Interpretable machine learning model for predicting acute kidney injury in critically ill patients

Background This study aimed to create a method for promptly predicting acute kidney injury (AKI) in intensive care patients by applying interpretable, explainable artificial intelligence techniques. Methods Population data regarding intensive care patients were derived from the Medical Information M...

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Vydáno v:	BMC medical informatics and decision making Ročník 24; číslo 1; s. 148 - 12
Hlavní autoři:	Li, Xunliang, Wang, Peng, Zhu, Yuke, Zhao, Wenman, Pan, Haifeng, Wang, Deguang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 31.05.2024 BioMed Central Ltd Springer Nature B.V BMC
Témata:	Acute kidney injury Acute Kidney Injury - diagnosis Acute renal failure Adult Aged Algorithms Artificial intelligence Care and treatment Critical Illness Critically ill Dementia Demographic aspects Diabetes Diagnosis Explainable artificial intelligence Female Health aspects Health Informatics Heart attacks Heart failure Hemodialysis Hospitalization Humans Information Systems and Communication Service Intensive care Intensive care unit Intensive Care Units Interpretability Kidney diseases Kidneys Learning algorithms Liver diseases Machine Learning Male Management of Computing and Information Systems Mechanical ventilation Medicine Medicine & Public Health Middle Aged Missing data Mortality Patient admissions Patients Prediction model Regression analysis Rheumatic diseases Risk factors Sepsis Ventilators Vital signs China Interpretability Acute kidney injury Intensive care unit Machine learning Prediction model
ISSN:	1472-6947, 1472-6947
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Popis
Shrnutí:	Background This study aimed to create a method for promptly predicting acute kidney injury (AKI) in intensive care patients by applying interpretable, explainable artificial intelligence techniques. Methods Population data regarding intensive care patients were derived from the Medical Information Mart for Intensive Care IV database from 2008 to 2019. Machine learning (ML) techniques with six methods were created to construct the predicted models for AKI. The performance of each ML model was evaluated by comparing the areas under the curve (AUC). Local Interpretable Model-Agnostic Explanations (LIME) method and Shapley Additive exPlanation values were used to decipher the best model. Results According to inclusion and exclusion criteria, 53,150 severely sick individuals were included in the present study, of which 42,520 (80%) were assigned to the training group, and 10,630 (20%) were allocated to the validation group. Compared to the other five ML models, the eXtreme Gradient Boosting (XGBoost) model greatly predicted AKI following ICU admission, with an AUC of 0.816. The top four contributing variables of the XGBoost model were SOFA score, weight, mechanical ventilation, and the Simplified Acute Physiology Score II. An AKI and Non-AKI cases were predicted separately using the LIME algorithm. Conclusion Overall, the constructed clinical feature-based ML models are excellent in predicting AKI in intensive care patients. It would be constructive for physicians to provide early support and timely intervention measures to intensive care patients at risk of AKI.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1472-6947 1472-6947
DOI:	10.1186/s12911-024-02537-9