NCT04883879

Artificial Intelligence-based Mortality Prediction Among Cancer Patients in the Hospice Ward

Artificial Intelligence-based Activity Recognition and Mortality Prediction Using Circadian Rhythm, Among Cancer Patients in the Hospice Ward

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 80 (estimated)
• Sponsor: Taipei Medical University · Academic / Other
• Sex: All
• Age: 20 Years
• Healthy volunteers: Not accepted

Summary

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.

Detailed description

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.

Conditions

• End Stage Cancer

Timeline

• Start date: 2019-12-11
• Primary completion: 2021-08-31
• Completion: 2021-12-31
• First posted: 2021-05-12
• Last updated: 2021-05-12

Locations

1 site across 1 country: Taiwan