###### Abstract  
Delirium in hospitalized patients is a worldwide problem, causing a burden on healthcare professionals and impacting patient prognosis. A machine learning interpretation method (ML interpretation method) presents the results of machine learning predictions and promotes guided decisions. This study focuses on visualizing the predictors of delirium using a ML interpretation method and implementing the analysis results in clinical practice. Retrospective data of 55,389 patients hospitalized in a single acute care center in Japan between December 2017 and February 2022 were collected. Patients were categorized into three analysis populations, according to inclusion and exclusion criteria, to develop delirium prediction models. The predictors were then visualized using Shapley additive explanation (SHAP) and fed back to clinical practice. The machine learning-based prediction of delirium in each population exhibited excellent predictive performance. SHAP was used to visualize the body mass index and albumin levels as critical contributors to delirium prediction. In addition, the cutoff value for age, which was previously unknown, was visualized, and the risk threshold for age was raised. By using the SHAP method, we demonstrated that data-driven decision support is possible using electronic medical record data. learning health system; machine learning interpretation method; Shapley additive explanation; delirium; machine learning +
Footnote †: journal: Medical Information Center, Kyushu University Hospital, Fukuoka 812-8582, Japan 2  
Delirium has been reported globally, and many evaluation tools have been developed to identify patients at high risk of delirium at the time of admission, and subsequently implement preventative measures [6; 7; 8; 9; 10; 11; 12; 13; 14]. With the accumulation of large amounts of medical data and the development of machine learning techniques, research on machine learning prediction models for predicting delirium has been stimulated [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26]. Machine learning techniques such as the decision tree ensemble do not need data preprocessing (neither normalization nor standardization), and can handle missing values without listwise deletion. Such models can use considerable data and capture non-linearity and interaction. In our study on ischemic stroke patients, we confirmed that machine learning can be used to predict prognoses more accurately than using previously developed prognostic scores [27]. However, the \"black box\" problem, whereby recognizing processes and formulas is difficult, hinders the medical implementation of machine learning-based methods. In a previous study, by focusing on the high prediction accuracy of machine learning technology and the disease management function of electronic clinical pathways, we developed a method for the management of patients with cerebral hemorrhage after stratifying the risk of developing aspiration pneumonia based on machine learning prediction results [28]. However, owing to the black-box nature of machine learning, the predictive contributing factors could not be well visualized, and the routine use of machine learning technology in clinical workflows was not possible. Machine learning interpretation methods (ML interpretation methods), which have attracted research attention in recent years, can enable clinicians to interpret predictive models and provide appropriate feedback to analysts, promoting the development of data-driven medicine. Therefore, the use of ML interpretation methods for clinical data has been actively investigated [29; 30; 31; 32]. However, because these methods have been developed recently, there are no examples of their operational incorporation into clinical workflows. Although there are previous studies that have also applied interpretation methods to models for predicting delirium [24; 25; 26], there are no examples of their implementation in clinical workflows.