ArticleFrontiers in neurology2023
Analyzing and predicting the risk of death in stroke patients using machine learning.
Article in Frontiers in neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Predictive Value of Machine Learning for Poststroke Mortality Risk: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Machine learning-based prediction model for long-term mortality after ischemic stroke.Scientific reports · 2026Article
- Redefining Stroke Care with Artificial Intelligence: Recent Advances.Current neurology and neuroscience reports · 2026Review
- Article
- Individualized Analysis of Nipple-Sparing Mastectomy Versus Modified Radical Mastectomy Using Deep Learning.Cancer innovation · 2025Article
- Predictive value of biomarker signatures for suicide risk in hospitalised patients with major depressive disorders: a multicentre study in Shanghai.General psychiatry · 2025Article
- Predicting stroke severity of patients using interpretable machine learning algorithms.European journal of medical research · 2024Article
- Determining individual suitability for neoadjuvant systemic therapy in breast cancer patients through deep learning.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2024Article
- Personalized surgical recommendations and quantitative therapeutic insights for patients with metastatic breast cancer: Insights from deep learning.Cancer innovation · 2024Article
- Utilizing machine learning to facilitate the early diagnosis of posterior circulation stroke.BMC neurology · 2024Article
- Article
- Optimizing adjuvant treatment options for patients with glioblastoma.Frontiers in neurology · 2024Article
- Machine learning-based prediction of one-year mortality in ischemic stroke patients.Oxford open neuroscience · 2024Article
- Deep learning to optimize radiotherapy decisions for elderly patients with early-stage breast cancer: a novel approach for personalized treatment.American journal of cancer research · 2024Article
- Personalized chemotherapy selection for patients with triple-negative breast cancer using deep learning.Frontiers in medicine · 2024Article
- Predicting mortality in brain stroke patients using neural networks: outcomes analysis in a longitudinal study.Scientific reports · 2023Article
- Heterogeneous treatment effects of Cerebrolysin as an early add-on to reperfusion therapy:Frontiers in pharmacology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Stroke is an acute disorder and dysfunction of the focal neurological system that has long been recognized as one of the leading causes of death and severe disability in most regions globally. This study aimed to supplement and exploit multiple comorbidities, laboratory tests and demographic factors to more accurately predict death related to stroke, and furthermore, to make inferences about the heterogeneity of treatment in stroke patients to guide better treatment planning. Methods: We extracted data from the Medical Information Mart from the Intensive Care (MIMIC)-IV database. We compared the distribution of the demographic factors between the control and death groups. Subsequently, we also developed machine learning (ML) models to predict mortality among stroke patients. Furthermore, we used meta-learner to recognize the heterogeneity effects of warfarin and human albumin. We comprehensively evaluated and interpreted these models using Shapley Additive Explanation (SHAP) analysis. Results: We included 7,483 patients with MIMIC-IV in this study. Of these, 1,414 (18.9%) patients died during hospitalization or 30 days after discharge. We found that the distributions of age, marital status, insurance type, and BMI differed between the two groups. Our machine learning model achieved the highest level of accuracy to date in predicting mortality in stroke patients. We also observed that patients who were consistent with the model determination had significantly better survival outcomes than the inconsistent population and were better than the overall treatment group. Conclusion: We used several highly interpretive machine learning models to predict stroke prognosis with the highest accuracy to date and to identify heterogeneous treatment effects of warfarin and human albumin in stroke patients. Our interpretation of the model yielded a number of findings that are consistent with clinical knowledge and warrant further study and verification.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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 OpenQuestion graph.