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Background <p>No drug recommender system exists to guide antipsychotic medication selection for hospital in-patients with schizophrenia. This study developed and validated a prototype personalised medication recommender system for in-hospital patients with schizophrenia.</p> Methods <p>This prognostic study analysed data from the Medical Information Mart for Intensive Care IV database on patients prescribed antipsychotic medications with hospital admission diagnoses of schizophrenic disorders between 2008 and 2022. Five similarity-based algorithms (two collaborative filtering and three distance-based representation methods) were evaluated using nested patient-wise cross-validation. The best performing model underwent external validation across geographic and temporal contexts using (a) Northwestern Intensive Care Unit and (b) Medical Information Mart for Intensive Care III datasets. Treatment success was measured through an affinity score (range 0–1) incorporating time between admissions, medication switches, and length of stay. Algorithm performance was assessed using root-mean-square error and mean average precision at position three, with visit-specific analysis across first, second, and third admissions.</p> Results <p>Among 1,152 patients (660 first visits, 251&#xa0;s visits, 241 third visits), cosine similarity-based collaborative filtering with K = 7 neighbours achieved optimal performance. Internal cross-validation demonstrated excellent prediction accuracy (root-mean-square error 0.116, standard deviation 0.007) and moderate recommendation quality (mean average precision at position three 0.490, standard deviation 0.029), improving to 0.697 (standard deviation 0.019) for first visits only. Geographic validation on 32 patients maintained recommendation quality (mean average precision at position three 0.432) despite increased prediction uncertainty (root-mean-square error 0.498). Temporal validation on 66 patients showed similar patterns (mean average precision at position three 0.359, root-mean-square error 0.578). Second-visit recommendations demonstrated exceptional transferability across validation contexts, achieving mean average precision at position three scores up to 0.813. In 163 cases where the model disagreed with attending clinicians, clinicians’ top-3 recommendations achieved significantly superior treatment outcomes (mean difference − 0.122 affinity points, 95% confidence interval: -0.145 to -0.098, <i>p</i> &lt; 0.001, Cohen’s d=-0.81).</p> Conclusions <p>This proof-of-concept study demonstrates feasibility of developing a machine learning-based medication recommender system for hospital in-patients with schizophrenia, that appear to maintain recommendation quality across different healthcare institutions and time periods. Prospective clinical trials are needed to establish real-world effectiveness and safety.</p>

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Developing and validating a machine learning pharmaceutical therapy recommender system for US-based hospital in-patients with schizophrenia spectrum disorders

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月1日

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  • Maximin Lange,
  • Urvik Mehta,
  • Nikolaos Koutsouleris,
  • Feras Fayez,
  • Ricardo Twumasi

摘要

Background

No drug recommender system exists to guide antipsychotic medication selection for hospital in-patients with schizophrenia. This study developed and validated a prototype personalised medication recommender system for in-hospital patients with schizophrenia.

Methods

This prognostic study analysed data from the Medical Information Mart for Intensive Care IV database on patients prescribed antipsychotic medications with hospital admission diagnoses of schizophrenic disorders between 2008 and 2022. Five similarity-based algorithms (two collaborative filtering and three distance-based representation methods) were evaluated using nested patient-wise cross-validation. The best performing model underwent external validation across geographic and temporal contexts using (a) Northwestern Intensive Care Unit and (b) Medical Information Mart for Intensive Care III datasets. Treatment success was measured through an affinity score (range 0–1) incorporating time between admissions, medication switches, and length of stay. Algorithm performance was assessed using root-mean-square error and mean average precision at position three, with visit-specific analysis across first, second, and third admissions.

Results

Among 1,152 patients (660 first visits, 251 s visits, 241 third visits), cosine similarity-based collaborative filtering with K = 7 neighbours achieved optimal performance. Internal cross-validation demonstrated excellent prediction accuracy (root-mean-square error 0.116, standard deviation 0.007) and moderate recommendation quality (mean average precision at position three 0.490, standard deviation 0.029), improving to 0.697 (standard deviation 0.019) for first visits only. Geographic validation on 32 patients maintained recommendation quality (mean average precision at position three 0.432) despite increased prediction uncertainty (root-mean-square error 0.498). Temporal validation on 66 patients showed similar patterns (mean average precision at position three 0.359, root-mean-square error 0.578). Second-visit recommendations demonstrated exceptional transferability across validation contexts, achieving mean average precision at position three scores up to 0.813. In 163 cases where the model disagreed with attending clinicians, clinicians’ top-3 recommendations achieved significantly superior treatment outcomes (mean difference − 0.122 affinity points, 95% confidence interval: -0.145 to -0.098, p < 0.001, Cohen’s d=-0.81).

Conclusions

This proof-of-concept study demonstrates feasibility of developing a machine learning-based medication recommender system for hospital in-patients with schizophrenia, that appear to maintain recommendation quality across different healthcare institutions and time periods. Prospective clinical trials are needed to establish real-world effectiveness and safety.