Clinical trial · Observational
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
NCT04883879CI-TRIAL-00051771unknownClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| End Stage Cancer | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
No intervention recorded.
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Specificity and Sensitivity of using Artificial Intelligence based models for prediction of Clinical Outcomes of End-stage Cancer Patients using actigraphy data
- timeFrame
- 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
- description
- 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.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 20 Years
Show eligibility criteria text
Inclusion Criteria: * Participants aged 20 years or older admitted to the hospice care unit at Taipei Medical University Hospital * Participants diagnosed with at least one end-stage solid tumor diseases * Participants consented to receive hospice care Exclusion Criteria: * Participants aged below 20 years of age * Participants diagnosed with leukemia or carcinoma of unknown primary * Participants with evident signs of approaching death upon admission * Participants with no vital signs upon admission * Participants who continued to receive aggressive treatment despite admission to the hospice care unit
References
Publications (1)
- DERIVEDYang TY, Kuo PY, Huang Y, Lin HW, Malwade S, Lu LS, Tsai LW, Syed-Abdul S, Sun CW, Chiou JF. Deep-Learning Approach to Predict Survival Outcomes Using Wearable Actigraphy Device Among End-Stage Cancer Patients. Front Public Health. 2021 Dec 9;9:730150. doi: 10.3389/fpubh.2021.730150. eCollection 2021. PMID 34957004