Authors:
Simone Marini
;
Arianna Dagliati
;
Lucia Sacchi
and
Riccardo Bellazzi
Affiliation:
University of Pavia, Italy
Keyword(s):
Type 2 Diabetes, Continuous Time Bayesian Network, Cohort Modeling, Disease Complexity.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Electronic Health Records and Standards
;
Enterprise Information Systems
;
Health Information Systems
;
Pattern Recognition and Machine Learning
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Predicting the complexity level (i.e. the number of complications and their related hospitalizations) in a T2D
cohort is a critical step in prevention, resource optimization and overall patient management. Our data set
was obtained by monitoring a T2D diabetic cohort along up to 10 years through electronic medical records
of a local healthcare agency data warehouse. In order to conveniently handle temporarily sparse data, we
designed a model describing the cohort evolution with Continuous Time Bayesian Networks (CTBN). The
network structure and its parameters are entirely data driven. Compared to traditional Bayesian Networks,
CTBNs admit cycles. As consequence, CTBNs fit the complexity of chronic metabolic syndromes where
variables show a reciprocal influence. Network nodes represent metabolic (glycated hemoglobin, lipid
profile (cholesterol, triglycerides), and biometric (BMI) data. We observed how these variables directly or
indirectly affect the disease level of complexity, and h
ow the variables influence the cumulative adverse
events a patient undergoes.
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