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Psychosis Prognosis Predictor: A continuous and uncertainty-aware prediction of treatment outcome in first-episode psychosis
van Opstal,Daniël P J Kia,Seyed Mostafa Jakob,Lea Somers,Metten Sommer,Iris E C Winter-van Rossum,Inge Kahn,René S Cahn,Wiepke Schnack,Hugo G
van Opstal,Daniël P J
Kia,Seyed Mostafa
Jakob,Lea
Somers,Metten
Sommer,Iris E C
Winter-van Rossum,Inge
Kahn,René S
Cahn,Wiepke
Schnack,Hugo G
Abstract
Introduction: Machine learning models have shown promising potential in individual-level outcome prediction for patients with psychosis, but also have several limitations. To address some of these limitations, we present a model that predicts multiple outcomes, based on longitudinal patient data, while integrating prediction uncertainty to facilitate more reliable clinical decision-making. Material and Methods: We devised a recurrent neural network architecture incorporating long short-term memory (LSTM) units to facilitate outcome prediction by leveraging multimodal baseline variables and clinical data collected at multiple time points. To account for model uncertainty, we employed a novel fuzzy logic approach to integrate the level of uncertainty into individual predictions. We predicted antipsychotic treatment outcomes in 446 first-episode psychosis patients in the OPTiMiSE study, for six different clinical scenarios. The treatment outcome measures assessed at both week 4 and week 10 encompassed symptomatic remission, clinical global remission, and functional remission. Results: Using only baseline predictors to predict different outcomes at week 4, leave-one-site-out validation AUC ranged from 0.62 to 0.66; performance improved when clinical data from week 1 was added (AUC = 0.66-0.71) . For outcome at week 10, using only baseline variables, the models achieved AUC = 0.56-0.64 ; using data from more time points (weeks 1, 4, and 6) improved the performance to AUC = 0.72-0.74 After incorporating prediction uncertainties and stratifying the model decisions based on model confidence, we could achieve accuracies above 0.8 for similar to 50% of patients in five out of the six clinical scenarios. Conclusion: We constructed prediction models utilizing a recurrent neural network architecture tailored to clinical scenarios derived from a time series dataset. One crucial aspect we incorporated was the consideration of uncertainty in individual predictions, which enhances the reliability of decision-making based on the model's output. We provided evidence showcasing the significance of leveraging time series data for achieving more accurate treatment outcome prediction in the field of psychiatry.
Description
© 2024 The Author(s). Acta Psychiatrica Scandinavica published by John Wiley & Sons Ltd.
Date
2024-09-18
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Research Projects
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Keywords
machine learning, precision psychiatry, psychosis prognosis prediction, uncertainty-aware decision making
Citation
van Opstal, D P J, Kia, S M, Jakob, L, Somers, M, Sommer, I E C, Winter-van Rossum, I, Kahn, R S, Cahn, W & Schnack, H G 2024, 'Psychosis Prognosis Predictor : A continuous and uncertainty-aware prediction of treatment outcome in first-episode psychosis', Acta Psychiatrica Scandinavica. https://doi.org/10.1111/acps.13754