Background Intracerebral hemorrhage (ICH) is a life-risking condition characterized by bleeding within the brain parenchyma. ICU readmission in ICH patients is a critical outcome, reflecting both clinical severity and resource utilization. Accurate prediction of ICU readmission risk is crucial for guiding clinical decision-making and optimizing healthcare resources.

Methods This study utilized the Medical Information Mart for Intensive Care (MIMIC-III and MIMIC-IV) databases, which contain comprehensive clinical and demographic data on ICU patients. Patients with ICH were identified from both databases. Various clinical, laboratory, and demographic features were extracted for analysis based on both overview literature and experts’ opinions. Preprocessing methods like imputing and sampling were applied to improve the performance of our models. Machine learning techniques, such as Artificial Neural Network (ANN), XGBoost, and RandomForest were employed to develop predictive models for ICU readmission risk. Model performance was evaluated using metrics such as AUROC, accuracy, sensitivity, and specificity.

Results The developed models demonstrated robust predictive accuracy for ICU readmission in ICH patients, with key predictors including demographic information, clinical parameters, and laboratory measurements.

Conclusion Our study provides a predictive framework for ICU read-mission risk in ICH patients, which can aid in clinical decision-making and improve resource allocation in intensive care settings.

The authors have declared no competing interest.

This study did not receive any funding.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The USC Institutional Review Board (IRB) of the University of Southern California waived ethical approval for this work involving the use of anonymized patient data from the MIMIC-IV database. The data were accessed under a Data Use Agreement following completion of the CITI training program.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

The data utilized in this study were obtained from the publicly available MIMIC-III and MIMIC-IV databases, which contain de-identified health records of ICU patients. Access to the databases requires successful completion of the CITI Program training and signing of a Data Use Agreement (DUA) with the Massachusetts Institute of Technology (MIT). The databases are accessible through PhysioNet at https://physionet.org/content/mimiciii/ and https://physionet.org/content/mimiciv/. Additional data generated during the study, including feature extraction files and model outputs, are available from the corresponding author upon reasonable request.