Browsing by Author "Park, Daniel"

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    Data-driven automated classification algorithms for acute health conditions: applying PheNorm to COVID-19 disease
    (American Medical Informatics Association, 2024) Smith, Joshua C.; Williamson, Brian D.; Cronkite, David J.; Park, Daniel; Whitaker, Jill M.; McLemore, Michael F.; Osmanski, Joshua T.; Winter, Robert; Ramaprasan, Arvind; Kelley, Ann; Shea, Mary; Wittayanukorn, Saranrat; Stojanovic, Danijela; Zhao, Yueqin; Toh, Sengwee; Johnson, Kevin B.; Aronoff, David M.; Carrell, David S.; Medicine, School of Medicine
    Objectives: Automated phenotyping algorithms can reduce development time and operator dependence compared to manually developed algorithms. One such approach, PheNorm, has performed well for identifying chronic health conditions, but its performance for acute conditions is largely unknown. Herein, we implement and evaluate PheNorm applied to symptomatic COVID-19 disease to investigate its potential feasibility for rapid phenotyping of acute health conditions. Materials and methods: PheNorm is a general-purpose automated approach to creating computable phenotype algorithms based on natural language processing, machine learning, and (low cost) silver-standard training labels. We applied PheNorm to cohorts of potential COVID-19 patients from 2 institutions and used gold-standard manual chart review data to investigate the impact on performance of alternative feature engineering options and implementing externally trained models without local retraining. Results: Models at each institution achieved AUC, sensitivity, and positive predictive value of 0.853, 0.879, 0.851 and 0.804, 0.976, and 0.885, respectively, at quantiles of model-predicted risk that maximize F1. We report performance metrics for all combinations of silver labels, feature engineering options, and models trained internally versus externally. Discussion: Phenotyping algorithms developed using PheNorm performed well at both institutions. Performance varied with different silver-standard labels and feature engineering options. Models developed locally at one site also worked well when implemented externally at the other site. Conclusion: PheNorm models successfully identified an acute health condition, symptomatic COVID-19. The simplicity of the PheNorm approach allows it to be applied at multiple study sites with substantially reduced overhead compared to traditional approaches.
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    Parents’ Communication Work in the Management of Food Allergies
    (Taylor & Francis, 2021-02-02) Bute, Jennifer J.; Bowers, Clarissa; Park, Daniel; Communication Studies, School of Liberal Arts
    The National Academies of Sciences, Engineering, and Medicine (NASEM) has identified food allergies as a critical public health issue that significantly affects quality of life for patients and their families. Despite the crisis-level status of food allergies, especially in children, there are currently no curative treatments. As a result, impacted families must learn how to carry the burden of disease management. Using an expanded application of the concept of communication work, this study features data from interviews with 26 parents of food allergic children and explores how parents navigate the nuances of food allergy maintenance while negotiating and preserving valued relationships and identities through everyday talk. Results revealed that parents used communication to legitimate food allergy, balance potential face-threats with identity and relational goals, and coordinate care with spouses. Due to the lack of therapeutic treatment options, we found that parents utilize communication work, which is both demanding and effortful, as a form of disease management.