Utilization of allogeneic hematopoietic stem cell transplantation among patients with newly diagnosed acute myeloid leukemia in California: a population-based linked dataset study.

Acute myeloid leukemia (AML) often requires allogeneic hematopoietic cell transplantation (alloHCT) for cure, but historically alloHCT has been strikingly underutilized. Reasons for this remain uncertain at the population level. We examined alloHCT utilization over time and explored associations between demographic/healthcare factors and use of alloHCT by age group (AYA 15-39y, adult 40-64y, older adult 65-79y) using a linked dataset merging the Center for International Blood and Marrow Transplant Research, California Cancer Registry, and California Patient Discharge Database.

Predicting venous thromboembolism (VTE) risk in cancer patients using machine learning.

Background: The association between cancer and venous thromboembolism (VTE) is well-established with cancer patients accounting for approximately 20% of all VTE incidents. In this paper, we have performed a comparison of machine learning (ML) methods to traditional clinical scoring models for predicting the occurrence of VTE in a cancer patient population, identified important features (clinical biomarkers) for ML model predictions, and examined how different approaches to reducing the number of features used in the model impact model performance.

Methods: We have developed an ML pipeline including three separate feature selection processes and applied it to routine patient care data from the electronic health records of 1910 cancer patients at the University of California Davis Medical Center.