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Explainable AI enables clinical trial patient selection to retrospectively improve treatment effects in schizophrenia

  • Mellem, Monika S.1
  • Kollada, Matt1
  • Tiller, Jane1
  • Lauritzen, Thomas1
  • 1 BlackThorn Therapeutics, 780 Brannan St., San Francisco, CA, 94103, USA , San Francisco (United States)
Published Article
BMC Medical Informatics and Decision Making
Springer (Biomed Central Ltd.)
Publication Date
May 20, 2021
DOI: 10.1186/s12911-021-01510-0
Springer Nature


BackgroundHeterogeneity among patients’ responses to treatment is prevalent in psychiatric disorders. Personalized medicine approaches—which involve parsing patients into subgroups better indicated for a particular treatment—could therefore improve patient outcomes and serve as a powerful tool in patient selection within clinical trials. Machine learning approaches can identify patient subgroups but are often not “explainable” due to the use of complex algorithms that do not mirror clinicians’ natural decision-making processes.MethodsHere we combine two analytical approaches—Personalized Advantage Index and Bayesian Rule Lists—to identify paliperidone-indicated schizophrenia patients in a way that emphasizes model explainability. We apply these approaches retrospectively to randomized, placebo-controlled clinical trial data to identify a paliperidone-indicated subgroup of schizophrenia patients who demonstrate a larger treatment effect (outcome on treatment superior than on placebo) than that of the full randomized sample as assessed with Cohen’s d. For this study, the outcome corresponded to a reduction in the Positive and Negative Syndrome Scale (PANSS) total score which measures positive (e.g., hallucinations, delusions), negative (e.g., blunted affect, emotional withdrawal), and general psychopathological (e.g., disturbance of volition, uncooperativeness) symptoms in schizophrenia.ResultsUsing our combined explainable AI approach to identify a subgroup more responsive to paliperidone than placebo, the treatment effect increased significantly over that of the full sample (p < 0.0001 for a one-sample t-test comparing the full sample Cohen’s d = 0.82 and a generated distribution of subgroup Cohen’s d’s with mean d = 1.22, std d = 0.09). In addition, our modeling approach produces simple logical statements (if–then-else), termed a “rule list”, to ease interpretability for clinicians. A majority of the rule lists generated from cross-validation found two general psychopathology symptoms, disturbance of volition and uncooperativeness, to predict membership in the paliperidone-indicated subgroup.ConclusionsThese results help to technically validate our explainable AI approach to patient selection for a clinical trial by identifying a subgroup with an improved treatment effect. With these data, the explainable rule lists also suggest that paliperidone may provide an improved therapeutic benefit for the treatment of schizophrenia patients with either of the symptoms of high disturbance of volition or high uncooperativeness.Trial Registration: identifier: NCT 00,083,668; prospectively registered May 28, 2004

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