Towards Constructing a New Taxonomy for Psychiatry Using Self-reported Symptoms.
The Diagnostic and Statistical Manual (DSM) has served as the gold standard for psychiatric diagnosis for the past several decades in the USA, and DSM diagnoses mirror mental health and substance abuse diagnoses in ICD-9 and ICD-10. However, DSM diagnoses have severe limitations when used as phenotypes for studies of the pathophysiology underlying mental disorders, as well as for clinical treatment and research. In this paper, we use a novel approach of deconstructing DSM diagnostic criteria, and using expert knowledge to inform feature selection for unsupervised machine learning. We are able to identify clusters of symptoms that stratify subjects with the same DSM disorders into cohorts with increased clinical and biological homogeneity. These findings suggest that itemized self-report symptom data should inform a new taxonomy for psychiatry, and will enhance the bi-directional translation of knowledge from the bench to the clinic through a common terminology.
Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA.
Jessica
J
Ross
Department of Psychiatry, University of California, San Francisco and San Francisco VA Medical Center, San Francisco, CA, USA.
Thomas
T
Neylan
Department of Psychiatry, University of California, San Francisco and San Francisco VA Medical Center, San Francisco, CA, USA.
Michael
M
Weiner
Department of Radiology, University of California, San Francisco and San Francisco VA Medical Center, San Francisco, USA.
Linda
L
Chao
San Francisco VA Medical Center, San Francisco, CA, USA e Alliant International University, San Francisco, CA, USA.
Kristin
K
Samuelson
Division of General Internal Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
Ida
I
Sim
D006801:Humans; D001523:Mental Disorders / Q000145:classification*; D009323:Natural Language Processing; D011570:Psychiatry / Q000145:classification; D012495:San Francisco; D057566:Self Report; D063189:Symptom Assessment / Q000379:methods*; D009626:Terminology as Topic; D000069558:Unsupervised Machine Learning; D018875:Vocabulary, Controlled