Predicting Affect Classification in Mental Status Examination Using Machine Learning Face Action Recognition System
A Pilot Study in Schizophrenia Patients
Bibliographic Data
| ID | 15525329 |
|---|---|
| Authors | Ran Barzilay (0000-0002-3247-2331, Lifespan), Nadav Israel (Tel Aviv University), Amir Krivoy (0000-0003-2218-3250, Tel Aviv University), Roi Sagy (Geha Mental Health Center), Shiri Kamhi-Nesher (Geha Mental Health Center), Oren Loebstein (Geha Mental Health Center), Lior Wolf (0000-0001-5578-8892, Tel Aviv University), Gal Shoval (0000-0003-1034-3640, Geha Mental Health Center, corresponding author) |
| Year | 2019 |
| Volume | 10 |
| Pages | 288-288 |
| Publication date | 2019-05-06 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Psychiatry (JOURNAL) |
| Journal identifiers | ISSN: 1664-0640 • E-ISSN: 1664-0640 |
| Publisher | Frontiers Media (PUBLISHER • CH) |
| DOI | 10.3389/fpsyt.2019.00288 |
| PMID | 31133892 |
| OpenAlex | W2944204681 |
| Language | EN |
| Citations received | 1 |
| References cited | 17 |
Classifying patients' affect is a pivotal part of the mental status examination. However, this common practice is often widely inconsistent between raters. Recent advances in the field of Facial Action Recognition (FAR) have enabled the development of tools that can act to identify facial expressions from videos. In this study, we aimed to explore the potential of using machine learning techniques on FAR features extracted from videotaped semi-structured psychiatric interviews of 25 male schizophrenia inpatients (mean age 41.2 years, STD = 11.4). Five senior psychiatrists rated patients' affect based on the videos. Then, a novel computer vision algorithm and a machine learning method were used to predict affect classification based on each psychiatrist affect rating. The algorithm is shown to have a significant predictive power for each of the human raters. We also found that the eyes facial area contributed the most to the psychiatrists' evaluation of the patients' affect. This study serves as a proof-of-concept for the potential of using the machine learning FAR system as a clinician-supporting tool, in an attempt to improve the consistency and reliability of mental status examination
Action (physics · Affect (linguistics · Consistency (knowledge bases · Machine learning · Power (physics · Psychiatry · Reliability (semiconductor · Schizophrenia (object-oriented programming · Clinical Psychology · Communication · Computer Science · Mental Health Research Topics · Mental Health Treatment and Access · Psychology · Schizophrenia research and treatment · Artificial Intelligence
| Citations per year | 0.17 |
|---|---|
| Citation span | 2020 - 2020 (1) |
| Highly cited | No |