Evidence map›Paper›PMID 41834990›Full record

ArticleNeuropsychiatric disease and treatment2026

Machine Learning Models for Predicting Antipsychotic Effectiveness and Separate Cost-Effectiveness Analysis in Hospitalized Schizophrenia Patients.

Jiatong Zhang, Qian Xu, WenLong Jiang, DaWei Sun, LongYan Peng

Abstract read
In one paragraph

Article in Neuropsychiatric disease and treatment, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jiatong ZhangCollege of Pharmacy, Qiqihar Medical University, Qiqihar, Heilongjiang, People's Republic of China.ORCID 0009-0004-6853-6255
Qian XuDepartment of Pharmacy, The Third Hospital of Daqing City, Daqing, Heilongjiang, People's Republic of China.ORCID 0009-0005-9902-6677
WenLong JiangDepartment of Psychiatry, The Third Hospital of Daqing City, Daqing, Heilongjiang, People's Republic of China.
DaWei SunDepartment of Psychiatry, The Third Hospital of Daqing City, Daqing, Heilongjiang, People's Republic of China.
LongYan PengDepartment of Psychology, The Third Hospital of Daqing City, Daqing, Heilongjiang, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Schizophrenia is a burden on patients' health and finances and long-term antipsychotic treatment is required; treatment response differs among patients. This study aims to leverage data from Chinese hospitals to develop a machine learning (ML) model that predicts antipsychotic treatment efficacy in patients with schizophrenia and to conduct a payer-perspective cost-effectiveness analysis to inform clinical practice. Patients and Methods: This single-center, real-world retrospective cohort study included 834 patients with schizophrenia from a Chinese hospital. Eight models were constructed using ML and performance was assessed. The model with highest accuracy was determined based on the area under the receiver operating characteristic curve (AUC). We used the Shapley Additive Explanations (SHAP) values to determine the relative importance of each factor. Cost-effectiveness and incremental cost-effectiveness analyses were performed to assess cost-effectiveness of various treatments. A univariate sensitivity analysis was also conducted to validate the results. Results: The top 10 strongly correlated variables, identified through the Boruta algorithm, were selected for in-depth analysis to construct the model. GBM demonstrates the highest performance following a comprehensive evaluation. On the independent test set, our model achieved an AUC of 0.879 (95% CI: 0.833-0.924), an accuracy of 0.836, and a recall of 0.823. Based on this model, we developed and made publicly available an online prediction calculator to assist in clinical decision-making. Among all the treatment regimens, risperidone was the most cost-effective. Conclusion: The GBM model and its online calculator predict the treatment efficacy for hospitalized schizophrenia patients, aiding doctors in tailoring personalised treatment strategies. Risperidone tablets exhibit the highest cost-effectiveness in treatment, guiding the optimization of treatment plans and cost reduction.

Indexed as

antipsychoticscost-effectivenessmachine learningpredictionschizophrenia

Identifiers

PMID41834990
PMCPMC12988738

What Socratic holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.