AIMC Topic: Professional-Patient Relations

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COMPASS: Computational mapping of patient-therapist alliance strategies with language modeling.

Translational psychiatry
The therapeutic working alliance is a critical predictor of psychotherapy success. Traditionally, working alliance assessment relies on questionnaires completed by both therapists and patients. In this paper, we present COMPASS, a novel framework to ...

Association of machine-learning-rated supportive counseling skills with psychotherapy outcome.

Journal of consulting and clinical psychology
OBJECTIVE: This study applied a machine-learning-based skill assessment system to investigate the association between supportive counseling skills (empathy, open questions, and reflections) and treatment outcomes. We hypothesized that higher empathy ...

Categorising patient concerns using natural language processing techniques.

BMJ health & care informatics
OBJECTIVES: Patient feedback is critical to identify and resolve patient safety and experience issues in healthcare systems. However, large volumes of unstructured text data can pose problems for manual (human) analysis. This study reports the result...

Generative transfer learning for measuring plausibility of EHR diagnosis records.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Due to a complex set of processes involved with the recording of health information in the Electronic Health Records (EHRs), the truthfulness of EHR diagnosis records is questionable. We present a computational approach to estimate the pro...

Machine learning and natural language processing in psychotherapy research: Alliance as example use case.

Journal of counseling psychology
Artificial intelligence generally and machine learning specifically have become deeply woven into the lives and technologies of modern life. Machine learning is dramatically changing scientific research and industry and may also hold promise for addr...

Developing Machine Learning Models for Behavioral Coding.

Journal of pediatric psychology
OBJECTIVE: The goal of this research is to develop a machine learning supervised classification model to automatically code clinical encounter transcripts using a behavioral code scheme.