Clinical Knowledge Dissemination
A collection of projects using a highly engineered clinical decision support system:
PROJECT 1
Client Decision Support System
Machine Learning & Qualitative Study of Clinical Decision Support Systems
Machine Learning and Artificial Intelligence are fast emerging as game-changers in healthcare. However, the impact of these emerging technologies on clinical outcomes is still limited, mostly due to design and adoption factors. There is a widening rift between clinicians and health IT professionals.
Bell’s research involves exploring the entire circle of health IT from design to implementation and finally, adoption by the clinical community. Bell strives to bridge the gap between software engineering and clinical medicine.
Publications
LesionMap: A Method and Tool for the Semantic Annotation of Dermatological Lesions for Documentation and Machine Learning
B. Eapen, N. Archer, K. Sartipi | February 2021 | Journal of JMIR Dermatol 2020
Serverless On FHIR: Deploying Machine Learning Models For Healthcare On The Cloud
B. Eapen, K. Sartipi, N. Archer | May 2020 | ArXiv.org
Research Team
Dr. Norm Archer
PROFESSOR EMERITUS
Information systems
DEGROOTE SCHOOL OF BUSINESS
MCMASTER UNIVERSITY
Dr. Brian Deltor
Professor, Area Chair
INFORMATION SYSTEMS
DEGROOTE SCHOOL OF BUSINESS
MCMASTER UNIVERSITY
PROJECT 2
Medical Informatics
Machine Learning to Provide Intelligent and Customizable Digital Health to Assist Physicians in Underserved Regions
The proposed project aims at providing advanced information intelligence techniques to allow the physicians in rural and underserved regions to enhance their quality of care through readily available intelligent decision systems that utilize mined knowledgebases of medical specialties.
A pilot project with collaboration of Division of Gastroenterology and Department of Family Medicine at McMaster University will provide the data for a personalized digital health datacenter at McMaster University
Project details
There are three sources of medical knowledge that will be used to create a heterogeneous specialty knowledgebase:
- Anonymized specialty patient data will be extracted from electronic health records to supply different data mining and machine learning analyses
- Research and statistical datasets generated by medical researchers and hospitals provide evidenced-based meta-data to annotate knowledge graph nodes
- Expert decisions (diagnosis, treatments, prescriptions) made by medical specialists will provide a rich and authenticated knowledgebase
This specialty knowledgebase is formed as an ontology graph of highly related group of diseases and their shared symptoms. The pattern discovery step utilizes different data mining techniques and machine learning techniques. The intelligent consultant services (ICS) will provide personalized medical information during the patient visit in real-time.
Our research team
GASTROENTEROLOGY, DEPARTMENT OF MEDICINE, MCMASTER UNIVERSITY
Dr. David Armstrong
PROFESSOR
DIvision of Gastroenterology
Department of medicine
McMaster University
Dr. Smita Halder
ASSOCIATE PROFESSOR
DIVISION OF GASTROENTEROLOGY
DEPARTMENT OF MEDICINE
MCMASTER UNIVERSITY
Dr. Kamran Sartipi
Adjunct associate professor
Information systems
DEGROOTE SCHOOL OF BUSINESS
McMaster University
Dr. Henry Siu
ASSISTANT PROFESSOR
DIvision of Gastroenterology
Department of FAMILY medicine
McMaster University
Other projects
Intelligent Home Monitoring
A multi-disciplinary research initiative on Information and System Intelligence research.