{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T08:11:48Z","timestamp":1761984708401,"version":"build-2065373602"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,1]]},"DOI":"10.7759\/cureus.95874","type":"journal-article","created":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T08:07:16Z","timestamp":1761984436000},"source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning-Based Mortality Prediction Model Using Minimal Features to Assist Decision-Making in End-of-Life Care"],"prefix":"10.7759","author":[{"given":"Tae Hoon","family":"Kong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jae Ha","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mi Sun","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2017.09.016","article-title":"Palliative radiation therapy in the last 30 days of life: a systematic review","volume":"125","author":"Park KR","year":"2017","unstructured":"Park KR, Lee CG, Tseng YD, Liao JJ, Reddy S, Bruera E, Yennurajalingam S. Palliative radiation therapy in the last 30 days of life: a systematic review. Radiother Oncol. 2017, 125:193-9. 10.1016\/j.radonc.2017.09.016","journal-title":"Radiother Oncol"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/djv278","article-title":"Prevalence and predictors of inappropriate delivery of palliative thoracic radiotherapy for metastatic lung cancer","volume":"107","author":"Koshy M","year":"2015","unstructured":"Koshy M, Malik R, Mahmood U, Husain Z, Weichselbaum RR, Sher DJ. Prevalence and predictors of inappropriate delivery of palliative thoracic radiotherapy for metastatic lung cancer. J Natl Cancer Inst. 2015, 107:djv278. 10.1093\/jnci\/djv278","journal-title":"J Natl Cancer Inst"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1111\/1754-9485.13635","article-title":"Thirty-day mortality after palliative radiotherapy in advanced cancer patients: optimizing end-of-life care in Asia","volume":"68","author":"Kim MS","year":"2024","unstructured":"Kim MS, Cha H, You SH, Kim S. Thirty-day mortality after palliative radiotherapy in advanced cancer patients: optimizing end-of-life care in Asia. J Med Imaging Radiat Oncol. 2024, 68:307-15. 10.1111\/1754-9485.13635","journal-title":"J Med Imaging Radiat Oncol"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1200\/CCI.20.00072","article-title":"Machine learning in oncology: methods, applications, and challenges","volume":"4","author":"Bertsimas D","year":"2020","unstructured":"Bertsimas D, Wiberg H. Machine learning in oncology: methods, applications, and challenges. JCO Clin Cancer Inform. 2020, 4:885-94. 10.1200\/CCI.20.00072","journal-title":"JCO Clin Cancer Inform"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.canlet.2020.03.032","article-title":"Machine learning in oncology: a clinical appraisal","volume":"481","author":"Cuocolo R","year":"2020","unstructured":"Cuocolo R, Caruso M, Perillo T, Ugga L, Petretta M. Machine learning in oncology: a clinical appraisal. Cancer Lett. 2020, 481:55-62. 10.1016\/j.canlet.2020.03.032","journal-title":"Cancer Lett"},{"key":"ref6","article-title":"Toxicity and response criteria of the Eastern Cooperative Oncology Group","volume":"5","author":"Oken MM","year":"1982","unstructured":"Oken MM, Creech RH, Tormey DC, et al.. Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol. 1982, 5:649-55.","journal-title":"Am J Clin Oncol"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2015.03.023","article-title":"30 day mortality in adult palliative radiotherapy - a retrospective population based study of 14,972 treatment episodes","volume":"115","author":"Spencer K","year":"2015","unstructured":"Spencer K, Morris E, Dugdale E, et al.. 30 day mortality in adult palliative radiotherapy - a retrospective population based study of 14,972 treatment episodes. Radiother Oncol. 2015, 115:264-71. 10.1016\/j.radonc.2015.03.023","journal-title":"Radiother Oncol"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1634\/theoncologist.2015-0015","article-title":"Surprise questions for survival prediction in patients with advanced cancer: a multicenter prospective cohort study","volume":"20","author":"Hamano J","year":"2015","unstructured":"Hamano J, Morita T, Inoue S, et al.. Surprise questions for survival prediction in patients with advanced cancer: a multicenter prospective cohort study. Oncologist. 2015, 20:839-44. 10.1634\/theoncologist.2015-0015","journal-title":"Oncologist"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3174\/ajnr.A6918","article-title":"Prediction of clinical outcome in patients with large-vessel acute ischemic stroke: performance of machine learning versus SPAN-100","volume":"42","author":"Jiang B","year":"2021","unstructured":"Jiang B, Zhu G, Xie Y, et al.. Prediction of clinical outcome in patients with large-vessel acute ischemic stroke: performance of machine learning versus SPAN-100. AJNR Am J Neuroradiol. 2021, 42:240-6. 10.3174\/ajnr.A6918","journal-title":"AJNR Am J Neuroradiol"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2018.0926","article-title":"Development and application of a machine learning approach to assess short-term mortality risk among patients with cancer starting chemotherapy","volume":"1","author":"Elfiky AA","year":"2018","unstructured":"Elfiky AA, Pany MJ, Parikh RB, Obermeyer Z. Development and application of a machine learning approach to assess short-term mortality risk among patients with cancer starting chemotherapy. JAMA Netw Open. 2018, 1:e180926. 10.1001\/jamanetworkopen.2018.0926","journal-title":"JAMA Netw Open"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1186\/s12904-025-01696-4","article-title":"Prognostic models for survival predictions in advanced cancer patients: a systematic review and meta-analysis","volume":"24","author":"Fung MY","year":"2025","unstructured":"Fung MY, Wong YL, Cheung KM, Bao KH, Sung WW. Prognostic models for survival predictions in advanced cancer patients: a systematic review and meta-analysis. BMC Palliat Care. 2025, 24:54. 10.1186\/s12904-025-01696-4","journal-title":"BMC Palliat Care"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1139809","article-title":"Effects of peripheral blood neutrophil\/lymphocyte ratio levels and their changes on the prognosis of patients with early cervical cancer","volume":"13","author":"Du JQ","year":"2023","unstructured":"Du JQ, Zhang F, Wang CQ, et al.. Effects of peripheral blood neutrophil\/lymphocyte ratio levels and their changes on the prognosis of patients with early cervical cancer. Front Oncol. 2023, 13:1139809. 10.3389\/fonc.2023.1139809","journal-title":"Front Oncol"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2024.1323192","article-title":"Predictive value of serum albumin levels on cancer survival: a prospective cohort study","volume":"14","author":"Tang Q","year":"2024","unstructured":"Tang Q, Li X, Sun CR. Predictive value of serum albumin levels on cancer survival: a prospective cohort study. Front Oncol. 2024, 14:1323192. 10.3389\/fonc.2024.1323192","journal-title":"Front Oncol"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1055\/a-2146-6879","article-title":"The incidence and effect of different organ metastasis on the prognosis of NSCLC","volume":"72","author":"Wei S","year":"2024","unstructured":"Wei S, Wei W, Wu B, Tian J, Hu P, Pan S, Song X. The incidence and effect of different organ metastasis on the prognosis of NSCLC. Thorac Cardiovasc Surg. 2024, 72:217-26. 10.1055\/a-2146-6879","journal-title":"Thorac Cardiovasc Surg"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-95128-1","article-title":"Preoperative ECOG performance status as a predictor of outcomes in upper tract urothelial cancer surgery","volume":"15","author":"Yang TM","year":"2025","unstructured":"Yang TM, Chen IA, Yu CC, et al.. Preoperative ECOG performance status as a predictor of outcomes in upper tract urothelial cancer surgery. Sci Rep. 2025, 15:10356. 10.1038\/s41598-025-95128-1","journal-title":"Sci Rep"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-30442-5","article-title":"Biological significance and prognostic relevance of peripheral blood neutrophil-to-lymphocyte ratio in soft tissue sarcoma","volume":"8","author":"Chan JY","year":"2018","unstructured":"Chan JY, Zhang Z, Chew W, et al.. Biological significance and prognostic relevance of peripheral blood neutrophil-to-lymphocyte ratio in soft tissue sarcoma. Sci Rep. 2018, 8:11959. 10.1038\/s41598-018-30442-5","journal-title":"Sci Rep"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1136\/bmjspcare-2012-d4020rep","article-title":"Development of prognosis in palliative care study (PiPS) predictor models to improve prognostication in advanced cancer: prospective cohort study","volume":"5","author":"Gwilliam B","year":"2015","unstructured":"Gwilliam B, Keeley V, Todd C, et al.. Development of prognosis in palliative care study (PiPS) predictor models to improve prognostication in advanced cancer: prospective cohort study. BMJ Support Palliat Care. 2015, 5:390-8. 10.1136\/bmjspcare-2012-d4020rep","journal-title":"BMJ Support Palliat Care"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.76227","article-title":"Optimal machine learning models for developing prognostic predictions in patients with advanced cancer","volume":"16","author":"Hamano J","year":"2024","unstructured":"Hamano J, Takeuchi A, Keyaki T, Nose H, Hayashi K. Optimal machine learning models for developing prognostic predictions in patients with advanced cancer. Cureus. 2024, 16:e76227. 10.7759\/cureus.76227","journal-title":"Cureus"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2024.1488118","article-title":"Personalized three-year survival prediction and prognosis forecast by interpretable machine learning for pancreatic cancer patients: a population-based study and an external validation","volume":"14","author":"Teng B","year":"2024","unstructured":"Teng B, Zhang X, Ge M, Miao M, Li W, Ma J. Personalized three-year survival prediction and prognosis forecast by interpretable machine learning for pancreatic cancer patients: a population-based study and an external validation. Front Oncol. 2024, 14:1488118. 10.3389\/fonc.2024.1488118","journal-title":"Front Oncol"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.21873\/cdp.10284","article-title":"Survival following palliative radiotherapy for head and neck squamous cell carcinoma: examining treatment indications in elderly patients","volume":"4","author":"Katano A","year":"2024","unstructured":"Katano A, Minamitani M, Tongyu G, Ohira S, Yamashita H. Survival following palliative radiotherapy for head and neck squamous cell carcinoma: examining treatment indications in elderly patients. Cancer Diagn Progn. 2024, 4:46-50. 10.21873\/cdp.10284","journal-title":"Cancer Diagn Progn"}],"container-title":["Cureus"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cureus.com\/articles\/413575-machine-learning-based-mortality-prediction-model-using-minimal-features-to-assist-decision-making-in-end-of-life-care","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T08:07:17Z","timestamp":1761984437000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cureus.com\/articles\/413575-machine-learning-based-mortality-prediction-model-using-minimal-features-to-assist-decision-making-in-end-of-life-care"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,1]]},"references-count":20,"URL":"https:\/\/doi.org\/10.7759\/cureus.95874","relation":{},"ISSN":["2168-8184"],"issn-type":[{"value":"2168-8184","type":"print"}],"subject":[],"published":{"date-parts":[[2025,11,1]]}}}