---
title: "Multimodal AI model predicts in-hospital mortality for critically ill patients using multicenter I"
id: "pubmed-42549798"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42549798"
content_type: "clinical_feed_article"
specialty: "Critical Care"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42549798/"
doi: "10.1097/ALN.0000000000006294"
published_at: "2026-08-04T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Multimodal AI model predicts in-hospital mortality for critically ill patients using multicenter I
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42549798
- **Specialty:** [Critical Care](https://medichelpline.com/clinical-feed/critical-care.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42549798/)
- **DOI:** [10.1097/ALN.0000000000006294](https://doi.org/10.1097%2FALN.0000000000006294)
- **Published At:** 2026-08-04T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- The study developed and externally validated a **multimodal artificial intelligence** deep learning model to predict in-hospital mortality after the first 24 hours of ICU admission. - Researchers used data from four multicenter critical care datasets: **MIMIC-III**, **MIMIC-IV**, **eICU**, and **HiRID**, totaling 203,434 ICU admissions from more than 200 hospitals between 2001 and 2022. - Inputs combined time-invariant variables, time-variant time series within the first 24 hours, clinical notes, and chest X-ray images to form an integrated model. - The model was trained on MIMIC datasets and externally validated in a temporally separated MIMIC population, HiRID, and across eight institutions within the eICU dataset. - Overall mortality rates across datasets ranged from 5.2% to 7.9%. - The model using structured data reported an **AUROC** of 0.92 (95% CI 0.90–0.93), **AUPRC** 0.53 (95% CI 0.49–0.57), and **Brier** score 0.19 (95% CI 0.18–0.20). - External validation in eICU institutions produced AUROCs from 0.84 to 0.92, demonstrating site-level generalizability. - In the subgroup with available notes and imaging, adding clinical notes and chest X-rays improved performance: AUROC from 0.87 to 0.89 (p=0.0167 by DeLong test), AUPRC from 0.43 to 0.48, and Brier score from 0.37 to 0.17. - The authors emphasize the value of integrating multiple data modalities for mortality prediction and the necessity of external validation across centers. - Conflict of interest disclosures note institutional funding received by one author’s institution; no other COI details were reported in the abstract.
## Clinical Analysis & Structured Key Points
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Online ahead of print. # Development and external validation of a multimodal artificial intelligence mortality prediction model of critically ill patients using multicenter data [Behrooz Mamandipoor](https://pubmed.ncbi.nlm.nih.gov/?term=Mamandipoor+B&cauthor_id=42549798)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42549798/#full-view-affiliation-1 "Department of Biomedical Informatics, University of California, San Diego Health, La Jolla, CA, USA.")[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42549798/#full-view-affiliation-2 "Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, La Jolla, CA, USA."), [Chun-Nan Hsu](https://pubmed.ncbi.nlm.nih.gov/?term=Hsu+CN&cauthor_id=42549798)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42549798/#full-view-affiliation-2 "Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, La Jolla, CA, USA."), [Martin Krause](https://pubmed.ncbi.nlm.nih.gov/?term=Krause+M&cauthor_id=42549798)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42549798/#full-view-affiliation-2 "Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, La Jolla, CA, USA.")[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42549798/#full-view-affiliation-3 "Division of Critical Care Medicine, Department of Anesthesiology, University of California, San Diego, La Jolla, CA, USA."), [Ulrich H Schmidt](https://pubmed.ncbi.nlm.nih.gov/?term=Schmidt+UH&cauthor_id=42549798)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42549798/#full-view-affiliation-3 "Division of Critical Care Medicine, Department of Anesthesiology, University of California, San Diego, La Jolla, CA, USA."), [Rodney A Gabriel](https://pubmed.ncbi.nlm.nih.gov/?term=Gabriel+RA&cauthor_id=42549798)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42549798/#full-view-affiliation-1 "Department of Biomedical Informatics, University of California, San Diego Health, La Jolla, CA, USA.")[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42549798/#full-view-affiliation-2 "Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, La Jolla, CA, USA.") Affiliations Expand ### Affiliations * 1 Department of Biomedical Informatics, University of California, San Diego Health, La Jolla, CA, USA. * 2 Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, La Jolla, CA, USA. * 3 Division of Critical Care Medicine, Department of Anesthesiology, University of California, San Diego, La Jolla, CA, USA. * PMID: **42549798** * DOI: [ 10.1097/ALN.0000000000006294 ](https://doi.org/10.1097/aln.0000000000006294) Item in Clipboard # Development and external validation of a multimodal artificial intelligence mortality prediction model of critically ill patients using multicenter data Behrooz Mamandipoor et al. Anesthesiology. 2026. Show details Display options Display options Format Abstract PubMed PMID Anesthesiology Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Anesthesiology%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Anesthesiology%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42549798/) . 2026 Aug 4. doi: 10.1097/ALN.0000000000006294. Online ahead of print. ### Authors [Behrooz Mamandipoor](https://pubmed.ncbi.nlm.nih.gov/?term=Mamandipoor+B&cauthor_id=42549798)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42549798/#short-view-affiliation-1 "Department of Biomedical Informatics, University of California, San Diego Health, La Jolla, CA, USA.")[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42549798/#short-view-affiliation-2 "Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, La Jolla, CA, USA."), [Chun-Nan Hsu](https://pubmed.ncbi.nlm.nih.gov/?term=Hsu+CN&cauthor_id=42549798)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42549798/#short-view-affiliation-2 "Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, La Jolla, CA, USA."), [Martin Krause](https://pubmed.ncbi.nlm.nih.gov/?term=Krause+M&cauthor_id=42549798)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42549798/#short-view-affiliation-2 "Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, La Jolla, CA, USA.")[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42549798/#short-view-affiliation-3 "Division of Critical Care Medicine, Department of Anesthesiology, University of California, San Diego, La Jolla, CA, USA."), [Ulrich H Schmidt](https://pubmed.ncbi.nlm.nih.gov/?term=Schmidt+UH&cauthor_id=42549798)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42549798/#short-view-affiliation-3 "Division of Critical Care Medicine, Department of Anesthesiology, University of California, San Diego, La Jolla, CA, USA."), [Rodney A Gabriel](https://pubmed.ncbi.nlm.nih.gov/?term=Gabriel+RA&cauthor_id=42549798)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42549798/#short-view-affiliation-1 "Department of Biomedical Informatics, University of California, San Diego Health, La Jolla, CA, USA.")[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42549798/#short-view-affiliation-2 "Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, La Jolla, CA, USA.") ### Affiliations * 1 Department of Biomedical Informatics, University of California, San Diego Health, La Jolla, CA, USA. * 2 Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, La Jolla, CA, USA. * 3 Division of Critical Care Medicine, Department of Anesthesiology, University of California, San Diego, La Jolla, CA, USA. * PMID: **42549798** * DOI: [ 10.1097/ALN.0000000000006294 ](https://doi.org/10.1097/aln.0000000000006294) Item in Clipboard Cite Display options Display options Format Abstract PubMed PMID ## Abstract **Background:** Early prediction of in-hospital mortality in critically ill patients can aid clinicians in optimizing treatment. The objective was to develop a multimodal deep learning model, using structured and unstructured clinical data, to predict in-hospital mortality risk among critically ill patients after their initial 24 hour intensive care unit (ICU) admission. **Methods:** We used data from MIMIC-III, MIMIC-IV, eICU, and HiRID. A multimodal model was developed on the MIMIC datasets, featuring time series components occurring within the first 24 hours of ICU admission and predicting risk of subsequent inpatient mortality. Inputs included time-invariant variables, time-variant variables, clinical notes, and chest X-ray images. External validation occurred in a temporally separated MIMIC population, HiRID, and eICU datasets. Area under the receiver operating characteristics (AUROC), area under the precision-recall curves (AUPRC), and Brier scores were reported. **Results:** A total of 203,434 ICU admissions from more than 200 hospitals between 2001 to 2022 were included, in which mortality rate ranged from 5.2% to 7.9% across the four datasets. The model integrating structured data points had AUROC, AUPRC, and Brier scores of 0.92 [95% confidence interval (CI) 0.90 - 0.93], 0.53 [95% CI 0.49 - 0.57], and 0.19 [95% CI 0.18 - 0.20], respectively. We externally validated the model on eight different institutions within the eICU dataset, demonstrating AUROCs ranging from 0.84-0.92. When including only patients with available clinical notes and imaging data, inclusion of notes and imaging into the model, the AUROC, AUPRC, and Brier score improved from 0.87 [95% CI 0.85 - 0.89] to 0.89 [95% CI 0.87 - 0.91], 0.43 [95% CI 0.36 - 0.51] to 0.48 [95% CI 0.40 - 0.56], and 0.37 [95% CI 0.36 - 0.39] to 0.17 [95% CI 0.16 - 0.19], respectively. The difference in AUROC was statistically significant based on DeLong test (p=0.0167). **Conclusions:** Our findings highlight the importance of incorporating multiple sources of patient information for mortality prediction and the importance of external validation. **Keywords:** artificial intelligence; critical care medicine; deep learning; mortality; predictive modeling. [PubMed Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) ## Conflict of interest statement Conflicts of Interest: Dr. Gabriel’s institution, the University of California, has received funding and/or product for research from Wellcome Leap (Los Angeles, CA), Advanced Research Projects Agency for Health (Bethesda, MD), National Institutes of Health (Bethesda, MD), Anesthesia Patient Safety Foundation (Schaumburg, Illinois), Avanos Medical (Alpharetta, GA), Pacira Biosciences (Tampa, FL), Takeda (San Diego, CA), and Merck (Rahway, NJ). ## Similar articles * [ Development of a Machine Learning-Based Predictive Model for Postoperative Delirium in Older Adult Intensive Care Unit Patients: Retrospective Study. ](https://pubmed.ncbi.nlm.nih.gov/40537091/) Li H, Zang Q, Li Q, Lin Y, Duan J, Huang J, Hu H, Zhang Y, Xia D, Zhou M.Li H, et al.J Med Internet Res. 2025 Jun 19;27:e67258. doi: 10.2196/67258.J Med Internet Res. 2025.PMID: 40537091Free PMC article. * [ External Validation, Recalibration, and Extension of a Prediction Model of Early Acute Kidney Injury in Critically Ill Children Using Multicenter Data. ](https://pubmed.ncbi.nlm.nih.gov/42183744/) Dziorny AC, Drury S, Clark A, Farris RWD, Nishisaki A, Cornell TT, Tawfik DS, Bennett TD, Shah SS, Weiss SL, Mohamed T, Shah N, McMahon J, Muthu N, Wetzel RC, Zand M, Sanchez-Pinto LN; Pediatric Learning Health System Network (PEDSnet) and the PICU Data Collaborative.Dziorny AC, et al.Crit Care Explor. 2026 May 25;8(6):e1425. doi: 10.1097/CCE.0000000000001425. eCollection 2026 Jun 1.Crit Care Explor. 2026.PMID: 42183744Free PMC article. * [ Interpretable model for early prediction of 28-day mortality in patients with cirrhosis and sepsis: a multi-cohort ICU study. ](https://pubmed.ncbi.nlm.nih.gov/41559593/) Xu X, Li J, Yu H, Huang J.Xu X, et al.BMC Gastroenterol. 2026 Jan 20;26(1):123. doi: 10.1186/s12876-026-04620-z.BMC Gastroenterol. 2026.PMID: 41559593Free PMC article. * [ Using Predictive Models to Improve Care for Patients Hospitalized with COVID-19 [Internet]. ](https://pubmed.ncbi.nlm.nih.gov/38976624/) Kaushal R, Zhang Y, Banerjee S, Weiner M, Su C, Wang F, Schenck E, Goyal P, Khullar D, Steel P, Flory J, Hupert N, Schpero W, Díaz I, Choi J, Wu Y, Orlander D, Morozyuk D.Kaushal R, et al.Washington (DC): Patient-Centered Outcomes Research Institute (PCORI); 2023 Jan.Washington (DC): Patient-Centered Outcomes Research Institute (PCORI); 2023 Jan.PMID: 38976624Free Books & Documents.Review. * [ The Prognostic Performance of Artificial Intelligence and Machine Learning Models for Mortality Prediction in Intensive Care Units: A Systematic Review. ](https://pubmed.ncbi.nlm.nih.gov/40978923/) Dhami A, Onyeukwu KA, Sattar S, Batra A, Mostafa Y, Haris M, Iqbal A, Bokhari SFH, Siddique MU.Dhami A, et al.Cureus. 2025 Aug 19;17(8):e90465. doi: 10.7759/cureus.90465. eCollection 2025 Aug.Cureus. 2025.PMID: 40978923Free PMC article.Review. 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