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

Abstract

Background: 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.

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: artificial intelligence; cancer mortality; clinical prediction models; end-of-life care; machine learning.

Figure 1

PRISMA (Preferred Reporting Item for Systematic Reviews and Meta-Analyses) flowchart diagram for the study selection process. ML: machine learning.

Figure 2

Risk of bias assessment for the included studies. Risk of bias assessment result for each included study using prediction model risk of bias assessment tool [15,35-49].

Figure 3

Pooled AUROC by machine learning (ML) algorithm. ANN: artificial neural network; AUROC: area under the receiver operating characteristic curve; BPM: Bayes point machine; DT: decision tree; GBT: gradient-boosted tree; LR: logistic regression; RF: random forest; SGB: stochastic gradient boosting; SVM: support vector machine.