Multi-level machine learning models for estimating the risk of delay between cancer diagnosis and treatment initiation in a large group of cancer patients.
By Dr. Sanchari Sinha Dutta, Ph.D.Aug 17 2023Reviewed by Benedette Cuffari, M.Sc. A study published in JAMA Network Open describes the utility of multi-level machine learning models in estimating the risk of delay between cancer diagnosis and treatment initiation in a large group of cancer patients.
The timely implementation of effective treatments can be achieved by identifying patients who are at an increased risk of health disparities. This must be accompanied by improvements in care coordination and patient navigation services; however, these approaches are resource intensive. Thus, a more effective approach would be identifying patients who are at a greater risk of diagnostic delays and subsequently targeting them for timely treatment.
Patient data related to cancer diagnosis-first treatment interval, health and demographic characteristics including race, ethnicity, laboratory findings, and comorbidities, as well as neighborhood-level health variables, were incorporated into the machine learning models. Important observations A total of 6,409 patients were included in the study, 14% of whom belonged to the most socioeconomically deprived neighborhoods. About 25% of the study cohort experienced a delay of more than 60 days between cancer diagnosis and treatment initiation.
Regarding neighborhood-level social determinants, the model predicted that patients belonging to the most socioeconomically deprived areas were more likely to experience a delay as compared to those belonging to the least socioeconomically deprived areas. While neighborhoods with high Hispanic populations were identified as a risk factor for treatment delays, patients residing in areas with a high Black population were less likely to experience a delay.
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