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

Classification result from CAESAR showing all 4,683 phenotypes (gray) with severe (red) and mild (pink) phenotype labels from the reference standard. All 4,683 phenotypes plotted using CAESAR’s dimensions 1 and 2 of the scaled 1-proximity matrix. Severe phenotypes are colored red, mild phenotypes are colored pink and phenotypes not in the reference standard are colored gray. Notice that most of the severe phenotypes are in the lower right hand portion of the plot while the “mild” space is found in the lower left hand portion.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig6: Classification result from CAESAR showing all 4,683 phenotypes (gray) with severe (red) and mild (pink) phenotype labels from the reference standard. All 4,683 phenotypes plotted using CAESAR’s dimensions 1 and 2 of the scaled 1-proximity matrix. Severe phenotypes are colored red, mild phenotypes are colored pink and phenotypes not in the reference standard are colored gray. Notice that most of the severe phenotypes are in the lower right hand portion of the plot while the “mild” space is found in the lower left hand portion.

Mentions: CAESAR used all 4,683 phenotypes plotted on the scaled 1-proximity for each phenotype [34] shown in Figure 6 with the reference standard overlaid on top. Notice that phenotypes cluster by severity class (i.e., mild or severe) with a “mild” space (lower left) and a “severe” space (lower right), and phenotypes of intermediate severity in between.Figure 6


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)

Classification result from CAESAR showing all 4,683 phenotypes (gray) with severe (red) and mild (pink) phenotype labels from the reference standard. All 4,683 phenotypes plotted using CAESAR’s dimensions 1 and 2 of the scaled 1-proximity matrix. Severe phenotypes are colored red, mild phenotypes are colored pink and phenotypes not in the reference standard are colored gray. Notice that most of the severe phenotypes are in the lower right hand portion of the plot while the “mild” space is found in the lower left hand portion.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig6: Classification result from CAESAR showing all 4,683 phenotypes (gray) with severe (red) and mild (pink) phenotype labels from the reference standard. All 4,683 phenotypes plotted using CAESAR’s dimensions 1 and 2 of the scaled 1-proximity matrix. Severe phenotypes are colored red, mild phenotypes are colored pink and phenotypes not in the reference standard are colored gray. Notice that most of the severe phenotypes are in the lower right hand portion of the plot while the “mild” space is found in the lower left hand portion.
Mentions: CAESAR used all 4,683 phenotypes plotted on the scaled 1-proximity for each phenotype [34] shown in Figure 6 with the reference standard overlaid on top. Notice that phenotypes cluster by severity class (i.e., mild or severe) with a “mild” space (lower left) and a “severe” space (lower right), and phenotypes of intermediate severity in between.Figure 6

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