ASH 2022 | Machine learning-based predictive model to identify individuals at risk to develop multiple myeloma
Moshe Mittelman, MD, Tel Aviv Sourasky (Ichilov) Medical Center, Tel Aviv University, discusses data from an observational retrospective study that investigated the use of a machine learning approach to develop a predictive model that identifies individuals at risk of developing multiple myeloma. Using electronic medical records extracted from Clalit Health Services (CHS), machine learning was employed to compare variables between patients with multiple myeloma and matched controls to create a predictive model of multiple myeloma development. The model uses parameters that are easily assessable by clinicians to calculate a risk score for the development of multiple myeloma within five years. This interview took place at the 64th ASH Annual Meeting and Exposition congress in New Orleans, LA.
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Disclosures
Novartis: Research Funding; Takeda: Honoraria, Research Funding; Janssen: Research Funding; Roche: Research Funding; BMS: Research Funding; Celgene: Research Funding; Medison: Research Funding; Gilead: Research Funding.
