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A Deep Learning–Enabled Workflow to Estimate Real-World Progression-Free Survival in Patients With Metastatic Breast Cancer: Study Using Deidentified Electronic Health Records

A Deep Learning–Enabled Workflow to Estimate Real-World Progression-Free Survival in Patients With Metastatic Breast Cancer: Study Using Deidentified Electronic Health Records

Nonprogressed patients showed greater mean peak reduction (5.57, SD 5.90 vs 2.95, SD 4.30; t92=−2.397, P=.02) and cumulative decline (mean 8.00, SD 12.68 vs 3.66, SD 6.40; t92=−2.201, P=.03) in PHQ-8 scores compared to progressed patients. See Table S3 in Multimedia Appendix 1 for details. The study showcases the development and validation of a novel semiautomated workflow for estimating rw PFS in patients with m BC using deidentified EHRs.

Gowtham Varma, Rohit Kumar Yenukoti, Praveen Kumar M, Bandlamudi Sai Ashrit, K Purushotham, C Subash, Sunil Kumar Ravi, Verghese Kurien, Avinash Aman, Mithun Manoharan, Shashank Jaiswal, Akash Anand, Rakesh Barve, Viswanathan Thiagarajan, Patrick Lenehan, Scott A Soefje, Venky Soundararajan

JMIR Cancer 2025;11:e64697

Prevalence of Multiple Chronic Conditions Among Adults in the All of Us Research Program: Exploratory Analysis

Prevalence of Multiple Chronic Conditions Among Adults in the All of Us Research Program: Exploratory Analysis

We used P This study analyzed deidentified data from participants enrolled in the Ao U Research Program. Participants in the Ao U Research Program were recruited either in-person at Ao U enrollment sites or on the Ao U web-based portal [12]. Participants gave informed consent when they joined the program, agreeing that their anonymized data could be used broadly for biomedical research [12].

Xintong Li, Caitlin Dreisbach, Carolina M Gustafson, Komal Patel Murali, Theresa A Koleck

JMIR Form Res 2025;9:e69138

Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a Japanese Population–Based Study

Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a Japanese Population–Based Study

The IMT of CCAs, IMT_c Max, was also significantly higher in patients with CHD (1.10 mm vs 1.00 mm; P BMI and waist circumference were also higher in participants with CHD, indicating a greater degree of obesity. Additionally, lipid profiles showed significant differences, with lower HDL-c levels and higher non-HDL-c and triglyceride levels in patients with CHD. Higher glucose levels and white blood cell counts were observed in participants with CHD, along with elevated hemoglobin levels.

Thien Vu, Yoshihiro Kokubo, Mai Inoue, Masaki Yamamoto, Attayeb Mohsen, Agustin Martin-Morales, Research Dawadi, Takao Inoue, Jie Ting Tay, Mari Yoshizaki, Naoki Watanabe, Yuki Kuriya, Chisa Matsumoto, Ahmed Arafa, Yoko M Nakao, Yuka Kato, Masayuki Teramoto, Michihiro Araki

JMIR Cardio 2025;9:e68066

Machine Learning Clinical Decision Support for Interdisciplinary Multimodal Chronic Musculoskeletal Pain Treatment: Prospective Pilot Study of Patient Assessment and Prognostic Profile Validation

Machine Learning Clinical Decision Support for Interdisciplinary Multimodal Chronic Musculoskeletal Pain Treatment: Prospective Pilot Study of Patient Assessment and Prognostic Profile Validation

Profile accuracy: H=high, M=medium, L=low. AUC: area under the curve; M: mixed; N: negative; P: positive; TPR: true-positive rate; TNR: true-negative rate. The above summary (Figure 2) presents results for all pilot study patients to show performance and overall results. However, the individual prognostic patient profile as used in IMPT clinical assessment provides clearly presented summary results for each patient.

Fredrick Zmudzki, Rob J E M Smeets, Jan S Groenewegen, Erik van der Graaff

JMIR Rehabil Assist Technol 2025;12:e65890