Augmented Intelligence to Predict 30-day Mortality in Patients with Cancer

Abstract

Aim: An augmented intelligence tool to predict short-term mortality risk among patients with cancer could help identify those in need of actionable interventions or palliative care services. Patients & methods: An algorithm to predict 30-day mortality risk was developed using socioeconomic and clinical data from patients in a large community hematology/oncology practice. Patients were scored weekly; algorithm performance was assessed using dates of death in patients’ electronic health records. Results: For patients scored as highest risk for 30-day mortality, the event rate was 4.9% (vs 0.7% in patients scored as low risk; a 7.4-times greater risk). Conclusion: The development and validation of a decision tool to accurately identify patients with cancer who are at risk for short-term mortality is feasible.

Reducing Avoidable Emergency Visits and Hospitalizations With Patient Risk-Based Prescriptive Analytics: A Quality Improvement Project at an Oncology Care Model Practice

“…Following Plan-Do-Study-Act (PDSA) methodology, 8 we implemented the Jvion Care Optimization and Recommendation Enhancement augmented intelligence (AI) tool, which applied continuous machine learning (ML) to predict risk of preventable harm (avoidable ACU) and generated patient-specific recommendations. [9][10][11][12] At their discretion, nurses contacted at-risk patients with interventions to avert the ACU.…”

Recent advances in artificial intelligence applications for supportive and palliative care in cancer patients

“…We found that the most common use of AI for predictive modeling in SPC was focused on mortality. Many studies in Table 1 and approximately half of all studies included in total focused on predicting mortality as a clinical outcome, which includes predicting short-term mortality risk 14▪,15,18▪,24▪ and survival over a longer horizon 19–21,22▪▪,23▪▪,25,29. Mortality risk and survival time were both usually predicted using machine learning (ML) models that analyze various patient factors such as clinical parameters, changes during treatment, and symptoms 14▪,15,18▪,20,21,22▪▪,23▪▪,24▪,25.…”

“…The accuracy of ML and deep learning (DL) models is typically evaluated by their area under the curve (AUC) value, which measures the accuracy of predictions and a model’s discriminative ability where 1.0 represents the highest possible AUC score indicating perfect discrimination 11,39. The models in Table 1 had AUC values between 0.70 and 0.92 indicating generally good model performance 14▪,15,18▪,19–21,23▪▪,24▪,25,26,40. Interestingly, models predicting 60-day and especially 180-day mortality had lower AUC values compared to 30-day mortality models 19,20,24▪,40, suggesting that predicting acute mortality risk could be more reliable than prediction over a longer term.…”

“…The models in Table 1 had AUC values between 0.70 and 0.92 indicating generally good model performance 14▪,15,18▪,19–21,23▪▪,24▪,25,26,40. Interestingly, models predicting 60-day and especially 180-day mortality had lower AUC values compared to 30-day mortality models 19,20,24▪,40, suggesting that predicting acute mortality risk could be more reliable than prediction over a longer term.…”

“…We found that the most common use of AI for predictive modeling in SPC was focused on mortality. Many studies in Table 1 and approximately half of all studies included in total focused on predicting mortality as a clinical outcome, which includes predicting short-term mortality risk [14 ▪ ,15,18 [19,20,24 ▪ ,40], suggesting that predicting acute mortality risk could be more reliable than prediction over a longer term.…”