Yourman LC, Lee SJ, Schonberg MA, Widera EW, Smith AK. Prognostic indices for older adults: a systematic review. J Am Med Assoc. 2012;307(2):182–92. https://doi.org/10.1001/jama.2011.1966.

Article  Google Scholar 

Clarke M, Kennedy K, MacDonagh R. Development of a clinical prediction model to calculate patient life expectancy: the measure of actuarial life expectancy (MALE). Med Decis Mak. 2009;29(2):239–46. https://doi.org/10.1177/0272989X08327114.

Article  MATH  Google Scholar 

Kalra S, Basourakos S, Abouassi A, Achim M, Volk RJ, Hoffman KE, Davis JW, Kim J. The implications of ageing and life expectancy in prostate cancer treatment. Nat Rev Urol. 2016;13(5):289–95. https://doi.org/10.1038/nrurol.2016.52.

Article  Google Scholar 

Seow H, O’Leary E, Perez R, Tanuseputro P. Access to palliative care by disease trajectory: a population-based cohort of Ontario decedents. BMJ Open. 2018;8(4):021147. https://doi.org/10.1136/bmjopen-2017-021147.

Article  Google Scholar 

Gomes B, Calanzani N, Gysels M, Hall S, Higginson IJ. Heterogeneity and changes in preferences for dying at home: a systematic review. BMC Palliat Care. 2013;12(1):1–13. https://doi.org/10.1186/1472-684X-12-7.

Article  Google Scholar 

Hsu AT, Garner RE. Associations between the receipt of inpatient palliative care and acute care outcomes: a retrospective study. Health Rep. 2020;31(10):3–13. https://doi.org/10.25318/82-003-x202001000001-eng.

Article  MATH  Google Scholar 

Brinkman-Stoppelenburg A, Rietjens JA, Heide A. The effects of advance care planning on end-of-life care: a systematic review. Palliat Med. 2014;28(8):1000–25. https://doi.org/10.1177/0269216314526272.

Article  Google Scholar 

Huber MT, Highland JD, Krishnamoorthi VR, Tang JW-Y. Utilizing the electronic health record to improve advance care planning: a systematic review. Am J Hosp Palliat Med. 2018;35(3):532–41. https://doi.org/10.1177/1049909117715217.

Article  Google Scholar 

Taseen R, Ethier J-F. Expected clinical utility of automatable prediction models for improving palliative and end-of-life care outcomes: toward routine decision analysis before implementation. J Am Med Inform Assoc. 2021;28(11):2366–78. https://doi.org/10.1093/jamia/ocab140.

Article  MATH  Google Scholar 

Heyland DK, Allan DE, Rocker G, Dodek P, Pichora D, Gafni A. Canadian Researchers at the End-of-Life Network (CARENET): discussing prognosis with patients and their families near the end of life: impact on satisfaction with end-of-life care. Open Med. 2009;3(2):101.

Google Scholar 

Yamaguchi T, Maeda I, Hatano Y, Mori M, Shima Y, Tsuneto S, Kizawa Y, Morita T, Yamaguchi T, Aoyama M, Miyashita M. Effects of end-of-life discussions on the mental health of bereaved family members and quality of patient death and care. J Pain Symptom Manage. 2017;54(1):17–26. https://doi.org/10.1016/j.jpainsymman.2017.03.008.

Article  Google Scholar 

Lund S, Richardson A, May C. Barriers to advance care planning at the end of life: an explanatory systematic review of implementation studies. PLoS ONE. 2015;10(2):0116629. https://doi.org/10.1371/journal.pone.0116629.

Article  Google Scholar 

walraven C. The Hospital-patient One-year Mortality Risk score accurately predicted long-term death risk in hospitalized patients. J Clin Epidemiol. 2014;67(9):1025–34. https://doi.org/10.1016/j.jclinepi.2014.05.003.

Article  MATH  Google Scholar 

walraven C, McAlister FA, Bakal JA, Hawken S, Donzé J. External validation of the Hospital-patient One-year Mortality Risk (HOMR) model for predicting death within 1 year after hospital admission. Can Med Assoc J. 2015;187(10):725–33. https://doi.org/10.1503/cmaj.150209.

Article  Google Scholar 

walraven C, Forster AJ. The HOMR-now! model accurately predicts 1-year death risk for hospitalized patients on admission. Am J Med. 2017;130(8):991–999116. https://doi.org/10.1016/j.amjmed.2017.03.008.

Article  Google Scholar 

Wegier P, Koo E, Ansari S, Kobewka D, O’Connor E, Wu P, Steinberg L, Bell C, Walton T, walraven C, Embuldeniya G, Costello J, Downar J. mHOMR: a feasibility study of an automated system for identifying inpatients having an elevated risk of 1-year mortality. BMJ Qual Saf. 2019;28(12):971–9. https://doi.org/10.1136/bmjqs-2018-009285.

Article  Google Scholar 

Guo A, Foraker R, White P, Chivers C, Courtright K, Moore N. Using electronic health records and claims data to identify high-risk patients likely to benefit from palliative care. Am J Manage Care. 2021. https://doi.org/10.37765/ajmc.2021.88578.

Article  Google Scholar 

Beeksma M, Verberne S, Bosch A, Das E, Hendrickx I, Groenewoud S. Predicting life expectancy with a long short-term memory recurrent neural network using electronic medical records. BMC Med Inform Decis Mak. 2019;19(1):1–15. https://doi.org/10.1186/s12911-019-0775-2.

Article  Google Scholar 

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

Article  MATH  Google Scholar 

Chung J, Gulcehre C, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling, 2014. arXiv preprint arXiv:1412.3555. https://doi.org/10.48550/arXiv.1412.3555.

LeCun Y, Boser B, Denker J, Henderson D, Howard R, Hubbard W, Jackel L. Handwritten digit recognition with a back-propagation network. In: Advances in neural information processing systems, 1989;2. https://proceedings.neurips.cc/paper_files/paper/1989/file/53c3bce66e43be4f209556518c2fcb54-Paper.pdf.

Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Lu, Polosukhin I. Attention is all you need. In: Advances in neural information processing systems, 2017;30. https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf.

Al Olaimat M, Bozdag S, Initiative ADN. TA-RNN: an attention-based time-aware recurrent neural network architecture for electronic health records. Bioinformatics. 2024;40:169–79. https://doi.org/10.1093/bioinformatics/btae264.

Article  Google Scholar 

Saggu S, Daneshvar H, Samavi R, Pires P, Sassi RB, Doyle TE, Zhao J, Mauluddin A, Duncan L. Prediction of emergency department revisits among child and youth mental health outpatients using deep learning techniques. BMC Med Inform Decis Mak. 2024;24(1):42. https://doi.org/10.1186/s12911-024-02450-1.

Article  Google Scholar 

Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G. The graph neural network model. IEEE Trans Neural Netw. 2008;20(1):61–80. https://doi.org/10.1109/TNN.2008.2005605.

Article  MATH  Google Scholar 

Suo Q, Ma F, Yuan Y, Huai M, Zhong W, Zhang A, Gao J. Personalized disease prediction using a CNN-based similarity learning method. In: 2017 IEEE international conference on bioinformatics and biomedicine (BIBM), 2017;811–816. https://doi.org/10.1109/BIBM.2017.8217759.

Suo Q, Ma F, Yuan Y, Huai M, Zhong W, Gao J, Zhang A. Deep patient similarity learning for personalized healthcare. IEEE Trans Nanobiosci. 2018;17(3):219–27. https://doi.org/10.1109/TNB.2018.2837622.

Article  MATH  Google Scholar 

Ju R, Zhou P, Wen S, Wei W, Xue Y, Huang X, Yang X. 3D-CNN-SPP: a patient risk prediction system from electronic health records via 3D CNN and spatial pyramid pooling. IEEE Trans Emerg Top Comput Intell. 2020;5(2):247–61. https://doi.org/10.1109/TETCI.2019.2960474.

Article  Google Scholar 

Luo J, Ye M, Xiao C, Ma F. HiTANet: hierarchical time-aware attention networks for risk prediction on electronic health records. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, 2020;647–656. https://doi.org/10.1145/3394486.3403107.

Li Y, Mamouei M, Salimi-Khorshidi G, Rao S, Hassaine A, Canoy D, Lukasiewicz T, Rahimi K. Hi-BEHRT: hierarchical transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health records. IEEE J Biomed Health Inform. 2022;27(2):1106–17. https://doi.org/10.1109/JBHI.2022.3224727.

Article  Google Scholar 

Yang Z, Mitra A, Liu W, Berlowitz D, Yu H. TransformEHR: transformer-based encoder-decoder generative model to enhance prediction of disease outcomes using electronic health records. Nat Commun. 2023;14(1):7857. https://doi.org/10.1038/s41467-023-43715-z.

Article  MATH  Google Scholar 

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–30. https://doi.org/10.48550/arXiv.1201.0490.

Article  MathSciNet  MATH  Google Scholar 

Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S. Pytorch: An imperative style, high-performance deep learning library. In: Advances in neural information processing systems, 2019;32. https://proceedings.neurips.cc/paper_files/paper/2019/file/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf.

Kingma DP, Ba J. Adam: A method for stochastic optimization, 2014. arXiv preprint arXiv:1412.6980. https://doi.org/10.48550/arXiv.1412.6980.

Akiba T, Sano S, Yanase T, Ohta T, Koyama M. Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 2019;2623–2631. https://doi.org/10.1145/3292500.3330701.

Wilcoxon F. Individual comparisons by ranking methods. New York: Springer; 1992. p. 196–202. https://doi.org/10.1007/978-1-4612-4380-9_16.

Book  MATH  Google Scholar 

Pencina MJ, D’Agostino RB Sr, D’Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27(2):157–72. https://doi.org/10.1002/sim.2929.

Article  MathSciNet  MATH  Google Scholar 

Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3(1):32–5.

Article  MATH  Google Scholar 

Fisher A, Rudin C, Dominici F. All models are wrong, but many are useful: learning a variable’s importance by studying an entire class of prediction models simultaneously. J Mach Learn Res. 2019;20(177):1–81.

MathSciNet  MATH  Google Scholar 

Herman R, Vanderheyden M, Vavrik B, Beles M, Palus T, Nelis O, Goethals M, Verstreken S, Dierckx R, Penicka M, Heggermont W, Bartunek J. Utilizing longitudinal data in assessing all-cause mortality in patients hospitalized with heart failure. ESC Heart Fail. 2022;9(5):3575–84. https://doi.org/10.1002/ehf2.14011.