Search Articles

View query in Help articles search

Search Results (1 to 10 of 6079 Results)

Download search results: CSV END BibTex RIS


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

At baseline, routine blood tests were conducted, including measurements of total cholesterol, high-density lipoprotein cholesterol (HDL-c), and fasting glucose levels. Non-HDL-c was calculated by subtracting HDL-c from total cholesterol. Diabetes mellitus was diagnosed if participants had fasting plasma glucose (FPG) ≥126 mg/d L, a non-FPG ≥200 mg/d L, or the use of diabetes mellitus medication.

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

Nonpharmacological Multimodal Interventions for Cognitive Functions in Older Adults With Mild Cognitive Impairment: Scoping Review

Nonpharmacological Multimodal Interventions for Cognitive Functions in Older Adults With Mild Cognitive Impairment: Scoping Review

=0.0001, P=.97) GC (ACE: Cohen d=0.71, P=.002; MMSE: η2=0.189, P=.001) ME (ACE: Cohen d=0.64, P=.007; AVLT: η2=0.173, P=.001) PS (DRT-II: η2=0.033, P=.11) VF (Cohen d=0.73, P=.001) HEaq ATT (TMT-A and TMT-B) GC (ADAS-Cog and KMMSEar) PS (DSST) Group×time interaction ATT—TMT-A: P GC (ADAS-Cog: P=.11); KMMSE (P=.72) PS (P=.02) CT only EF (EFPT-Kas and FABat) EF (EFPT-K: η2=0.132, P CT only PT only SA only ATT (VFT-Category) GC (ADAS-Cog, CDR-SOBau, and CMMSEav) ME (list learning delayed recall test) ATT (VFT-C:

Raffy Chi-Fung Chan, Joson Hao-Shen Zhou, Yuan Cao, Kenneth Lo, Peter Hiu-Fung Ng, David Ho-Keung Shum, Arnold Yu-Lok Wong

JMIR Aging 2025;8:e70291

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