DOI: 10.1101/19006189Sep 27, 2019Paper

Assessing psychiatric comorbid disorders of cognition: A machine learning approach using UK Biobank data

MedRxiv : the Preprint Server for Health Sciences
C. LiSarah Bauermeister

Abstract

Background Conceptualising comorbidity is complex and the term is used variously. Here, it is the coexistence of two or more diagnoses which might be defined as chronic and, although they may be pathologically related, they may also act independently 1. Of interest here is the comorbidity of common psychiatric disorders and impaired cognition. Objectives To examine whether anxiety and/or depression are important longitudinal predictors of impaired cognition. Methods UK Biobank participants used at three timepoints (n= 502,664): baseline, 1st follow-up (n= 20,257) and 1st imaging study (n=40,199). Participants with no missing data were 1,159 participants aged 40 to 70 years, 41% female. Machine learning (ML) was applied and the main outcome measure of reaction time intraindividual variability (cognition) was used. Findings Using the area under the Receiver Operating Characteristic (ROC) curve, the anxiety model achieves the best performance with an Area Under the Curve (AUC) of 0.68, followed by the depression model with an AUC of 0.64. The cardiovascular and diabetes model, and the demographics model have weaker performance in predicting cognition, with an AUC of 0.60 and 0.57, respectively. Conclusions Outcomes suggest psychia...Continue Reading

Software Mentioned

MATLAB
Dementias Platform UK

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