Hamed Helisaz: Machine Learning for Detecting Missing Heart Medications
Hamed Helisaz, Data Scientist, Co-Founder of GranTAZ, shared a post on LinkedIn about a recent article he and his colleagues co-authored, published in JTH, adding:
“Our recent publication has been featured by NEJM Group
We utilize Machine Learning to show that missing heart medications just one day a week poses a much higher risk than previously thought. Global guidelines traditionally considered an 80% adherence rate ‘good enough’ but our advanced computer modeling proved that patients actually need 90% to 95% adherence to safely minimize the risk of stroke.”
1 Title: Optimal adherence thresholds for oral anticoagulants in patients with atrial fibrillation using machine learning and population administrative data
Authors: Abdollah Safari , Hamed Helisaz , Mina Tadrous , Marc W Deyell , Jason G Andrade , Shahrzad Salmasi , Adenike Adelakun , Kristian B Filion , Mary A De Vera , Peter Loewen
Read the Full Article on JTH.
2 Title: Defining Optimal Adherence to Oral Anticoagulants in Patients with Atrial Fibrillation
Authors: Paul S. Mueller
Read the Full Article on NEJM.
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