ArticleJMIR cancer2019
Developing Machine Learning Algorithms for the Prediction of Early Death in Elderly Cancer Patients: Usability Study.
Article in JMIR cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- From mechanistic models to artificial intelligence: exploring the potential of digital twins in geriatric oncology.Frontiers in artificial intelligence · 2026Article
- Making Community-Based Palliative Care Eligibility Determinations: Palliative Care Team Member Perspectives on Access to Information and Algorithm Use.The American journal of hospice & palliative care · 2025Article
- A model for predicting physical function upon discharge of hospitalized older adults in Taiwan-a machine learning approach based on both electronic health records and comprehensive geriatric assessment.Frontiers in medicine · 2023Article
- Machine Learning-Based Short-Term Mortality Prediction Models for Patients With Cancer Using Electronic Health Record Data: Systematic Review and Critical Appraisal.JMIR medical informatics · 2022Review
- Survivorship Care of Older Adults With Cancer: Priority Areas for Clinical Practice, Training, Research, and Policy.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2021Review
- Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches.JMIR medical informatics · 2020Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe importance of classifying cancer patients into high- or low-risk groups has led many research teams, from the biomedical and bioinformatics fields, to study the application of machine learning (ML) algorithms. The International Society of Geriatric Oncology recommends the use of the comprehensive geriatric assessment (CGA), a multidisciplinary tool to evaluate health domains, for the follow-up of elderly cancer patients. However, no applications of ML have been proposed using CGA to classify elderly cancer patients.
objectiveThe aim of this study was to propose and develop predictive models, using ML and CGA, to estimate the risk of early death in elderly cancer patients.
methodsThe ability of ML algorithms to predict early mortality in a cohort involving 608 elderly cancer patients was evaluated. The CGA was conducted during admission by a multidisciplinary team and included the following questionnaires: mini-mental state examination (MMSE), geriatric depression scale-short form, international physical activity questionnaire-short form, timed up and go, Katz index of independence in activities of daily living, Charlson comorbidity index, Karnofsky performance scale (KPS), polypharmacy, and mini nutritional assessment-short form (MNA-SF). The 10-fold cross-validation algorithm was used to evaluate all possible combinations of these questionnaires to estimate the risk of early death, considered when occurring within 6 months of diagnosis, in a variety of ML classifiers, including Naive Bayes (NB), decision tree algorithm J48 (J48), and multilayer perceptron (MLP). On each fold of evaluation, tiebreaking is handled by choosing the smallest set of questionnaires.
resultsIt was possible to select CGA questionnaire subsets with high predictive capacity for early death, which were either statistically similar (NB) or higher (J48 and MLP) when compared with the use of all questionnaires investigated. These results show that CGA questionnaire selection can improve accuracy rates and decrease the time spent to evaluate elderly cancer patients.
conclusionsA simplified predictive model aiming to estimate the risk of early death in elderly cancer patients is proposed herein, minimally composed by the MNA-SF and KPS. We strongly recommend that these questionnaires be incorporated into regular geriatric assessment of older patients with cancer.
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