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gamAID: Greedy CP tensor decomposition for supervised EHR-based disease trajectory differentiation.

Jette Henderson, Joyce Ho, Joydeep Ghosh

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 25, 2017
    PubMed
    Summary

    We developed gamAID, a new method to find disease risk factors from patient data. It identifies patient subgroups, helping predict who may develop chronic kidney disorder (CKD).

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    Area of Science:

    • Computational biology
    • Medical informatics
    • Data science

    Background:

    • Diabetic patients are at increased risk for developing chronic kidney disorder (CKD).
    • Early identification of at-risk patients is crucial for timely intervention.
    • Current methods may not fully capture complex patient data patterns.

    Purpose of the Study:

    • To introduce gamAID, a novel supervised nonnegative tensor factorization method.
    • To extract clinically relevant phenotypes from medical count data.
    • To identify patient characteristics predictive of CKD development in diabetic individuals.

    Main Methods:

    • gamAID iteratively extracts phenotypes from tensors derived from medical count data.
    • The method employs supervised nonnegative tensor factorization.

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  • The approach was validated using data from diabetic patients with and without subsequent CKD diagnosis.

    • Main Results:

    • gamAID successfully identified phenotypes associated with CKD risk in diabetic patients.
    • The extracted phenotypes characterize patients predisposed to developing the disease.
    • The method demonstrates potential for early disease risk prediction.

    Conclusions:

    • gamAID is a promising tool for phenotype extraction and disease risk prediction.
    • This method can aid in identifying diabetic patients at high risk for CKD.
    • Further research can explore gamAID's application in other complex diseases.