MACHINE LEARNING--BASED PREDICTION OF CHEMOTHERAPY TOXICITY IN COLORECTAL CANCER: A PERSONALIZED RISK STRATIFICATION APPROACH.
Background: Machine learning models learn feature connections from data to learn general behavior. The goal was to build a prediction model to identify the percentage of patients with colorectal cancer who are at increased risk of chemotherapy-induced toxicity and to determine the factors that affec...
| Publicado en: | Scientific Culture Vol. 12; no. 5 Part 1; pp. 942 - 953 |
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| Formato: | Artículo |
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University of the Aegean
2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=193975242&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 193975242 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 24080071 I6HU jtl: Scientific Culture issn: 24080071 maglogo: N pubinfo: dt: 2026 vid: 12 iid: 5 Part 1 pid: 47715 pub: University of the Aegean artinfo: ui: 193975242 10.5281/zenodo.12511075 ppf: 942 ppct: 11 formats: tig: atl: MACHINE LEARNING--BASED PREDICTION OF CHEMOTHERAPY TOXICITY IN COLORECTAL CANCER: A PERSONALIZED RISK STRATIFICATION APPROACH. aug: au: Rajendran, Ohmini Krishnamurthy affil: Consultant, MBBS, MD Radiodiagnosis, KIMS Hospital and Research Centre Krishna Rajendra Road, Parvathipuram, Vishweshwarapura, Basavanagudi, Bengaluru, Karnataka 560004 su: Chemotherapy complications Colorectal cancer Artificial intelligence Individualized medicine Risk assessment Random forest algorithms Machine learning Prediction models sug: subj: Chemotherapy complications Colorectal cancer Artificial intelligence Individualized medicine Risk assessment Random forest algorithms Machine learning Prediction models keyword: Artificial Intelligence Chemotherapy Toxicity Metastatic Colorectal Cancer Prediction Model ab: Background: Machine learning models learn feature connections from data to learn general behavior. The goal was to build a prediction model to identify the percentage of patients with colorectal cancer who are at increased risk of chemotherapy-induced toxicity and to determine the factors that affect treatment-related side effects. Methods: Ninety-five features of the health of 74 patients prior to the first round of chemotherapy were chosen for training data, using general toxicity as the predictor. Following data processing, Random Forest models were constructed to balance accuracy and interpretability. Results: We developed a machine learning predictor that ranks numerical and categorical features for toxicity. Conclusions: The use of artificial intelligence to predict and manage toxicities in the treatment of colorectal cancer is a major step forward in the direction of more individualized and precise medical care. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2026 holdings: @attributes: islocal: N |
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