COACH handwashing system [20], researchers reported that voice-based interaction with the system was much more effective for users with dementia than the previous iterations that had used a camera to detect step completion, indicating that a human-in-the-loop system may be more effective than a fully automated one.

Madeleine Cannings, Ruth Brookman, Simon Parker, Leonard Hoon, Asuka Ono, Hiroaki Kawata, Hisashi Matsukawa, Celia B Harris

JMIR Aging 2024;7:e56055

Rates of Attrition and Dropout in App-Based Interventions for Chronic Disease: Systematic Review and Meta-Analysis

Rates of Attrition and Dropout in App-Based Interventions for Chronic Disease: Systematic Review and Meta-Analysis

This has led to a large body of research focusing on how to prevent and manage these diseases, with many recommendations now advising moving from a model of care that is medically focused to patient-centric and community-focused care [3]. However, there are many difficulties in implementing programs for chronic disease prevention and care, in particular, the challenges posed by catering to a large, diverse, and growing population of people requiring these services [1,4].

Gideon Meyerowitz-Katz, Sumathy Ravi, Leonard Arnolda, Xiaoqi Feng, Glen Maberly, Thomas Astell-Burt

J Med Internet Res 2020;22(9):e20283

Self-Reflected Well-Being via a Smartphone App in Clinical Medical Students: Feasibility Study

Self-Reflected Well-Being via a Smartphone App in Clinical Medical Students: Feasibility Study

A total of 29 students voluntarily applied for the study, and all 29 students met the inclusion criteria. As a result, all 29 students were enrolled in the study. The inclusion criteria included (1) The ownership of a personal Android or apple smartphone device; (2) Enrolled at the Dunedin School of Medicine in the bachelor of medicine and surgery (MBCh B) degree; (3) Were currently in their 4th, 5th, or 6th year of the undergraduate MBCh B program; and (4) Currently undertaking a clinical placement.

Elizabeth K Kristal Berryman, Daniel J Leonard, Andrew R Gray, Ralph Pinnock, Barry Taylor

JMIR Med Educ 2018;4(1):e7

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