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Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study

Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study

The x-axis represents the predicted probability of delirium as output by our model, whereas the y-axis represents the observed frequency of delirium in the validation cohorts. The black dashed line indicates perfect calibration, in which the predicted probabilities exactly match the observed outcomes. In general, the prediction score concomitantly increased with positive CAM-ICU results, signaling the onset of delirium (Figure 4).

Chanmin Park, Changho Han, Su Kyeong Jang, Hyungjun Kim, Sora Kim, Byung Hee Kang, Kyoungwon Jung, Dukyong Yoon

J Med Internet Res 2025;27:e59520

Effect of the Yon PD App on the Management of Self-Care in People With Parkinson Disease: Randomized Controlled Trial

Effect of the Yon PD App on the Management of Self-Care in People With Parkinson Disease: Randomized Controlled Trial

Ethical approval was obtained from the Institute Review Board of Yonsei University Health System Human Research Protection Center (Y-2020-0220). The hospital where this study was conducted belongs to the institution that granted ethical approval. Researchers explained the purpose and process of the study to eligible participants and obtained informed consent from each participant. Participants were guaranteed the right to withdraw, anonymity, and confidentiality of the collected data.

JuHee Lee, Subin Yoo, Yielin Kim, Eunyoung Kim, Hyeran Park, Young H Sohn, Yun Joong Kim, Seok Jong Chung, Kyoungwon Baik, Kiyeon Kim, Jee-Hye Yoo

J Med Internet Res 2025;27:e62822