Publications by authors named "Ana Caroline Lopes-Rocha"
Natural language processing in at-risk mental states: enhancing the assessment of thought disorders and psychotic traits with semantic dynamics and graph theory.
Braz J Psychiatry
July 2024
Article Synopsis
- The study explores the relationship between verbal communication and mental health evaluation, particularly in early psychosis, using natural-language-processing (NLP) techniques.
- Researchers analyzed speech from individuals at risk of psychosis and a control group, identifying various NLP features that correlate with psychotic symptoms.
- Findings suggest that subtle speech impairments can effectively indicate mental health risks, proposing a new framework for using speech analysis in clinical assessments.
Article Synopsis
- The study developed a machine learning model to differentiate individuals with "at-risk mental states for psychosis" (ARMS) from healthy controls using facial data from video recordings.
- It involved 58 ARMS subjects and 70 healthy individuals, examining 649 facial features extracted from short videos filmed during a structured interview.
- The final model achieved strong performance metrics, including an 83% mean F1-score and a 93% area under the curve, indicating its potential utility for screening in low-resource environments.
Nonverbal communication (NVC) is a complex behavior that involves different modalities that are impaired in the schizophrenia spectrum, including gesticulation. However, there are few studies that evaluate it in individuals with at-risk mental states (ARMS) for psychosis, mostly in developed countries. Given our prior findings of reduced movement during speech seen in Brazilian individuals with ARMS, we now aim to determine if this can be accounted for by reduced gesticulation behavior.
View Article and Find Full Text PDFBackground: The clinical high-risk for psychosis (CHR) paradigm is one of the best studied preventive paradigms in psychiatry. However, most studies have been conducted in high-income countries. It is unclear if knowledge from such countries applies to low and middle-income countries (LAMIC), and if there are specific limitations hindering CHR research there.
View Article and Find Full Text PDFRecent developments in artificial intelligence technologies have come to a point where machine learning algorithms can infer mental status based on someone's photos and texts posted on social media. More than that, these algorithms are able to predict, with a reasonable degree of accuracy, future mental illness. They potentially represent an important advance in mental health care for preventive and early diagnosis initiatives, and for aiding professionals in the follow-up and prognosis of their patients.
View Article and Find Full Text PDFMovement abnormalities are commonly observed in schizophrenia and at-risk mental states (ARMS) for psychosis. They are usually detected with clinical interviews, such that automated analysis would enhance assessment. Our aim was to use motion energy analysis (MEA) to assess movement during free-speech videos in ARMS and control individuals, and to investigate associations between movement metrics and negative and positive symptoms.
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