Genetic and Survey Data Improves Performance of Machine Learning Model for Long COVID.
Wei-Qi WeiChristopher GuardoSrushti GandireddyChao YanHenry OngVern KerchbergerAlyson L DicksonEmily Rose PfaffHiral MasterMelissa BasfordNguyen TranSalvatore MancusoToufeeq SyedZhong-Ming ZhaoQiPing FengMelissa A HaendelChristopher LuntGeoffrey S GinsburgChristopher G ChuteJoshua DennyDan RodenPublished in: Research square (2023)
Over 200 million SARS-CoV-2 patients have or will develop persistent symptoms (long COVID). Given this pressing research priority, the National COVID Cohort Collaborative (N3C) developed a machine learning model using only electronic health record data to identify potential patients with long COVID. We hypothesized that additional data from health surveys, mobile devices, and genotypes could improve prediction ability. In a cohort of SARS-CoV-2 infected individuals (n=17,755) in the All of Us program, we applied and expanded upon the N3C long COVID prediction model, testing machine learning infrastructures, assessing model performance, and identifying factors that contributed most to the prediction models. For the survey/mobile device information and genetic data, extreme gradient boosting and a convolutional neural network delivered the best performance for predicting long COVID, respectively. Combined survey, genetic, and mobile data increased specificity and the Area Under Curve the Receiver Operating Characteristic score versus the original N3C model.
Keyphrases
- sars cov
- electronic health record
- coronavirus disease
- machine learning
- big data
- respiratory syndrome coronavirus
- cross sectional
- artificial intelligence
- clinical decision support
- end stage renal disease
- healthcare
- quality improvement
- public health
- health information
- adverse drug
- copy number
- gene expression
- chronic kidney disease
- social media
- data analysis
- peritoneal dialysis