Electronic Health Record Analysis for Personalized Medicine: Predicting Malnutrition-Related Health Outcomes and Secondary Neuropsychiatric Health Concerns.

Abstract

Malnutrition poses risks regarding cognitive, behavioral, and physical well-being. The aim of this study was to investigate the prevalent health issues associated with malnutrition by utilizing electronic health records (EHR) data. The IBM Watson Health, Explorys platform was used to access the EHR data. Two cohorts were created by two queries; patients with a history of malnutrition (n=5180) and patients without a history of malnutrition diagnosis (n= 413890). The log odds ratio and χ2 statistic were used to identify the statistically significant differences between these two cohorts. We found that there were 35 terms that were more common among the cohort with the malnutrition diagnosis. These terms were categorized under developmental anomalies, infectious agents, respiratory system issues, digestive system issues, pregnancy/prenatal problems, mental, behavioral, or neurodevelopmental disorders, diseases of the ear or mastoid process, diseases of the visual system, and chromosomal anomalies. The management of malnutrition in children is a complex problem that can be addressed with a multifactorial approach. Based on the key themes emerging from among the commonly prevalent terms identified in our study, infection prevention, education in appropriate nutritional solutions for digestive health issues, supportive services to address neurodevelopmental needs, and quality prenatal healthcare would constitute beneficial prevention efforts. Improving our understanding of malnutrition is necessary to develop new interventions for prevention and treatment.