Machine Learning-Based Short-Term Mortality Prediction Models for Patients With Cancer Using Electronic Health Record Data: Systematic Review and Critical Appraisal

• Published in: JMIR medical informatics Vol. 10; no. 3; p. e33182
• Main Authors: Lu, Sheng-Chieh, Xu, Cai, Nguyen, Chandler H, Geng, Yimin, Pfob, André, Sidey-Gibbons, Chris
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 14.03.2022
• Subjects: Artificial intelligence; Cancer; Datasets; Machine learning; Mortality; Palliative care; Review; Systematic review; clinical prediction models; machine learning; artificial intelligence; cancer mortality; end-of-life care
• ISSN: 2291-9694; 2291-9694
• DOI: 10.2196/33182

In the United States, national guidelines suggest that aggressive cancer care should be avoided in the final months of life. However, guideline compliance currently requires clinicians to make judgments based on their experience as to when a patient is nearing the end of their life. Machine learning (ML) algorithms may facilitate improved end-of-life care provision for patients with cancer by identifying patients at risk of short-term mortality. This study aims to summarize the evidence for applying ML in ≤1-year cancer mortality prediction to assist with the transition to end-of-life care for patients with cancer. We searched MEDLINE, Embase, Scopus, Web of Science, and IEEE to identify relevant articles. We included studies describing ML algorithms predicting ≤1-year mortality in patients of oncology. We used the prediction model risk of bias assessment tool to assess the quality of the included studies. We included 15 articles involving 110,058 patients in the final synthesis. Of the 15 studies, 12 (80%) had a high or unclear risk of bias. The model performance was good: the area under the receiver operating characteristic curve ranged from 0.72 to 0.92. We identified common issues leading to biased models, including using a single performance metric, incomplete reporting of or inappropriate modeling practice, and small sample size. We found encouraging signs of ML performance in predicting short-term cancer mortality. Nevertheless, no included ML algorithms are suitable for clinical practice at the current stage because of the high risk of bias and uncertainty regarding real-world performance. Further research is needed to develop ML models using the modern standards of algorithm development and reporting.