Accuracy of machine learning methods in predicting prognosis of patients with psychotic spectrum disorders: a systematic review.

Abstract

We aimed to examine the predictive accuracy of functioning, relapse or remission among patients with psychotic disorders, using machine learning methods. We also identified specific features that were associated with these clinical outcomes. The methodology of this review was guided by the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. CINAHL, EMBASE, PubMed, PsycINFO, Scopus and ScienceDirect were searched for relevant articles from database inception until 21 November 2024. Studies were included if they involved the use of machine learning methods to predict functioning, relapse and/or remission among individuals with psychotic spectrum disorders. Two independent reviewers screened the records from the database search. Risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies tool from Cochrane. Synthesised findings were presented in tables. 23 studies were included in the review, which were mostly conducted in the west (91%). Predictive summary area under the curve values for functioning, relapse and remission were 0.63-0.92 (poor to outstanding), 0.45-0.95 (poor to outstanding), 0.70-0.79 (acceptable), respectively. Logistic regression and random forest were the best performing algorithms. Factors influencing outcomes included demographic (age, ethnicity), illness (duration of untreated illness, types of symptoms), functioning (baseline functioning, interpersonal relationships and activity engagement), treatment variables (use of higher doses of antipsychotics, electroconvulsive therapy), data from passive sensor (call log, distance travelled, time spent in certain locations) and online activities (time of use, use of certain words, changes in search frequencies and length of queries). Machine learning methods show promise in the prediction of prognosis (specifically functioning, relapse and remission) of mental disorders based on relevant collected variables. Future machine learning studies may want to focus on the inclusion of a broader swathe of variables including ecological momentary assessments, with a greater amount of good quality big data covering longer longitudinal illness courses and coupled with external validation of study findings. The review was registered on PROSPERO, ID: CRD42023441108.