Limits...
Development and validation of a classification approach for extracting severity automatically from electronic health records.

Boland MR, Tatonetti NP, Hripcsak G - J Biomed Semantics (2015)

Bottom Line: Further, phenotype-level severity does not change based on the individual patient.CAESAR combines multiple severity measures - number of comorbidities, medications, procedures, cost, treatment time, and a proportional index term.CAESAR enables researchers to measure phenotype severity from EHRs to identify phenotypes that are important for comparative effectiveness research.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, Columbia University, New York, NY USA ; Observational Health Data Sciences and Informatics (OHDSI), Columbia University, 622 West 168th Street, PH-20, New York, NY USA.

ABSTRACT

Background: Electronic Health Records (EHRs) contain a wealth of information useful for studying clinical phenotype-genotype relationships. Severity is important for distinguishing among phenotypes; however other severity indices classify patient-level severity (e.g., mild vs. acute dermatitis) rather than phenotype-level severity (e.g., acne vs. myocardial infarction). Phenotype-level severity is independent of the individual patient's state and is relative to other phenotypes. Further, phenotype-level severity does not change based on the individual patient. For example, acne is mild at the phenotype-level and relative to other phenotypes. Therefore, a given patient may have a severe form of acne (this is the patient-level severity), but this does not effect its overall designation as a mild phenotype at the phenotype-level.

Methods: We present a method for classifying severity at the phenotype-level that uses the Systemized Nomenclature of Medicine - Clinical Terms. Our method is called the Classification Approach for Extracting Severity Automatically from Electronic Health Records (CAESAR). CAESAR combines multiple severity measures - number of comorbidities, medications, procedures, cost, treatment time, and a proportional index term. CAESAR employs a random forest algorithm and these severity measures to discriminate between severe and mild phenotypes.

Results: Using a random forest algorithm and these severity measures as input, CAESAR differentiates between severe and mild phenotypes (sensitivity = 91.67, specificity = 77.78) when compared to a manually evaluated reference standard (k = 0.716).

Conclusions: CAESAR enables researchers to measure phenotype severity from EHRs to identify phenotypes that are important for comparative effectiveness research.

No MeSH data available.


Related in: MedlinePlus

Severity measure correlation matrix. Histograms of each severity measure shown (along the diagonal) with pairwise correlation graphs (lower triangle) and correlation coefficients and p-values (upper triangle). Notice the condition length is the least correlated with the other measures while number of medications and number of procedures are highly correlated (r = 0.88, p < 0.001).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4386082&req=5

Fig2: Severity measure correlation matrix. Histograms of each severity measure shown (along the diagonal) with pairwise correlation graphs (lower triangle) and correlation coefficients and p-values (upper triangle). Notice the condition length is the least correlated with the other measures while number of medications and number of procedures are highly correlated (r = 0.88, p < 0.001).

Mentions: For condition/phenotype information we used data from CUMC EHRs, which was initially recorded using ICD-9 codes. These ICD-9 codes were mapped to SNOMED-CT codes using the OMOP CDM v.4 [2]. For this paper, we used all phenotypes (each phenotype being a unique SNOMED-CT code) with prevalence of at least 0.0001 in our hospital database. This constituted 4,683 phenotypes. We then analyzed the distribution of each of the five measures and E-PSI among the 4,683 phenotypes. Figure 2 shows the correlation matrix among the 5 severity measures and E-PSI.Figure 2


Development and validation of a classification approach for extracting severity automatically from electronic health records.

Boland MR, Tatonetti NP, Hripcsak G - J Biomed Semantics (2015)

Severity measure correlation matrix. Histograms of each severity measure shown (along the diagonal) with pairwise correlation graphs (lower triangle) and correlation coefficients and p-values (upper triangle). Notice the condition length is the least correlated with the other measures while number of medications and number of procedures are highly correlated (r = 0.88, p < 0.001).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4386082&req=5

Fig2: Severity measure correlation matrix. Histograms of each severity measure shown (along the diagonal) with pairwise correlation graphs (lower triangle) and correlation coefficients and p-values (upper triangle). Notice the condition length is the least correlated with the other measures while number of medications and number of procedures are highly correlated (r = 0.88, p < 0.001).
Mentions: For condition/phenotype information we used data from CUMC EHRs, which was initially recorded using ICD-9 codes. These ICD-9 codes were mapped to SNOMED-CT codes using the OMOP CDM v.4 [2]. For this paper, we used all phenotypes (each phenotype being a unique SNOMED-CT code) with prevalence of at least 0.0001 in our hospital database. This constituted 4,683 phenotypes. We then analyzed the distribution of each of the five measures and E-PSI among the 4,683 phenotypes. Figure 2 shows the correlation matrix among the 5 severity measures and E-PSI.Figure 2

Bottom Line: Further, phenotype-level severity does not change based on the individual patient.CAESAR combines multiple severity measures - number of comorbidities, medications, procedures, cost, treatment time, and a proportional index term.CAESAR enables researchers to measure phenotype severity from EHRs to identify phenotypes that are important for comparative effectiveness research.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, Columbia University, New York, NY USA ; Observational Health Data Sciences and Informatics (OHDSI), Columbia University, 622 West 168th Street, PH-20, New York, NY USA.

ABSTRACT

Background: Electronic Health Records (EHRs) contain a wealth of information useful for studying clinical phenotype-genotype relationships. Severity is important for distinguishing among phenotypes; however other severity indices classify patient-level severity (e.g., mild vs. acute dermatitis) rather than phenotype-level severity (e.g., acne vs. myocardial infarction). Phenotype-level severity is independent of the individual patient's state and is relative to other phenotypes. Further, phenotype-level severity does not change based on the individual patient. For example, acne is mild at the phenotype-level and relative to other phenotypes. Therefore, a given patient may have a severe form of acne (this is the patient-level severity), but this does not effect its overall designation as a mild phenotype at the phenotype-level.

Methods: We present a method for classifying severity at the phenotype-level that uses the Systemized Nomenclature of Medicine - Clinical Terms. Our method is called the Classification Approach for Extracting Severity Automatically from Electronic Health Records (CAESAR). CAESAR combines multiple severity measures - number of comorbidities, medications, procedures, cost, treatment time, and a proportional index term. CAESAR employs a random forest algorithm and these severity measures to discriminate between severe and mild phenotypes.

Results: Using a random forest algorithm and these severity measures as input, CAESAR differentiates between severe and mild phenotypes (sensitivity = 91.67, specificity = 77.78) when compared to a manually evaluated reference standard (k = 0.716).

Conclusions: CAESAR enables researchers to measure phenotype severity from EHRs to identify phenotypes that are important for comparative effectiveness research.

No MeSH data available.


Related in: MedlinePlus