Limits...
A knowledge-based, automated method for phenotyping in the EHR using only clinical pathology reports.

Yahi A, Tatonetti NP - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: Central to this goal is, EHR-phenotyping, also known as cohort identification, which remains a significant challenge.We present Ontology-driven Reports-based Phenotyping from Unique Signatures (ORPheUS), an automated approach to EHR-phenotyping.To do this we identify unique signatures of abnormal clinical pathology reports that correspond to pre-defined medical terms from biomedical ontologies.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, Department of Systems Biology, Department of Medicine, Columbia University, New York, NY, USA.

ABSTRACT
The secondary use of electronic health records (EHR) represents unprecedented opportunities for biomedical discovery. Central to this goal is, EHR-phenotyping, also known as cohort identification, which remains a significant challenge. Complex phenotypes often require multivariate and multi-scale analyses, ultimately leading to manually created phenotype definitions. We present Ontology-driven Reports-based Phenotyping from Unique Signatures (ORPheUS), an automated approach to EHR-phenotyping. To do this we identify unique signatures of abnormal clinical pathology reports that correspond to pre-defined medical terms from biomedical ontologies. By using only the clinical pathology, or "lab", reports we are able to mitigate clinical biases enabling researchers to explore other dimensions of the EHR. We used ORPheUS to generate signatures for 858 diseases and validated against reference cohorts for Type 2 Diabetes Mellitus (T2DM) and Atrial Fibrillation (AF). Our results suggest that our approach, using solely clinical pathology reports, is an effective as a primary screening tool for automated clinical phenotyping.

No MeSH data available.


Related in: MedlinePlus

(left) Precision and Recall curves for Diabetes Mellitus signatures tested on T2DM patients
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4525265&req=5

f1-2090751: (left) Precision and Recall curves for Diabetes Mellitus signatures tested on T2DM patients

Mentions: We computed the precision and recall curves for the Diabetes Mellitus in 83,246 patients with T2DM as determined by the reference standard. We observed that of the 14 T2DM specific ATs in the signature, we only found up to 10 simultaneously in a single patient’s record. The precision is significantly better than by chance and increases above 80% with when at least 4 ATs are matched. At 6 or more distinct ATs the recall falls to below 5% (Figure 1).


A knowledge-based, automated method for phenotyping in the EHR using only clinical pathology reports.

Yahi A, Tatonetti NP - AMIA Jt Summits Transl Sci Proc (2015)

(left) Precision and Recall curves for Diabetes Mellitus signatures tested on T2DM patients
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4525265&req=5

f1-2090751: (left) Precision and Recall curves for Diabetes Mellitus signatures tested on T2DM patients
Mentions: We computed the precision and recall curves for the Diabetes Mellitus in 83,246 patients with T2DM as determined by the reference standard. We observed that of the 14 T2DM specific ATs in the signature, we only found up to 10 simultaneously in a single patient’s record. The precision is significantly better than by chance and increases above 80% with when at least 4 ATs are matched. At 6 or more distinct ATs the recall falls to below 5% (Figure 1).

Bottom Line: Central to this goal is, EHR-phenotyping, also known as cohort identification, which remains a significant challenge.We present Ontology-driven Reports-based Phenotyping from Unique Signatures (ORPheUS), an automated approach to EHR-phenotyping.To do this we identify unique signatures of abnormal clinical pathology reports that correspond to pre-defined medical terms from biomedical ontologies.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, Department of Systems Biology, Department of Medicine, Columbia University, New York, NY, USA.

ABSTRACT
The secondary use of electronic health records (EHR) represents unprecedented opportunities for biomedical discovery. Central to this goal is, EHR-phenotyping, also known as cohort identification, which remains a significant challenge. Complex phenotypes often require multivariate and multi-scale analyses, ultimately leading to manually created phenotype definitions. We present Ontology-driven Reports-based Phenotyping from Unique Signatures (ORPheUS), an automated approach to EHR-phenotyping. To do this we identify unique signatures of abnormal clinical pathology reports that correspond to pre-defined medical terms from biomedical ontologies. By using only the clinical pathology, or "lab", reports we are able to mitigate clinical biases enabling researchers to explore other dimensions of the EHR. We used ORPheUS to generate signatures for 858 diseases and validated against reference cohorts for Type 2 Diabetes Mellitus (T2DM) and Atrial Fibrillation (AF). Our results suggest that our approach, using solely clinical pathology reports, is an effective as a primary screening tool for automated clinical phenotyping.

No MeSH data available.


Related in: MedlinePlus