Olang O, Mohseni S, Shahabinezhad A, Hamidianshirazi Y, Goli A, Abolghasemian M et al (2024) Artificial intelligence-based models for prediction of mortality in ICU patients: a scoping review. J Intensive Care Med. https://doi.org/10.1177/08850666241277134

Article  PubMed  Google Scholar 

Ryan L, Lam C, Mataraso S, Allen A, Green-Saxena A, Pellegrini E et al (2020) Mortality prediction model for the triage of COVID-19, pneumonia, and mechanically ventilated ICU patients: a retrospective study. Ann Med Surg (Lond). 59:207–16. https://doi.org/10.1016/j.amsu.2020.09.044

Article  PubMed  PubMed Central  Google Scholar 

Kumar AA (2022) Mortality prediction in the ICU: the daunting task of predicting the unpredictable. Indian J Crit Care Med. 26(1):13–4. https://doi.org/10.5005/jp-journals-10071-24063

Article  PubMed  PubMed Central  Google Scholar 

Xia J, Pan S, Zhu M, Cai G, Yan M, Su Q et al (2019) A long short-term memory ensemble approach for improving the outcome prediction in intensive care unit. Comput Math Methods Med. 2019:8152713. https://doi.org/10.1155/2019/8152713

Article  PubMed  PubMed Central  Google Scholar 

Knaus WA, Draper EA, Wagner DP, Zimmerman JE (1985) APACHE II: a severity of disease classification system. Crit Care Med 13(10):818–829

Article  CAS  PubMed  Google Scholar 

Vincent JL, Moreno R, Takala J, Willatts S, De Mendonça A, Bruining H et al (1996) The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med. 22(7):707–10. https://doi.org/10.1007/bf01709751

Article  CAS  PubMed  Google Scholar 

Le Gall JR, Lemeshow S, Saulnier F (1993) A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. JAMA. 270(24):2957–63. https://doi.org/10.1001/jama.270.24.2957

Article  PubMed  Google Scholar 

Tian Y, Yao Y, Zhou J, Diao X, Chen H, Cai K et al (2021) Dynamic APACHE II score to predict the outcome of intensive care unit patients. Front Med (Lausanne). 8:744907. https://doi.org/10.3389/fmed.2021.744907

Article  PubMed  Google Scholar 

Yuan Q, Li W, Yang K, Guo J, Zheng Y (2024) Predictive mortality of the prognostic nutritional index combined with APACHE II Score for critically ill tuberculosis patients. Am J Trop Med Hyg. 111(5):1027–33. https://doi.org/10.4269/ajtmh.23-0661

Article  CAS  PubMed  Google Scholar 

Lopez Bernal J, Soumerai S, Gasparrini A (2018) A methodological framework for model selection in interrupted time series studies. J Clin Epidemiol. 103:82–91. https://doi.org/10.1016/j.jclinepi.2018.05.026

Article  CAS  PubMed  Google Scholar 

Perotte A, Ranganath R, Hirsch JS, Blei D, Elhadad N (2015) Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis. J Am Med Inform Assoc. 22(4):872–80. https://doi.org/10.1093/jamia/ocv024

Article  PubMed  PubMed Central  Google Scholar 

Nemati S, Holder A, Razmi F, Stanley MD, Clifford GD, Buchman TG (2018) An interpretable machine learning model for accurate prediction of sepsis in the ICU. Crit Care Med. 46(4):547–53. https://doi.org/10.1097/ccm.0000000000002936

Article  PubMed  PubMed Central  Google Scholar 

LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature. 521(7553):436–44. https://doi.org/10.1038/nature14539

Article  CAS  PubMed  Google Scholar 

Iima M, Mizuno R, Kataoka M, Tsuji K, Yamazaki T, Minami A et al (2024) Deep learning applied to diffusion-weighted imaging for differentiating malignant from benign breast tumors without lesion segmentation. Radiol Artif Intell. https://doi.org/10.1148/ryai.240206

Article  Google Scholar 

Zhang N, Zhang H, Li S, Wu W, Luo P, Liu Z et al (2024) Uncovering the predictive and immunomodulatory potential of transient receptor potential melastatin family-related CCNE1 in pan-cancer. Mol Cancer. 23(1):258. https://doi.org/10.1186/s12943-024-02169-7

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhou Y, Mei S, Wang J, Xu Q, Zhang Z, Qin S et al (2024) Development and validation of a deep learning-based framework for automated lung CT segmentation and acute respiratory distress syndrome prediction: a multicenter cohort study. EClinicalMedicine. 75:102772. https://doi.org/10.1016/j.eclinm.2024.102772

Article  PubMed  PubMed Central  Google Scholar 

Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput. 9(8):1735–80. https://doi.org/10.1162/neco.1997.9.8.1735

Article  CAS  PubMed  Google Scholar 

Ge W, Huh JW, Park YR, Lee JH, Kim YH, Turchin A (2018) An interpretable ICU mortality prediction model based on logistic regression and recurrent neural networks with LSTM units. AMIA Annu Symp Proc. 2018:460–9

PubMed  PubMed Central  Google Scholar 

Gandin I, Scagnetto A, Romani S, Barbati G (2021) Interpretability of time-series deep learning models: a study in cardiovascular patients admitted to Intensive care unit. J Biomed Inform. 121:103876. https://doi.org/10.1016/j.jbi.2021.103876

Article  PubMed  Google Scholar 

Wernly B, Mamandipoor B, Baldia P, Jung C, Osmani V (2021) Machine learning predicts mortality in septic patients using only routinely available ABG variables: a multi-centre evaluation. Int J Med Inform. 145:104312. https://doi.org/10.1016/j.ijmedinf.2020.104312

Article  PubMed  Google Scholar 

Awad A, Bader-El-Den M, McNicholas J, Briggs J (2017) Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach. Int J Med Inform. 108:185–95. https://doi.org/10.1016/j.ijmedinf.2017.10.002

Article  PubMed  Google Scholar 

Zhu Y, Zhang R, Ye X, Liu H, Wei J (2022) SAPS III is superior to SOFA for predicting 28-day mortality in sepsis patients based on Sepsis 3.0 criteria. Int J Infect Dis. 114:135–41. https://doi.org/10.1016/j.ijid.2021.11.015

Article  CAS  PubMed  Google Scholar 

Zhang YY, Zhou XB, Wang QZ, Zhu XY (2017) Quality of reporting of multivariable logistic regression models in Chinese clinical medical journals. Medicine (Baltimore). 96(21):e6972. https://doi.org/10.1097/md.0000000000006972

Article  PubMed  PubMed Central  Google Scholar 

He J, Lin J, Duan M (2021) Application of machine learning to predict acute kidney disease in patients with sepsis associated acute kidney injury. Front Med (Lausanne). 8:792974. https://doi.org/10.3389/fmed.2021.792974

Article  PubMed  PubMed Central  Google Scholar 

Song X, Liu X, Liu F, Wang C (2021) Comparison of machine learning and logistic regression models in predicting acute kidney injury: a systematic review and meta-analysis. Int J Med Inform. 151:104484. https://doi.org/10.1016/j.ijmedinf.2021.104484

Article  PubMed  Google Scholar