AIMC Topic: Psychiatric Status Rating Scales
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Accuracy of automated classification of major depressive disorder as a function of symptom severity.
NeuroImage. Clinical
Jul 27, 2016
BACKGROUND: Growing evidence documents the potential of machine learning for developing brain based diagnostic methods for major depressive disorder (MDD). As symptom severity may influence brain activity, we investigated whether the severity of MDD ...
Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features.
Schizophrenia research
Jul 15, 2016
Recently, an increasing number of researchers have endeavored to develop practical tools for diagnosing patients with schizophrenia using machine learning techniques applied to EEG biomarkers. Although a number of studies showed that source-level EEG...
Stress Detection Using Wearable Physiological and Sociometric Sensors.
International journal of neural systems
May 16, 2016
Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physio...
Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion.
Journal of child psychology and psychiatry, and allied disciplines
Apr 19, 2016
BACKGROUND: Machine learning (ML) provides novel opportunities for human behavior research and clinical translation, yet its application can have noted pitfalls (Bone et al., 2015). In this work, we fastidiously utilize ML to derive autism spectrum d...
Prediction of psychosis using neural oscillations and machine learning in neuroleptic-naïve at-risk patients.
The world journal of biological psychiatry : the official journal of the World Federation of Societies of Biological Psychiatry
Oct 9, 2015
OBJECTIVES: This study investigates whether abnormal neural oscillations, which have been shown to precede the onset of frank psychosis, could be used towards the individualised prediction of psychosis in clinical high-risk patients.
Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy.
International journal of methods in psychiatric research
May 21, 2015
The study objective was to apply machine learning methodologies to identify predictors of remission in a longitudinal sample of 296 adults with a primary diagnosis of obsessive compulsive disorder (OCD). Random Forests is an ensemble machine learning...
Screening Internet forum participants for depression symptoms by assembling and enhancing multiple NLP methods.
Computer methods and programs in biomedicine
Apr 1, 2015
Depression is a disease that can dramatically lower quality of life. Symptoms of depression can range from temporary sadness to suicide. Embarrassment, shyness, and the stigma of depression are some of the factors preventing people from getting help ...
Diagnostic classification of specific phobia subtypes using structural MRI data: a machine-learning approach.
Journal of neural transmission (Vienna, Austria : 1996)
Jul 19, 2014
While neuroimaging research has advanced our knowledge about fear circuitry dysfunctions in anxiety disorders, findings based on diagnostic groups do not translate into diagnostic value for the individual patient. Machine-learning generates predictiv...
Phenomenological psychopathology meets machine learning: A multicentric retrospective study (Mu.St.A.R.D.) targeting the role of Aberrant Salience assessment in psychosis detection.
Schizophrenia research
Jul 1, 2025
BACKGROUND: The Aberrant Salience (AS) model conceptualizes psychosis onset as the altered attribution of salience to neutral stimuli. The Aberrant Salience Inventory (ASI), a psychometric tool, measures this phenomenon. This study utilized a multi-c...
Concise multi-class anxiety disorder risk assessment: A novel advanced machine learning approach.
Journal of anxiety disorders
Jun 1, 2025
Rapidly assessing anxiety disorder risk is crucial for effective mental health screen and intervention. However, traditional survey tools such as DASS-42 are time-consuming in responding and scoring. We used a novel advanced machine learning approach...
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