Artificial intelligence to predict in-hospital mortality using novel anatomical injury score

Kang, Wu Seong; Chung, Ho Young; Ko, Hoon; Kim, Nan Yeol; Kim, Do Wan; Cho, Jayun; Shim, Hongjin; Kim, Jin Goo; Jang, Ji Young; Kim, Kyung Won; Lee, Jin-Seok

doi:10.1038/s41598-021-03024-1

Cited by 7 publications

(15 citation statements)

References 20 publications

(27 reference statements)

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“…To the best of our knowledge, our study is the first to demonstrate an AI model that drastically outperforms conventional ICD-based models and triage scales using a population-based data set. Our future goal is to construct a more comprehensive model incorporating both NEDIS-based and AIS-based AI [ 17 ].…”

Section: Discussionmentioning

confidence: 99%

“…Recently, several AI models were proposed to predict trauma-related mortality. Previously, in a multicenter retrospective study in South Korea, we investigated a deep learning model using the AIS code for predicting mortality [ 17 ]. We reanalyzed the ISS system and redefine 46 new regions to discriminate the risk among different internal organs.…”

Section: Discussionmentioning

confidence: 99%

“…Sixth, we could not compare the prediction performances from our AI model with those from AIS code-based approaches such as ISS and NISS, as NEDIS does not provide AIS codes. Recently, we presented an AI model using AIS codes to predict in-hospital mortality [ 17 ]. The model outperformed conventional methods such as ISS and NISS for all accuracy metrics of sensitivity, specificity, balanced accuracy, and AUROC.…”

Section: Discussionmentioning

confidence: 99%

“…Artificial intelligence (AI) is widely used to find complex associations between various features in medical applications [ 14 - 16 ], such as individual injuries and mortality. We recently proposed AI technology utilizing AIS codes that outperformed conventional ISS [ 1 ], providing a favorable area under the receiver operating characteristic (AUROC) of 0.908 [ 17 ]. Tran et al [ 18 ] also used AI technology for mortality prediction using the ICD-10 from the National Trauma Database (NTDB) data set, but the AUROC value was not as high as that of our previous proposed AI model.…”

Section: Introductionmentioning

confidence: 99%

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Model for Predicting In-Hospital Mortality of Physical Trauma Patients Using Artificial Intelligence Techniques: Nationwide Population-Based Study in Korea

Lee¹,

Kang²,

Seo³

et al. 2022

J Med Internet Res

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Background Physical trauma–related mortality places a heavy burden on society. Estimating the mortality risk in physical trauma patients is crucial to enhance treatment efficiency and reduce this burden. The most popular and accurate model is the Injury Severity Score (ISS), which is based on the Abbreviated Injury Scale (AIS), an anatomical injury severity scoring system. However, the AIS requires specialists to code the injury scale by reviewing a patient's medical record; therefore, applying the model to every hospital is impossible. Objective We aimed to develop an artificial intelligence (AI) model to predict in-hospital mortality in physical trauma patients using the International Classification of Disease 10th Revision (ICD-10), triage scale, procedure codes, and other clinical features. Methods We used the Korean National Emergency Department Information System (NEDIS) data set (N=778,111) compiled from over 400 hospitals between 2016 and 2019. To predict in-hospital mortality, we used the following as input features: ICD-10, patient age, gender, intentionality, injury mechanism, and emergent symptom, Alert/Verbal/Painful/Unresponsive (AVPU) scale, Korean Triage and Acuity Scale (KTAS), and procedure codes. We proposed the ensemble of deep neural networks (EDNN) via 5-fold cross-validation and compared them with other state-of-the-art machine learning models, including traditional prediction models. We further investigated the effect of the features. Results Our proposed EDNN with all features provided the highest area under the receiver operating characteristic (AUROC) curve of 0.9507, outperforming other state-of-the-art models, including the following traditional prediction models: Adaptive Boosting (AdaBoost; AUROC of 0.9433), Extreme Gradient Boosting (XGBoost; AUROC of 0.9331), ICD-based ISS (AUROC of 0.8699 for an inclusive model and AUROC of 0.8224 for an exclusive model), and KTAS (AUROC of 0.1841). In addition, using all features yielded a higher AUROC than any other partial features, namely, EDNN with the features of ICD-10 only (AUROC of 0.8964) and EDNN with the features excluding ICD-10 (AUROC of 0.9383). Conclusions Our proposed EDNN with all features outperforms other state-of-the-art models, including the traditional diagnostic code-based prediction model and triage scale.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model for Predicting In-Hospital Mortality of Physical Trauma Patients Using Artificial Intelligence Techniques: Nationwide Population-Based Study in Korea

Lee¹,

Kang²,

Seo³

et al. 2022

J Med Internet Res

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“…However, the TRISS and its various modifications are evidence-based tools, and the results of some studies indicate that they may mislead physicians by misclassifying the patient's condition [ 8 ]. Nevertheless, both categories of models performed poorly when collinearity, heteroskedasticity, higher order interactions, and nonlinear relationships among variables were present [ 9 – 11 ]. Hence, more valuable and accurate prognostic tools that are not limited to these assumptions are needed to achieve better patient outcomes and make the best use of resources.…”

Section: Introductionmentioning

confidence: 99%

Hospital mortality prediction in traumatic injuries patients: comparing different SMOTE-based machine learning algorithms

Hassanzadeh

Farhadian

Rafieemehr

2023

BMC Med Res Methodol

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Background Trauma is one of the most critical public health issues worldwide, leading to death and disability and influencing all age groups. Therefore, there is great interest in models for predicting mortality in trauma patients admitted to the ICU. The main objective of the present study is to develop and evaluate SMOTE-based machine-learning tools for predicting hospital mortality in trauma patients with imbalanced data. Methods This retrospective cohort study was conducted on 126 trauma patients admitted to an intensive care unit at Besat hospital in Hamadan Province, western Iran, from March 2020 to March 2021. Data were extracted from the medical information records of patients. According to the imbalanced property of the data, SMOTE techniques, namely SMOTE, Borderline-SMOTE1, Borderline-SMOTE2, SMOTE-NC, and SVM-SMOTE, were used for primary preprocessing. Then, the Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) methods were used to predict patients' hospital mortality with traumatic injuries. The performance of the methods used was evaluated by sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), accuracy, Area Under the Curve (AUC), Geometric Mean (G-means), F1 score, and P-value of McNemar's test. Results Of the 126 patients admitted to an ICU, 117 (92.9%) survived and 9 (7.1%) died. The mean follow-up time from the date of trauma to the date of outcome was 3.98 ± 4.65 days. The performance of ML algorithms is not good with imbalanced data, whereas the performance of SMOTE-based ML algorithms is significantly improved. The mean area under the ROC curve (AUC) of all SMOTE-based models was more than 91%. F1-score and G-means before balancing the dataset were below 70% for all ML models except ANN. In contrast, F1-score and G-means for the balanced datasets reached more than 90% for all SMOTE-based models. Among all SMOTE-based ML methods, RF and ANN based on SMOTE and XGBoost based on SMOTE-NC achieved the highest value for all evaluation criteria. Conclusions This study has shown that SMOTE-based ML algorithms better predict outcomes in traumatic injuries than ML algorithms. They have the potential to assist ICU physicians in making clinical decisions.

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A Retrospective Cohort Study: Predicting 90-Day Mortality for ICU Trauma Patients with a Machine Learning Algorithm Using XGBoost Using MIMIC-III Database

Yang,

Cao,

Zhou

et al. 2023

JMDH

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Objective The aim of this study was to develop and validate a machine learning-based predictive model that predicts 90-day mortality in ICU trauma patients. Methods Data of patients with severe trauma were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The performances of mortality prediction models generated using nine machine learning extreme gradient boosting (XGBoost), logistic regression, random forest, AdaBoost, multilayer perceptron (MLP) neural networks, support vector machine (SVM), light gradient boosting machine (GBM), k nearest neighbors (KNN) and gaussian naive bayes (GNB). The performance of the model was evaluated in terms of discrimination, calibration and clinical application. Results We found that the accuracy, sensitivity, specificity, PPV, NPV and F1 score of our proposed XGBoost model were 82.8%, 79.7%, 77.6%, 51.2%, 91.5% and 0.624, respectively. Among the nine models, the XGBoost model performed best. Compared with traditional logistic regression, the calibration curves of the XGBoost model and decision curve analysis (DCA) performed well. Conclusion Our study shows that the XGBoost model outperforms other machine learning models in predicting 90-day mortality in trauma patients. It can be used to assist clinicians in the early identification of mortality risk factors and early intervention to reduce mortality.

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Artificial intelligence to predict in-hospital mortality using novel anatomical injury score

Cited by 7 publications

References 20 publications

Model for Predicting In-Hospital Mortality of Physical Trauma Patients Using Artificial Intelligence Techniques: Nationwide Population-Based Study in Korea

Model for Predicting In-Hospital Mortality of Physical Trauma Patients Using Artificial Intelligence Techniques: Nationwide Population-Based Study in Korea

Hospital mortality prediction in traumatic injuries patients: comparing different SMOTE-based machine learning algorithms

A Retrospective Cohort Study: Predicting 90-Day Mortality for ICU Trauma Patients with a Machine Learning Algorithm Using XGBoost Using MIMIC-III Database

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