Can I read Development of an Algorithm for Finding Pertussis Episodes in a Population-based Electronic Health Record Database on EtoBox?

Development of an Algorithm for Finding Pertussis Episodes in a Population-based Electronic Health Record Database by Chathuri Daluwatte; Maryia Dvaretskaya; Sam Ekhtiari; Paul Hayat; Martin Montmerle; Sachin Mathur; Denis Macina is a Medicine article available to read on EtoBox.

What is Development of an Algorithm for Finding Pertussis Episodes in a Population-based Electronic Health Record Database about?

## ABSTRACT While tetanus-diphtheria-acellular pertussis (Tdap) vaccines for adolescents and adults were licensed in 2005 and immunization strategies proposed, the burden of pertussis in this population remains under-recognized mainly due to atypical disease presentation, undermining efforts to optimize protection through vaccination. We developed a machine learning algorithm to identify undiagnosed/misdiagnosed pertussis episodes in patients diagnosed with acute respiratory disease (ARD) using signs, diseases and symptoms from clinician notes and demographic information within electronic health-care records (Optum Humedica repository [2007–2019]). We used two patient cohorts aged ≥11 years to develop the model: a positive pertussis cohort (4,515 episodes in 4,316 patients) and a negative pertussis (ARD) cohort (4,573,445 episodes and patients), defined using ICD 9/10 codes. To improve contrast between positive pertussis and negative pertussis (ARD) episodes, only episodes with ≥7 symptoms were selected. LightGBM was used as the machine learning model for pertussis episode identification. Model validity was determined using laboratory-confirmed pertussis positive and negative cohor

Who reads Development of an Algorithm for Finding Pertussis Episodes in a Population-based Electronic Health Record Database?

Author

Chathuri Daluwatte; Maryia Dvaretskaya; Sam Ekhtiari; Paul Hayat; Martin Montmerle; Sachin Mathur; Denis Macina

Publisher

Informa UK Limited

Published

2023

Language

EN

Field

Medicine (Health Sciences)