Back to Search
Start Over
Data-based Decision Rules to Personalize Depression Follow-up
- Source :
- Scientific Reports, Scientific Reports, Vol 8, Iss 1, Pp 1-8 (2018)
- Publication Year :
- 2018
- Publisher :
- Nature Publishing Group UK, 2018.
-
Abstract
- Depression is a common mental illness with complex and heterogeneous progression dynamics. Risk grouping of depression treatment population based on their longitudinal patterns has the potential to enable cost-effective monitoring policy design. This paper establishes a rule-based method to identify a set of risk predictive patterns from person-level longitudinal disease measurements by integrating the data transformation, rule discovery and rule evaluation. We further extend the identified rules to create rule-based monitoring strategies to adaptively monitor individuals with different disease severities. We applied the rule-based method on an electronic health record (EHR) dataset of depression treatment population containing person-level longitudinal Patient Health Questionnaire (PHQ)-9 scores for assessing depression severity. 12 risk predictive rules are identified, and the rule-based prognostic model based on identified rules enables more accurate prediction of disease severity than other prognostic models including RuleFit, logistic regression and Support Vector Machine. Two rule-based monitoring strategies outperform the latest PHQ-9 based monitoring strategy by providing higher sensitivity and specificity. The rule-based method can lead to a better understanding of disease dynamics, achieving more accurate prognostics of disease progressions, personalizing follow-up intervals, and designing cost-effective monitoring of patients in clinical practice.
- Subjects :
- Male
Databases, Factual
Computer science
Cost-Benefit Analysis
Disease
computer.software_genre
Logistic regression
0302 clinical medicine
Risk Factors
Electronic Health Records
030212 general & internal medicine
Longitudinal Studies
Precision Medicine
education.field_of_study
Multidisciplinary
Depression
Middle Aged
Prognosis
Evaluation Studies as Topic
Disease Progression
Prognostics
Medicine
Female
Adult
Science
Population
Data transformation (statistics)
Machine learning
Article
03 medical and health sciences
Disease severity
medicine
Humans
education
Prognostic models
Aged
Monitoring, Physiologic
business.industry
Decision rule
Mental illness
medicine.disease
Decision Support Systems, Clinical
030227 psychiatry
Support vector machine
Patient Health Questionnaire
Logistic Models
Prognostic model
Artificial intelligence
business
computer
Follow-Up Studies
Subjects
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- Scientific Reports
- Accession number :
- edsair.doi.dedup.....501efbc3878758b5eb3fa7b43fbc9549