Forecasting acute kidney injury and resource utilization in ICU patients using longitudinal, multimodal models
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Abstract
Background Advances in artificial intelligence (AI) have realized the potential of revolutionizing healthcare, such as predicting disease progression via longitudinal inspection of Electronic Health Records (EHRs) and lab tests from patients admitted to Intensive Care Units (ICU). Although substantial literature exists addressing broad subjects, including the prediction of mortality, length-of-stay, and readmission, studies focusing on forecasting Acute Kidney Injury (AKI), specifically dialysis anticipation like Continuous Renal Replacement Therapy (CRRT) are scarce. The technicality of how to implement AI remains elusive. Objective This study aims to elucidate the important factors and methods that are required to develop effective predictive models of AKI and CRRT for patients admitted to ICU, using EHRs in the Medical Information Mart for Intensive Care (MIMIC) database. Methods We conducted a comprehensive comparative analysis of established predictive models, considering both time-series measurements and clinical notes from MIMIC-IV databases. Subsequently, we proposed a novel multi-modal model which integrates embeddings of top-performing unimodal models, including Long Short-Term Memory (LSTM) and BioMedBERT, and leverages both unstructured clinical notes and structured time series measurements derived from EHRs to enable the early prediction of AKI and CRRT. Results Our multimodal model achieved a lead time of at least 12 h ahead of clinical manifestation, with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.888 for AKI and 0.997 for CRRT, as well as an Area Under the Precision Recall Curve (AUPRC) of 0.727 for AKI and 0.840 for CRRT, respectively, which significantly outperformed the baseline models. Additionally, we performed a SHapley Additive exPlanation (SHAP) analysis using the expected gradients algorithm, which highlighted important, previously underappreciated predictive features for AKI and CRRT. Conclusion Our study revealed the importance and the technicality of applying longitudinal, multimodal modeling to improve early prediction of AKI and CRRT, offering insights for timely interventions. The performance and interpretability of our model indicate its potential for further assessment towards clinical applications, to ultimately optimize AKI management and enhance patient outcomes.
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Only a small number of studies connected multimodal predictions to clinical interventions.
“Only a small number of studies connected multimodal predictions to clinical interventions [36–39]”
From Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review (2026) · cites this paper as [37]
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- Authors
- Tan, Y., Dede, M., Mohanty, V., Dou, J., Hill, H., Bernstam, E., & Chen, K.
- Affiliations
- The University of Texas MD Anderson Cancer Center (US)
- The University of Texas Health Science Center at Houston (US)
- Corresponding author
- Chen, Ken
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2024-06
- Volume
- 154
- Pages
- 104648
- ISSN
- 1532-0464 (ISSN-L)
- PMC
- PMC12374600
- OpenAlex
- W4396521009
- Cited by
- 19
- References
- 63
- Citations by year
- 2026: 9 · 2025: 10
- FWCI
- 2.47
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 365 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- SSRN Electronic Journal (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (submittedVersion, cc-by-nc-nd)
- Digital Commons-TMC (Texas Medical Center) (submittedVersion)
- Funders
- National Cancer Institute
- University of Texas MD Anderson Cancer Center (U01CA247760, U01CA281902)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Nephrology › Acute Kidney Injury Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- acute kidney injury, continuous renal replacement therapy, intensive care unit patients, multimodal predictive model, LSTM, electronic health records, MIMIC-IV, early prediction, resource utilization (health care), SHAP
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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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