Automated chart review utilizing natural language processing algorithm for asthma predictive index.

Background: Thus far, no algorithms have been developed to automatically extract patients who meet Asthma Predictive Index (API) criteria from the Electronic health records (EHR) yet. Our objective is to develop and validate a natural language processing (NLP) algorithm to identify patients that meet API criteria.

Methods: This is a cross-sectional study nested in a birth cohort study in Olmsted County, MN.

Ascertainment of asthma prognosis using natural language processing from electronic medical records.

NLP algorithm successfully determined asthma prognosis (i.e., no remission, long-term remission, and intermittent remission) by taking into account asthma symptoms documented in EMR, and addressed the limitations of billing code- based asthma outcome assessment.

Application of a Natural Language Processing Algorithm to Asthma Ascertainment. An Automated Chart Review.

Rationale: Difficulty of asthma ascertainment and its associated methodologic heterogeneity have created significant barriers to asthma care and research.

Objectives: We evaluated the validity of an existing natural language processing (NLP) algorithm for asthma criteria to enable an automated chart review using electronic medical records (EMRs).

Methods: The study was designed as a retrospective birth cohort study using a random sample of 500 subjects from the 1997-2007 Mayo Birth Cohort who were born at Mayo Clinic and enrolled in primary pediatric care at Mayo Clinic Rochester.

What accounts for the association between late preterm births and risk of asthma?

Background: Although results of many studies have indicated an increased risk of asthma in former late preterm (LPT) infants, most of these studies did not fully address covariate imbalance.

Objective: To compare the cumulative frequency of asthma in a population-based cohort of former LPT infants to that of matched term infants in their early childhood, when accounting for covariate imbalance.

Methods: From a population-based birth cohort of children born 2002-2006 in Olmsted County, Minnesota, we assessed a random sample of LPT (34 to 36 6/7 weeks) and frequency-matched term (37 to 40 6/7 weeks) infants.

Risk of Asthma in Late Preterm Infants: A Propensity Score Approach.

Background: The risk of asthma, specifically in former late preterm infants, has not been well defined. Covariate imbalance and lack of controlling for this has led to inconsistent results in prior studies.

Objective: The objective of this study was to determine the risk of asthma in former late preterm infants using a propensity score approach.

ADHD and learning disabilities in former late preterm infants: a population-based birth cohort.

Background And Objective: Previous studies suggest that former late preterm infants are at increased risk for learning and behavioral problems compared with term infants. These studies have primarily used referred clinical samples of children followed only until early school age. Our objective was to determine the cumulative incidence of attention deficit/hyperactivity disorder (ADHD) and learning disabilities (LD) in former late preterm versus term infants in a population-based birth cohort.