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In Future oncology (London, England)

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.

Gajra Ajeet, Zettler Marjorie E, Miller Kelly A, Blau Sibel, Venkateshwaran Swetha S, Sridharan Shreenath, Showalter John, Valley Amy W, Frownfelter John G


augmented intelligence, decision tool, machine learning