In electronic health records (EHRs) data analysis, nonparametric regression and classification using International Classification of Disease (ICD) codes as covariates remain understudied. Automated methods have been developed over the years for predicting biomedical responses using EHRs, but relatively less attention has been paid to developing patient similarity measures that use ICD codes and chronic conditions, where a chronic condition is defined as a set of ICD codes. We address this problem by first developing a string kernel function for measuring the similarity between a pair of primary chronic conditions, represented as subsets of ICD codes. Second, we extend this similarity measure to a family of covariance functions on subsets of chronic conditions. This family is used in developing Gaussian process (GP) priors for Bayesian nonparametric regression and classification using diagnoses and other demographic information as covariates. Markov chain Monte Carlo (MCMC) algorithms are used for posterior inference and predictions. The proposed methods are tuning free, so they are ideal for automated prediction of biomedical responses depending on chronic conditions. We evaluate the practical performance of our method on EHR data collected from 1660 patients at the University of Iowa Hospitals and Clinics (UIHC) with six different primary cancer sites. Our method provides better sensitivity and specificity than its competitors in classifying different primary cancer sites and estimates the marginal associations between chronic conditions and primary cancer sites.
- Award ID(s):
- 1920920
- NSF-PAR ID:
- 10228537
- Date Published:
- Journal Name:
- Applied Clinical Informatics
- Volume:
- 12
- Issue:
- 01
- ISSN:
- 1869-0327
- Page Range / eLocation ID:
- 010 to 016
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Background Aspirin‐exacerbated respiratory disease (AERD) is the triad of asthma, nasal polyposis, and sensitivity to cyclooxygenase‐1 inhibitors. Treatment options include medical management, surgical intervention, and aspirin desensitization (AsaD).
Methods AERD patients were identified using the MarketScan Database from 2009 to 2015. Patients were included using International Classification of Diseases, 9th edition (ICD‐9) codes for asthma, nasal polyposis, and drug allergy. Treatments were determined by Current Procedural Terminology (CPT) codes for drug desensitization and endonasal procedures. Geographic trends and timing of interventions between those exposed and not exposed to desensitization were explored.
Results A total of 5628 patients met inclusion criteria for AERD, with mean age 46 years, 60% female; 395 (7%) underwent AsaD and 2171 (39%) underwent sinus surgery. Among patients who were desensitized, 229 (58%) underwent surgery, of whom 201 (88%) had surgery prior to AsaD (median [quartile 1, quartile 3]; 61 days [30, 208] prior to desensitization). For patients undergoing surgery following AsaD (n = 46), surgery was performed a median of 302 (163, 758) days after AsaD. Nineteen patients had multiple surgeries post‐AsaD with median time between surgeries being 734 days (312, 1484); 261 patients were not desensitized to aspirin but did undergo multiple surgeries, with the median of the median time between surgeries being 287 days (15, 617), which is shorter than for patients post‐AsaD (
p < 0.001).Conclusion A very small percentage of AERD patients undergo AsaD. Patients who had AsaD underwent surgery approximately 2 months prior to AsaD. Patients who underwent AsaD experienced an increased time between surgeries compared to patients who did not undergo AsaD.
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Conclusion Machine learning algorithms were used to predict PCOS based on a large at-risk population. This approach may guide early detection of PCOS within EHR-interfaced populations to facilitate counseling and interventions that may reduce long-term health consequences. Our model illustrates the potential benefits of an artificial intelligence-enabled provider assistance tool that can be integrated into the EHR to reduce delays in diagnosis. However, model validation in other hospital-based populations is necessary.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1055/s-0040-1721012
