首页|National Cancer Centre Singapore Reports Findings in Cancer (Towards proactive p alliative care in oncology: developing an explainable EHR-based machine learning model for mortality risk prediction)
National Cancer Centre Singapore Reports Findings in Cancer (Towards proactive p alliative care in oncology: developing an explainable EHR-based machine learning model for mortality risk prediction)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Cancer is the subject of a report. According to news reporting from Singapore, Singapore, by NewsRx jo urnalists, research stated, “Ex-ante identification of the last year in life fac ilitates a proactive palliative approach. Machine learning models trained on ele ctronic health records (EHR) demonstrate promising performance in cancer prognos tication.” Financial supporters for this research include National Medical Research Council , Lien Centre for Palliative Care. The news correspondents obtained a quote from the research from National Cancer Centre Singapore, “However, gaps in literature include incomplete reporting of m odel performance, inadequate alignment of model formulation with implementation use-case, and insufficient explainability hindering trust and adoption in clinic al settings. Hence, we aim to develop an explainable machine learning EHR-based model that prompts palliative care processes by predicting for 365-day mortality risk among patients with advanced cancer within an outpatient setting. Our coho rt consisted of 5,926 adults diagnosed with Stage 3 or 4 solid organ cancer betw een July 1, 2017, and June 30, 2020 and receiving ambulatory cancer care within a tertiary center. The classification problem was modelled using Extreme Gradien t Boosting (XGBoost) and aligned to our envisioned use-case: ‘Given a prediction point that corresponds to an outpatient cancer encounter, predict for mortality within 365-days from prediction point, using EHR data up to 365-days prior.’ Th e model was trained with 75% of the dataset (n = 39,416 outpatient encounters) and validated on a 25% hold-out dataset (n = 13,122 o utpatient encounters). To explain model outputs, we used Shapley Additive Explan ations (SHAP) values. Clinical characteristics, laboratory tests and treatment d ata were used to train the model. Performance was evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision-re call curve (AUPRC), while model calibration was assessed using the Brier score. In total, 17,149 of the 52,538 prediction points (32.6%) had a mort ality event within the 365-day prediction window. The model demonstrated an AURO C of 0.861 (95% CI 0.856-0.867) and AUPRC of 0.771. The Brier scor e was 0.147, indicating slight overestimations of mortality risk. Explanatory di agrams utilizing SHAP values allowed visualization of feature impacts on predict ions at both the global and individual levels. Our machine learning model demons trated good discrimination and precision-recall in predicting 365-day mortality risk among individuals with advanced cancer.”
SingaporeSingaporeAsiaCancerCybo rgsElectronic Medical RecordsEmerging TechnologiesHealth and MedicineInf ormation TechnologyMachine LearningOncologyPalliative and Supportive CareRisk and Prevention
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