Towards Clustering Human Behavioral Patterns based on Digital Phenotyping

Autor(es):Mostrar menos -

Ribeiro Filho, Jose Daniel P. ; Teles, Ariel S. ; Silva, Francisco J. S. ; Coutinho, Luciano R. ; Almeida, JR ; Gonzalez, AR ; Shen, L ; Kane, B ; Traina, A ; Soda, P ; Oliveira, JL

• Tipo de documento: Artigo Científico
• Fonte: 2021 IEEE 34TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS); v. N/A, p. 6-pg., 2021-01-01.

Resumo

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. (AU)

• Processo FAPESP: 15/24485-9 - Internet do futuro aplicada a cidades inteligentes
• Beneficiário: Fabio Kon
• Modalidade de apoio: Auxílio à Pesquisa - Temático