Authors:
Ariella Richardson
1
;
Avigail Perl
1
;
Sapir Natan
1
and
Gil Segev
2
Affiliations:
1
Lev Academic Center, Jerusalem and Israel
;
2
BGSegev Ltd. (segevlabs.org), Jerusalem and Israel
Keyword(s):
Mobile Health, Digital Health, Digital Monitoring, Clinical Decision Support System (CDSS), Medical Decision Support System (MDSS), Cardiovascular Disease, Silent Disease.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Health Engineering and Technology Applications
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
Clinical decision support systems typically rely on medical records and information collected in the doctor’s office. We propose a clinical decision support system that uses data collected from patients continuously and in an unobtrusive manner. The system uses data collected from a mobile app installed on the patient’s device (such as a mobile phone, smart-watch etc). The app collects data without user interference and combines it with conventional medical records. Our system uses machine learning methods to extract meaningful insights from the data. The output from the learning process is then presented to the doctor in a clear and meaningful fashion on a web based platform. This system can be used to assist effective treatment selection, enable early diagnosis, trigger alarms in case of an emergency and provide a tool for disease monitoring. We describe our clinical decision support system and directions for future work.