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
Study Overview
Status
Status
Conditions
Conditions
Detailed Description
Study Type
Study Type
Enrollment (Anticipated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Shabbir Syed-Abdul, PhD
- Phone Number: 1514 886 2-6638-2736
- Email: drshabbir@tmu.edu.tw
Study Locations
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TW - Taiwan
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Taipei City, TW - Taiwan, Taiwan, 110
- Recruiting
- Taipei Medical University
-
Contact:
- Shabbir Syed Abdul
- Phone Number: 1501 +886-2-66382736
- Email: drshabbir@tmu.edu.tw
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
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
Study Plan
How is the study designed?
Design Details
- Observational Models: Cohort
- Time Perspectives: Prospective
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Specificity and Sensitivity of using Artificial Intelligence based models for prediction of Clinical Outcomes of End-stage Cancer Patients using actigraphy data
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
|
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.
|
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
|
Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Investigators
Investigators
- Principal Investigator: Shabbir Syed-Abdul, PhD, Taipei Medical University
Publications and helpful links
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Anticipated)
Primary Completion
Study Completion (Anticipated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Keywords
Other Study ID Numbers
Other Study ID Numbers
- N201910041
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
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