Forecasting Acute Kidney Injury and Resource Utilization in ICU patients using longitudinal, multimodal models Article Swipe
Yukun Tan
,
Merve Dede
,
Vakul Mohanty
,
Jinzhuang Dou
,
Holly A. Hill
,
Elmer V. Bernstam
,
Ken Chen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.03.14.24304230
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.03.14.24304230
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 hours 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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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.03.14.24304230
- https://www.medrxiv.org/content/medrxiv/early/2024/03/15/2024.03.14.24304230.full.pdf
- OA Status
- green
- References
- 63
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392855314
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392855314Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2024.03.14.24304230Digital Object Identifier
- Title
-
Forecasting Acute Kidney Injury and Resource Utilization in ICU patients using longitudinal, multimodal modelsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-15Full publication date if available
- Authors
-
Yukun Tan, Merve Dede, Vakul Mohanty, Jinzhuang Dou, Holly A. Hill, Elmer V. Bernstam, Ken ChenList of authors in order
- Landing page
-
https://doi.org/10.1101/2024.03.14.24304230Publisher landing page
- PDF URL
-
https://www.medrxiv.org/content/medrxiv/early/2024/03/15/2024.03.14.24304230.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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-
greenOpen access status per OpenAlex
- OA URL
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https://www.medrxiv.org/content/medrxiv/early/2024/03/15/2024.03.14.24304230.full.pdfDirect OA link when available
- Concepts
-
Renal replacement therapy, Acute kidney injury, Receiver operating characteristic, Medicine, Intensive care medicine, Dialysis, Intensive care unit, Intensive care, Mechanical ventilation, Emergency medicine, Artificial intelligence, Medical emergency, Computer science, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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63Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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