Algorithmic fairness in precision psychiatry: analysis of prediction models in individuals at clinical high risk for psychosis.

Şahin, Derya; Kambeitz-Ilankovic, Lana; Wood, Stephen; Dwyer, Dominic; Upthegrove, Rachel; Salokangas, Raimo; Borgwardt, Stefan; Brambilla, Paolo; Meisenzahl, Eva; Ruhrmann, Stephan; Schultze-Lutter, Frauke; Lencer, Rebekka; Bertolino, Alessandro; Pantelis, Christos; Koutsouleris, Nikolaos; Kambeitz, Joseph (2024). Algorithmic fairness in precision psychiatry: analysis of prediction models in individuals at clinical high risk for psychosis. The British journal of psychiatry, 224(2), pp. 55-65. Cambridge University Press 10.1192/bjp.2023.141

Text algorithmic-fairness-in-precision-psychiatry-analysis-of-prediction-models-in-individuals-at-clinical-high-risk-for-psychosis.pdf - Published Version Restricted to registered users only Available under License Publisher holds Copyright. Author holds Copyright Download (448kB)

BACKGROUND

Computational models offer promising potential for personalised treatment of psychiatric diseases. For their clinical deployment, fairness must be evaluated alongside accuracy. Fairness requires predictive models to not unfairly disadvantage specific demographic groups. Failure to assess model fairness prior to use risks perpetuating healthcare inequalities. Despite its importance, empirical investigation of fairness in predictive models for psychiatry remains scarce.

AIMS

To evaluate fairness in prediction models for development of psychosis and functional outcome.

METHOD

Using data from the PRONIA study, we examined fairness in 13 published models for prediction of transition to psychosis (n = 11) and functional outcome (n = 2) in people at clinical high risk for psychosis or with recent-onset depression. Using accuracy equality, predictive parity, false-positive error rate balance and false-negative error rate balance, we evaluated relevant fairness aspects for the demographic attributes 'gender' and 'educational attainment' and compared them with the fairness of clinicians' judgements.

RESULTS

Our findings indicate systematic bias towards assigning less favourable outcomes to individuals with lower educational attainment in both prediction models and clinicians' judgements, resulting in higher false-positive rates in 7 of 11 models for transition to psychosis. Interestingly, the bias patterns observed in algorithmic predictions were not significantly more pronounced than those in clinicians' predictions.

CONCLUSIONS

Educational bias was present in algorithmic and clinicians' predictions, assuming more favourable outcomes for individuals with higher educational level (years of education). This bias might lead to increased stigma and psychosocial burden in patients with lower educational attainment and suboptimal psychosis prevention in those with higher educational attainment.

Interest & Impact

Downloads

1 since deposited on 08 Nov 2023
1 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item