Jon Nolan Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
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Biography and Research Information
OverviewAI-generated summary
Jon Nolan's research focuses on the application of machine learning to predict mental health crises using electronic health records. He has investigated the use of machine learning models for this purpose, with publications in 2021 and 2022 detailing these efforts. His work also includes an analysis of insomnia as a predictor of treatment engagement and symptom change in veterans with PTSD symptoms and hazardous alcohol use, stemming from a web-based Cognitive Behavioral Therapy intervention. Additionally, Nolan has explored a pilot study for a career mentoring program aimed at juveniles. His scholarship metrics include an h-index of 6, with 15 total publications and 265 citations. He has collaborated with researchers from the University of Arkansas for Medical Sciences and within the University of Central Arkansas.
Metrics
- h-index: 6
- Publications: 15
- Citations: 276
Selected Publications
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Pilot study of career mentoring program for juveniles. (2022)
Collaboration Network
Top Collaborators
- Machine learning model to predict mental health crises from electronic health records
- Machine learning model to predict mental health crisis from electronic health records
- Machine learning model to predict mental health crises from electronic health records
- Machine learning model to predict mental health crisis from electronic health records
- Machine learning model to predict mental health crises from electronic health records
- Machine learning model to predict mental health crisis from electronic health records
- Machine learning model to predict mental health crises from electronic health records
- Machine learning model to predict mental health crisis from electronic health records
- Machine learning model to predict mental health crises from electronic health records
- Machine learning model to predict mental health crisis from electronic health records
- Machine learning model to predict mental health crisis from electronic health records
- Insomnia predicts treatment engagement and symptom change: a secondary analysis of a web-based CBT intervention for veterans with PTSD symptoms and hazardous alcohol use
- Insomnia predicts treatment engagement and symptom change: a secondary analysis of a web-based CBT intervention for veterans with PTSD symptoms and hazardous alcohol use
- Insomnia predicts treatment engagement and symptom change: a secondary analysis of a web-based CBT intervention for veterans with PTSD symptoms and hazardous alcohol use
- Insomnia predicts treatment engagement and symptom change: a secondary analysis of a web-based CBT intervention for veterans with PTSD symptoms and hazardous alcohol use
- Insomnia predicts treatment engagement and symptom change: a secondary analysis of a web-based CBT intervention for veterans with PTSD symptoms and hazardous alcohol use
- Insomnia predicts treatment engagement and symptom change: a secondary analysis of a web-based CBT intervention for veterans with PTSD symptoms and hazardous alcohol use
- Machine learning model to predict mental health crises from electronic health records
- Robotic Task Sequencing and Motion Coordination for Multiarm Systems
- Robotic Task Sequencing and Motion Coordination for Multiarm Systems
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