ArticleTranslational cancer research2021
Application of data mining in the provision of in-home medical care for patients with advanced cancer.
Article in Translational cancer research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic models for survival predictions in advanced cancer patients: a systematic review and meta-analysis.BMC palliative care · 2025Pooled it
- Review
- Machine learning applied to electronic health record data in home healthcare: A scoping review.International journal of medical informatics · 2023Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAs the number of patients with cancer rises, home care for patients with advanced disease is becoming increasingly important. To provide guidance for home medical services and hospice care, we investigated the basic information and medical service information of patients with advanced cancer receiving home care by using a data mining algorithm to predict the patients' survival and medical expenses.
methodsData from patients with advanced cancer who received home care in Chongming District (Shanghai, China) between 2016 and 2018 were collected. The medical expenses and survival time of the patients were classified and predicted through the use of random forest algorithms, support-vector machine algorithms, and back-propagation (BP) neural network algorithms.
resultsThe performances of the 3 algorithms in classifying patient survival and predicting medical expenses were compared. The random forest algorithm, support vector machine, and BP neural network in the classification of patient survival had accuracy of 81.94%±6.12%, 74.61%±7.01%, and 72.90%±8.08%, respectively. The standard mean square errors of the regression model for predicting medical expenses were 0.4194±0.2393, 1.1222±0.0648, and 1.2986±0.1762, respectively.
conclusionsThe random forest algorithm is the most suitable prediction model for predicting medical costs and patient survival with the quantity of data currently available. Further optimization of the random forest algorithm could provide guidance and help medical institutions improve the efficiency and quality of home medical services for patients with advanced cancer.
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