Computational Model and Methods for Selecting Clinical Trial Subjects to Reduce Heterogeneity

    公开(公告)号:US20240185965A1

    公开(公告)日:2024-06-06

    申请号:US18284130

    申请日:2022-03-30

    CPC classification number: G16H10/20 G16H20/70 G16H50/20

    Abstract: Clinical study populations require reduced heterogeneity to properly determine effectiveness of treatments. In an embodiment, a method of verifying eligibility of a subject for a treatment includes representing the subject's symptoms in a rating scale as a vector. The method computes an anomaly score based on the vector of the subject and multiple vectors representing rating scales of other subjects. The method ranks, based on the anomaly score, the subject with a likelihood of contributing to a subgroup of patients having a common element structure of the rating scale. The method enriches a study population in a clinical trial prior to randomization, the enriched study population having a reduced heterogeneity. Therefore, the method can verify diseases or conditions or diagnoses of subjects for eligibility for a clinical trial or for other purposes.

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