Ex-ray: Data mining and mental health

Diederich, Joachim, Al-Ajmi, Aqeel and Yellowlees, Peter (2007) Ex-ray: Data mining and mental health. Applied Soft Computing, 7 3: 923-928. doi:10.1016/j.asoc.2006.04.007


Author Diederich, Joachim
Al-Ajmi, Aqeel
Yellowlees, Peter
Title Ex-ray: Data mining and mental health
Formatted title
Ex-ray: Data mining and mental health
Journal name Applied Soft Computing   Check publisher's open access policy
ISSN 1568-4946
1872-9681
Publication date 2007-06
Year available 2006
Sub-type Article (original research)
DOI 10.1016/j.asoc.2006.04.007
Volume 7
Issue 3
Start page 923
End page 928
Total pages 6
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Language eng
Abstract Machine learning techniques such as support vector machines are applied to a text classification task to determine mental health problems. Inputs are transcribed speech samples from a “structured-narrative task” and outputs are psychiatric categories such as schizophrenia. In a preliminary trial, subjects from three groups generated speech samples: those with clinically diagnosed schizophrenia (31 patients), clinically diagnosed mania (16 patients) and controls (9 subjects). Even though the structured narrative task resulted in the use of a limited vocabulary by all subjects (only a total of 1100 different words were used), a classification performance approaching 80% accuracy was achieved for the schizophrenia versus control task. Classification performance at this level indicates that the method is suitable for diagnostic or screening purposes. It is expected that results improve further in experiments utilising free-speech samples. Diagnostic categories in psychiatry can be broad and heterogeneous, e.g. schizophrenia, which includes a range of very different symptoms. In further experiments, clustering techniques are used to extract task-relevant diagnostic categories from psychiatric reports. In these reports, psychiatrists typically include biographic, background and referral information, a description of symptoms and an opinion on treatment recommendations. At the task level, diagnostic reports are written for a specific audience or decision making body. In preliminary experiments, detailed and specific diagnostic categories have been extracted from psychiatric reports by use of unsupervised learning. These categories genuinely reflect the everyday practise of a mental health professional.
Keyword Machine learning
Text classification
Mental health
Psychiatry
Diagnosis
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status Non-UQ
Additional Notes Available online 3 July 2006

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
Centre for Online Health Publications
 
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Created: Thu, 16 Apr 2009, 15:40:44 EST by Ms Sarada Rao on behalf of Centre for On-Line Health