Towards Clustering Human Behavioral Patterns based on Digital Phenotyping

Mental health professionals use clinical evidence and information self-reported by patients for diagnosing mental disorders. However, self-reports and questionnaires are approaches affected by cognitive biases due to imprecision in the report. Machine learning has been used along with sensor data em...

Full description

Saved in:
Bibliographic Details
Main Authors: Filho, Jose Daniel P. Ribeiro, Teles, Ariel S., Silva, Francisco J.S., Coutinho, Luciano R.
Format: Conference Proceeding
Language:English
Subjects:
Clustering algorithms
Digital Phenotyping
Group Analysis
Heuristic algorithms
Machine learning
Machine learning algorithms
Mental disorders
Mental health
Object recognition
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Mental health professionals use clinical evidence and information self-reported by patients for diagnosing mental disorders. However, self-reports and questionnaires are approaches affected by cognitive biases due to imprecision in the report. Machine learning has been used along with sensor data embedded in mobile devices (e.g., smartphones) to identify patterns correlated to mental disorders. This study proposes a solution that uses a clustering algorithm to analyze group behaviors over time. We believe that our solution can help mental health professionals to continuously monitor patients, identify similar behaviors, and changes in behaviors. We present an analysis of different clustering algorithms and show group dynamics. We conclude that the Birch algorithm has the best performance for grouping behaviors in our experiments.
ISSN:2372-9198
DOI:10.1109/CBMS52027.2021.00076