A novel data-driven workflow combining literature and electronic health records to estimate comorbidities burden for a specific disease: a case study on autoimmune comorbidities in patients with celiac disease

Abstract

BackgroundData collected in EHRs have been widely used to identifying specific conditions; however there is still a need for methods to define comorbidities and sources to identify comorbidities burden. We propose an approach to assess comorbidities burden for a specific disease using the literature and EHR data sources in the case of autoimmune diseases in celiac disease (CD).MethodsWe generated a restricted set of comorbidities using the literature (via the MeSH® co-occurrence file). We extracted the 15 most co-occurring autoimmune diseases of the CD. We used mappings of the comorbidities to EHR terminologies: ICD-10 (billing codes), ATC (drugs) and UMLS (clinical reports). Finally, we extracted the concepts from the different data sources. We evaluated our approach using the correlation between prevalence estimates in our cohort and co-occurrence ranking in the literature.ResultsWe retrieved the comorbidities for 741 patients with CD. 18.1% of patients had at least one of the 15 studied autoimmune disorders. Overall, 79.3% of the mapped concepts were detected only in text, 5.3% only in ICD codes and/or drugs prescriptions, and 15.4% could be found in both sources. Prevalence in our cohort were correlated with literature (Spearman’s coefficient 0.789, p = 0.0005). The three most prevalent comorbidities were thyroiditis 12.6% (95% CI 10.1–14.9), type 1 diabetes 2.3% (95% CI 1.2–3.4) and dermatitis herpetiformis 2.0% (95% CI 1.0–3.0).ConclusionWe introduced a process that leveraged the MeSH terminology to identify relevant autoimmune comorbidities of the CD and several data sources from EHRs to phenotype a large population of CD patients. We achieved prevalence estimates comparable to the literature.Electronic supplementary materialThe online version of this article (10.1186/s12911-017-0537-y) contains supplementary material, which is available to authorized users.

Characterizing the Value of Information in Medical Notes

“…Prior work shows that some important phenotypic characteristics can only be inferred from text reports (Shivade et al, 2014). For example, Escudié et al (2017) observed that 92.5% of information regarding autoimmune thyroiditis is only presented in text. Despite the potential valuable information in medical notes, prior work also points out the redundancy in EHRs.…”

Workflow Differences Affect Data Accuracy in Oncologic EHRs: A First Step Toward Detangling the Diagnosis Data Babel

“…Our study expands beyond existing literature by exploring DX code assignment beyond accuracy 1,22,47 and shifts the view of static clinical data as raw analysis material to data as the product of clinical workflows. Our findings are congruent with the existing literature [15][16][17]36 but also unlock new dimensions of oncologic data quality assurance.…”

Phenotype-genotype comorbidity analysis of patients with rare disorders provides insight into their pathological and molecular bases

“…This work contributes to the growing field of co-occurence based phenotype network study. Whilst many previous studies have investigated comorbidity alone, often using electronic health records and billing codes [ 15 , 53 – 56 ], fewer approaches have looked at genes shared between phenotypes [ 22 , 23 , 57 , 58 ]. Moreover, most of these studies have used disease/phenotype-gene mapping taken from databases, rather than using patient data directly.…”