Explainable machine learning for cancer patient segmentation and hospital resource optimization in oncology care

National Institute of Statistics and Geography (INEGI). Statistics on World Cancer Day. 2024; Available from: https://www.inegi.org.mx/contenidos/saladeprensa/aproposito/2025/EAP_DMvsCancer25.pdf. Accessed 24 Mar 2025.

Siegel RL, Miller KD, Nargis S, Wender RC, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73:17–48. https://doi.org/10.3322/caac.21763.

Article  Google Scholar 

World Health Organization (WHO). Global cancer burden growing, amidst mounting need for services. 2024; Available from: https://www.who.int/news/item/01-02-2024-global-cancer-burden-growing--amidst-mounting-need-for-services. Accessed 24 Mar 2025.

Binder A, Bockmayr M, Hägele M, Wienert S, Heim D, Hellweg K, et al. Morphological and molecular breast cancer profiling through explainable machine learning. Nat Mach Intell. 2021;3:355–66. https://doi.org/10.1038/s42256-021-00303-4.

Article  Google Scholar 

Flamand L. Cancer and social inequalities in Mexico 2020. Mexico City: El Colegio de México; 2021; Available from: https://desigualdades.colmex.mx/cancer/informe-cancer-desigualdades-2020.pdf. Accessed 22 Feb 2025.

Grant RW, McCloskey J, Hatfield M, Uratsu C, Ralston JD, Bayliss E, et al. Use of latent class analysis and k-means clustering to identify complex patient profiles. JAMA Netw Open. 2020;3:e2029068. https://doi.org/10.1001/jamanetworkopen.2020.29068.

Article  Google Scholar 

Blumenthal D, Abrams MK. Tailoring complex care management for high-need, high-cost patients. JAMA. 2016;316:1657–8. https://doi.org/10.1001/jama.2016.12388.

Article  Google Scholar 

Figueroa JF, Jha AK. Approach for achieving effective care for high-need patients. JAMA Intern Med. 2018;178:845–6. https://doi.org/10.1001/jamainternmed.2018.0823.

Article  Google Scholar 

Khanmohammadi S, Adibeig N, Shanehbandy S. An improved overlapping k-means clustering method for medical applications. Expert Syst Appl. 2017;67:12–8. https://doi.org/10.1016/j.eswa.2016.09.025.

Article  Google Scholar 

Shpigelman E, Hochstadt A, Coster D, Merdler I, Ghantous E, Szekely Y, et al. Clustering of clinical and echocardiographic phenotypes of COVID-19 patients. Sci Rep. 2023;13:1–10. https://doi.org/10.1038/s41598-023-35449-1.

Article  Google Scholar 

Wang Y, Zhao Y, Therneau TM, Atkinson EJ, Tafti AP, Zhang N, et al. Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records. J Biomed Inform. 2020;102:103364. https://doi.org/10.1016/j.jbi.2019.103364.

Article  Google Scholar 

Salehi A, Khedmati M. Identifying at-risk patients for congenital heart disease using integrated predictive models and fuzzy clustering analysis: a cross-sectional study. Heliyon. 2024;10:e39609. https://doi.org/10.1016/j.heliyon.2024.e39609.

Article  Google Scholar 

Kourou K, Exarchos KP, Papaloukas C, Sakaloglou P, Exarchos T, Fotiadis DI. Applied machine learning in cancer research: a systematic review for patient diagnosis, classification and prognosis. Computational and Structural Biotechnology Journal. 2021;19:5546–55. https://doi.org/10.1016/j.csbj.2021.10.006.

Article  Google Scholar 

Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2:719–31. https://doi.org/10.1038/s41551-018-0305-z.

Article  Google Scholar 

Chowdhury MM, Ayon RS, Hossain MS. An investigation of machine learning algorithms and data augmentation techniques for diabetes diagnosis using class imbalanced BRFSS dataset. Healthc Anal. 2024;5:100297. https://doi.org/10.1016/j.health.2023.100297.

Article  Google Scholar 

Tajally AR, Zarean J, Bozorgi-Amiri A, Tavakkoli-Moghaddam R. Deep uncertainty quantification algorithms for confidence-aware hope classification of breast cancer patients based on their cognitive features. Appl Soft Comput. 2025;172:112860. https://doi.org/10.1016/j.asoc.2025.112860.

Article  Google Scholar 

Wu CP, Sleiman J, Fakhry B, Chedraoui C, Attaway A, Bhattacharyya A, Bleecker ER, Erdemir A, Hu B, Kethireddy S, et al. Novel machine learning identifies five asthma phenotypes using cluster analysis of real-world data. J Allergy Clin Immunol Pract. 2024;12:2084–e914. https://doi.org/10.1016/j.jaip.2024.04.035.

Article  Google Scholar 

Nnoaham KE, Cann KF. Can cluster analyses of linked healthcare data identify unique population segments in a general practice-registered population? BMC Public Health. 2020;20:1–10. https://doi.org/10.1186/s12889-020-08930-z.

Article  Google Scholar 

Mosavi NS, Santos MF. Unveiling precision medicine with data mining: discovering patient subgroups and patterns. In: 2023 IEEE Symposium Series on Computational Intelligence (SSCI 2023). IEEE; 2023; pp. 1304–9. https://doi.org/10.1109/SSCI52147.2023.10372022

Foster JC, Mohile SG, Dale W. How does older age influence oncologists’ cancer management? Oncologist. 2010;15:584–92. https://doi.org/10.1634/theoncologist.2009-0198.

Article  Google Scholar 

Ronald A, Hurria A, Cohen HJ, Muss HB. Impact of age and comorbidity on treatment and outcomes in elderly cancer patients. Semin Radiat Oncol. 2012;22:265–71. https://doi.org/10.1016/j.semradonc.2012.05.002.

Article  Google Scholar 

Marxen TJ, Elzinga KE, Clemens MW. The safety of same-day discharge after immediate alloplastic breast reconstruction: a systematic review. Plast Reconstr Surg Glob Open. 2022;10:e4448. https://doi.org/10.1097/GOX.0000000000004448.

Article  Google Scholar 

Campbell KL, Beale C, Taylor LJ, Symonds P. Early catheter removal following laparoscopic radical hysterectomy for cervical cancer: assessment of a new bladder care protocol. J Obstet Gynaecol. 2017;37:845–50. https://doi.org/10.1080/01443615.2017.1328668.

Article  Google Scholar 

Amir A, Ghaferi AA, Birkmeyer JD, Dimick JB. Hospital volume and failure to rescue with high-risk surgery. Med Care. 2011;49:1076–81. https://doi.org/10.1097/MLR.0b013e3182329b97.

Article  Google Scholar 

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