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Caregiving Artificial Intelligence Chatbot for Older Adults and Their Preferences, Well-Being, and Social Connectivity: Mixed-Method Study

Caregiving Artificial Intelligence Chatbot for Older Adults and Their Preferences, Well-Being, and Social Connectivity: Mixed-Method Study

When introduced to other benefits of using AI, Clarence pondered, Oh, like reminders for medications and workout schedules and meal suggestions. I didn’t realize that that stuff was out there, but I pretty much eat salad and chicken and that’s, you know, fruits and vegetables, healthy diet.

Brooke H Wolfe, Yoo Jung Oh, Hyesun Choung, Xiaoran Cui, Joshua Weinzapfel, R Amanda Cooper, Hae-Na Lee, Rebecca Lehto

J Med Internet Res 2025;27:e65776

Understanding Morning Emotions by Analyzing Daily Wake-Up Alarm Usage: Longitudinal Observational Study

Understanding Morning Emotions by Analyzing Daily Wake-Up Alarm Usage: Longitudinal Observational Study

Similarly, Oh et al [15] highlighted the necessity of considering morning contexts to comprehend changes in morning behavior. This study aimed to explore the contextual factors that affect morning emotions. To achieve this, we analyzed alarm usage logs to evaluate the contextual information surrounding the moment of waking up. An alarm usage log reflects various aspects of waking behavior, such as the time and regularity of waking.

Kyue Taek Oh, Jisu Ko, Nayoung ­Jin, Sangbin Han, Chan Yul Yoon, Jaemyung Shin, Minsam Ko

JMIR Hum Factors 2024;11:e50835

Development and Validation of a Prediction Model Using Sella Magnetic Resonance Imaging–Based Radiomics and Clinical Parameters for the Diagnosis of Growth Hormone Deficiency and Idiopathic Short Stature: Cross-Sectional, Multicenter Study

Development and Validation of a Prediction Model Using Sella Magnetic Resonance Imaging–Based Radiomics and Clinical Parameters for the Diagnosis of Growth Hormone Deficiency and Idiopathic Short Stature: Cross-Sectional, Multicenter Study

Oh et al [30] showed that extreme gradient boosting (XGBoost) can precisely estimate low-density lipoprotein cholesterol, a therapeutic target for dyslipidemia, using large-scale electronic health records. A light gradient boosting machine could predict cardiac arrest within 24 hours by training on heart rate variability calculated from electrocardiograms in the intensive care unit [31].

Kyungchul Song, Taehoon Ko, Hyun Wook Chae, Jun Suk Oh, Ho-Seong Kim, Hyun Joo Shin, Jeong-Ho Kim, Ji-Hoon Na, Chae Jung Park, Beomseok Sohn

J Med Internet Res 2024;26:e54641