Learning to Identify Patients at Risk of Uncontrolled Hypertension Using Electronic Health Records Data
Ramin Mohammadi; Sarthak Jain; Stephen Agboola; Ramya Palacholla; Sagar Kamarthi; Byron C. Wallace · 2019 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
Hypertension is a major risk factor for stroke, cardiovascular disease, and end-stage renal disease, and its prevalence is expected to rise dramatically. Effective hypertension management is thus critical. A particular priority is decreasing the incidence of uncontrolled hypertension. Early identification of patients at risk for uncontrolled hypertension would allow targeted use of personalized, proactive treatments. We develop machine learning models (logistic regression and recurrent neural networks) to stratify patients with respect to the risk of exhibiting uncontrolled hypertension within
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Metadata source: arXiv