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Biostatistics
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Interpretable Machine Learning
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Risk Score Models
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Tuberculosis
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A Computationally Efficient Algorithm for Producing Risk Score Models with Applications to Tuberculosis Diagnosis and Treatment Adherence
Description:
Risk score models are simple scoring systems that map patient characteristics to the probability of an outcome occurring. These models are popular with clinicians because …
Year:
2024
Contributor:
Eglinton, Hannah Jones (creator)
Paul, Alice (Advisor)
Hogan, Joseph (Reader)
Brown University. Department of Biostatistics (sponsor)
Genre:
theses
Subject:
Tuberculosis
Interpretable Machine Learning
Risk Score Models
Collection:
Public Health
Biostatistics
Theses and Dissertations
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