The purpose of this study is to develop a novel deep-learning-based survival prediction model employing patient activity data recorded by a wearable device.
This study aims to develop a deep-learning-based survival prediction model that utilizes patient movement data upon admission to predict their clinical outcomes: either death or discharge with stable condition. Objective data of the patients are recorded by a wearable device and documented as parameters of physical activity, angle, and spin. In addition to objective data, the investigators also document patients' Karnofsky Performance Status assessed subjectively by clinical doctors. Finally, the investigators aim to explore and describe the applicability, potential, and limitations of the survival prediction model based on patient movement data as a simple prognostic parameter in clinical settings.
Study Type
OBSERVATIONAL
Enrollment
80
Taipei Medical University
Taipei, TW - Taiwan, Taiwan
RECRUITINGSpecificity and Sensitivity of using Artificial Intelligence based models for prediction of Clinical Outcomes of End-stage Cancer Patients using actigraphy data
The primary outcome of the study will be to evaluate whether the analysis of the movement data captured using actigraphy device can help to predict clinical outcomes either deceased or discharged alive from hospital, with a high specificity and sensitivity, using Artificial Intelligence based prediction modelling.
Time frame: From date of admission to hospice ward until the date of first documented discharge from hospital or date of death from any cause, whichever came first, assessed up to 1 month
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