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Year Number of Results
2018 4
2019 9
2020 29
2021 103
2022 142
2023 189
2024 84

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511 results

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Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost.
Hou N, Li M, He L, Xie B, Wang L, Zhang R, Yu Y, Sun X, Pan Z, Wang K. Hou N, et al. J Transl Med. 2020 Dec 7;18(1):462. doi: 10.1186/s12967-020-02620-5. J Transl Med. 2020. PMID: 33287854 Free PMC article.
The aims of this study were to develop a machine learning approach using XGboost to predict the 30-days mortality for MIMIC-III Patients with sepsis-3 and to determine whether such model performs better than traditional prediction models. ...CONCLUSION …
The aims of this study were to develop a machine learning approach using XGboost to predict the 30-days mortality for M …
Prediction model of in-hospital mortality in intensive care unit patients with heart failure: machine learning-based, retrospective analysis of the MIMIC-III database.
Li F, Xin H, Zhang J, Fu M, Zhou J, Lian Z. Li F, et al. BMJ Open. 2021 Jul 23;11(7):e044779. doi: 10.1136/bmjopen-2020-044779. BMJ Open. 2021. PMID: 34301649 Free PMC article.
We aimed to develop and validate a prediction model for all-cause in-hospital mortality among ICU-admitted HF patients. ...In pairwise comparison, the prediction effectiveness was higher with the XGBoost and LASSO regression models than with the GWTG-H …
We aimed to develop and validate a prediction model for all-cause in-hospital mortality among ICU-admitted HF patients. ...In …
Predicting Mortality in Intensive Care Unit Patients With Heart Failure Using an Interpretable Machine Learning Model: Retrospective Cohort Study.
Li J, Liu S, Hu Y, Zhu L, Mao Y, Liu J. Li J, et al. J Med Internet Res. 2022 Aug 9;24(8):e38082. doi: 10.2196/38082. J Med Internet Res. 2022. PMID: 35943767 Free PMC article.
However, due to their lack of interpretability, most HF mortality prediction models have not yet reached clinical practice. OBJECTIVE: We aimed to develop an interpretable model to predict the mortality risk for patients with HF in intensive care units …
However, due to their lack of interpretability, most HF mortality prediction models have not yet reached clinical practice. OB …
Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP.
Wang K, Tian J, Zheng C, Yang H, Ren J, Liu Y, Han Q, Zhang Y. Wang K, et al. Comput Biol Med. 2021 Oct;137:104813. doi: 10.1016/j.compbiomed.2021.104813. Epub 2021 Aug 28. Comput Biol Med. 2021. PMID: 34481185 Free article.
BACKGROUND: This study sought to evaluate the performance of machine learning (ML) models and establish an explainable ML model with good prediction of 3-year all-cause mortality in patients with heart failure (HF) caused by coronary heart disease (CHD). METHODS: We …
BACKGROUND: This study sought to evaluate the performance of machine learning (ML) models and establish an explainable ML model with good …
Machine learning for the prediction of acute kidney injury in patients with sepsis.
Yue S, Li S, Huang X, Liu J, Hou X, Zhao Y, Niu D, Wang Y, Tan W, Wu J. Yue S, et al. J Transl Med. 2022 May 13;20(1):215. doi: 10.1186/s12967-022-03364-0. J Transl Med. 2022. PMID: 35562803 Free PMC article.
The XGBoost model had the best predictive performance in terms of discrimination, calibration, and clinical application among all models. CONCLUSION: The ML models can be reliable tools for predicting AKI in septic patients. The XGBoost model has the b …
The XGBoost model had the best predictive performance in terms of discrimination, calibration, and clinical application among …
An explainable knowledge distillation method with XGBoost for ICU mortality prediction.
Liu M, Guo C, Guo S. Liu M, et al. Comput Biol Med. 2023 Jan;152:106466. doi: 10.1016/j.compbiomed.2022.106466. Epub 2022 Dec 21. Comput Biol Med. 2023. PMID: 36566626
Hence, an explainable Knowledge Distillation method with XGBoost (XGB-KD) is proposed to improve the predictive performance of XGBoost while supporting better explainability. ...Our method can also provide intuitive explanations. CONCLUSIONS: Our method is us …
Hence, an explainable Knowledge Distillation method with XGBoost (XGB-KD) is proposed to improve the predictive performance of …
Prediction of the development of acute kidney injury following cardiac surgery by machine learning.
Tseng PY, Chen YT, Wang CH, Chiu KM, Peng YS, Hsu SP, Chen KL, Yang CY, Lee OK. Tseng PY, et al. Crit Care. 2020 Jul 31;24(1):478. doi: 10.1186/s13054-020-03179-9. Crit Care. 2020. PMID: 32736589 Free PMC article.
BACKGROUND: Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication that results in increased morbidity and mortality after cardiac surgery. Most established prediction models are limited to the analysis of nonlinear relationships and fail t …
BACKGROUND: Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication that results in increased morbidity and mort …
Interpretable prediction of mortality in liver transplant recipients based on machine learning.
Zhang X, Gavaldà R, Baixeries J. Zhang X, et al. Comput Biol Med. 2022 Dec;151(Pt A):106188. doi: 10.1016/j.compbiomed.2022.106188. Epub 2022 Oct 12. Comput Biol Med. 2022. PMID: 36306583
BACKGROUND: Accurate prediction of the mortality of post-liver transplantation is an important but challenging task. ...With the selected optimal feature set, seven machine learning models were applied to predict mortality over different time windows. …
BACKGROUND: Accurate prediction of the mortality of post-liver transplantation is an important but challenging task. ...With t …
Mortality Prediction in Severe Traumatic Brain Injury Using Traditional and Machine Learning Algorithms.
Wu X, Sun Y, Xu X, Steyerberg EW, Helmrich IRAR, Lecky F, Guo J, Li X, Feng J, Mao Q, Xie G, Maas AIR, Gao G, Jiang J. Wu X, et al. J Neurotrauma. 2023 Jul;40(13-14):1366-1375. doi: 10.1089/neu.2022.0221. Epub 2023 Apr 12. J Neurotrauma. 2023. PMID: 37062757
This study aimed to develop and validate prediction models for in-hospital mortality after severe traumatic brain injury (sTBI). ...External validation was performed in 1113 patients with sTBI in the CENTER-TBI European Registry study. XGBoost achieved high d …
This study aimed to develop and validate prediction models for in-hospital mortality after severe traumatic brain injury (sTBI …
Mortality Prediction Using SaO(2)/FiO(2) Ratio Based on eICU Database Analysis.
Patel S, Singh G, Zarbiv S, Ghiassi K, Rachoin JS. Patel S, et al. Crit Care Res Pract. 2021 Nov 8;2021:6672603. doi: 10.1155/2021/6672603. eCollection 2021. Crit Care Res Pract. 2021. PMID: 34790417 Free PMC article.
Quantitative correlation between S/F and P/F has been verified, but the data for the relative predictive ability for ICU mortality remains in question. We hypothesize that S/F is noninferior to P/F as a predictive feature for ICU mortality. ...The feat …
Quantitative correlation between S/F and P/F has been verified, but the data for the relative predictive ability for ICU mortality …
511 results