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Automating Identification of Multiple Chronic Conditions in Clinical Practice Guidelines.

Leung TI, Jalal H, Zulman DM, Dumontier M, Owens DK, Musen MA, Goldstein MK - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: Then, we compared the co-prevalence of common pairs of chronic conditions from Medicare CCW data to the frequency of disease-comorbidity pairs in CPGs.Our results show that some disease-comorbidity pairs occur more frequently in CPGs than others.Knowledge extracted from CPG text in this way may be useful to inform gaps in guideline recommendations regarding MCC and therefore identify potential opportunities for guideline improvement.

View Article: PubMed Central - PubMed

Affiliation: Department of Veterans Affairs, VA Palo Alto Health Care System, Palo Alto, CA ; Center for Primary Care and Outcomes Research, Stanford University, Stanford, CA ; Division of General Medical Disciplines, Stanford University, Stanford, CA.

ABSTRACT
Many clinical practice guidelines (CPGs) are intended to provide evidence-based guidance to clinicians on a single disease, and are frequently considered inadequate when caring for patients with multiple chronic conditions (MCC), or two or more chronic conditions. It is unclear to what degree disease-specific CPGs provide guidance about MCC. In this study, we develop a method for extracting knowledge from single-disease chronic condition CPGs to determine how frequently they mention commonly co-occurring chronic diseases. We focus on 15 highly prevalent chronic conditions. We use publicly available resources, including a repository of guideline summaries from the National Guideline Clearinghouse to build a text corpus, a data dictionary of ICD-9 codes from the Medicare Chronic Conditions Data Warehouse (CCW) to construct an initial list of disease terms, and disease synonyms from the National Center for Biomedical Ontology to enhance the list of disease terms. First, for each disease guideline, we determined the frequency of comorbid condition mentions (a disease-comorbidity pair) by exactly matching disease synonyms in the text corpus. Then, we developed an annotated reference standard using a sample subset of guidelines. We used this reference standard to evaluate our approach. Then, we compared the co-prevalence of common pairs of chronic conditions from Medicare CCW data to the frequency of disease-comorbidity pairs in CPGs. Our results show that some disease-comorbidity pairs occur more frequently in CPGs than others. Sixty-one (29.0%) of 210 possible disease-comorbidity pairs occurred zero times; for example, no guideline on chronic kidney disease mentioned depression, while heart failure guidelines mentioned ischemic heart disease the most frequently. Our method adequately identifies comorbid chronic conditions in CPG recommendations with precision 0.82, recall 0.75, and F-measure 0.78. Our work identifies knowledge currently embedded in the free text of clinical practice guideline recommendations and provides an initial view of the extent to which CPGs mention common comorbid conditions. Knowledge extracted from CPG text in this way may be useful to inform gaps in guideline recommendations regarding MCC and therefore identify potential opportunities for guideline improvement.

No MeSH data available.


Related in: MedlinePlus

Annotation pipeline for automated identification of disease labels for 15 chronic conditions. Labels include NCBO preferred labels and synonyms.
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f1-2091334: Annotation pipeline for automated identification of disease labels for 15 chronic conditions. Labels include NCBO preferred labels and synonyms.

Mentions: We first constructed a list of 448 ICD-9 codes for the 15 diseases using the Medicare CCW.24 In addition, we identified three corresponding ICD-9 codes for obesity (Figure 1). Then, we used the NCBO Bioportal Representational State Transfer (REST) services to obtain 1,829 unique preferred labels and synonyms for each ICD-9 code.25 Finally, we developed a text-mining algorithm to identify disease mentions by exactly matching the preferred labels and synonyms in the text corpus. All algorithms were developed using Python 2.7.8.


Automating Identification of Multiple Chronic Conditions in Clinical Practice Guidelines.

Leung TI, Jalal H, Zulman DM, Dumontier M, Owens DK, Musen MA, Goldstein MK - AMIA Jt Summits Transl Sci Proc (2015)

Annotation pipeline for automated identification of disease labels for 15 chronic conditions. Labels include NCBO preferred labels and synonyms.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4525235&req=5

f1-2091334: Annotation pipeline for automated identification of disease labels for 15 chronic conditions. Labels include NCBO preferred labels and synonyms.
Mentions: We first constructed a list of 448 ICD-9 codes for the 15 diseases using the Medicare CCW.24 In addition, we identified three corresponding ICD-9 codes for obesity (Figure 1). Then, we used the NCBO Bioportal Representational State Transfer (REST) services to obtain 1,829 unique preferred labels and synonyms for each ICD-9 code.25 Finally, we developed a text-mining algorithm to identify disease mentions by exactly matching the preferred labels and synonyms in the text corpus. All algorithms were developed using Python 2.7.8.

Bottom Line: Then, we compared the co-prevalence of common pairs of chronic conditions from Medicare CCW data to the frequency of disease-comorbidity pairs in CPGs.Our results show that some disease-comorbidity pairs occur more frequently in CPGs than others.Knowledge extracted from CPG text in this way may be useful to inform gaps in guideline recommendations regarding MCC and therefore identify potential opportunities for guideline improvement.

View Article: PubMed Central - PubMed

Affiliation: Department of Veterans Affairs, VA Palo Alto Health Care System, Palo Alto, CA ; Center for Primary Care and Outcomes Research, Stanford University, Stanford, CA ; Division of General Medical Disciplines, Stanford University, Stanford, CA.

ABSTRACT
Many clinical practice guidelines (CPGs) are intended to provide evidence-based guidance to clinicians on a single disease, and are frequently considered inadequate when caring for patients with multiple chronic conditions (MCC), or two or more chronic conditions. It is unclear to what degree disease-specific CPGs provide guidance about MCC. In this study, we develop a method for extracting knowledge from single-disease chronic condition CPGs to determine how frequently they mention commonly co-occurring chronic diseases. We focus on 15 highly prevalent chronic conditions. We use publicly available resources, including a repository of guideline summaries from the National Guideline Clearinghouse to build a text corpus, a data dictionary of ICD-9 codes from the Medicare Chronic Conditions Data Warehouse (CCW) to construct an initial list of disease terms, and disease synonyms from the National Center for Biomedical Ontology to enhance the list of disease terms. First, for each disease guideline, we determined the frequency of comorbid condition mentions (a disease-comorbidity pair) by exactly matching disease synonyms in the text corpus. Then, we developed an annotated reference standard using a sample subset of guidelines. We used this reference standard to evaluate our approach. Then, we compared the co-prevalence of common pairs of chronic conditions from Medicare CCW data to the frequency of disease-comorbidity pairs in CPGs. Our results show that some disease-comorbidity pairs occur more frequently in CPGs than others. Sixty-one (29.0%) of 210 possible disease-comorbidity pairs occurred zero times; for example, no guideline on chronic kidney disease mentioned depression, while heart failure guidelines mentioned ischemic heart disease the most frequently. Our method adequately identifies comorbid chronic conditions in CPG recommendations with precision 0.82, recall 0.75, and F-measure 0.78. Our work identifies knowledge currently embedded in the free text of clinical practice guideline recommendations and provides an initial view of the extent to which CPGs mention common comorbid conditions. Knowledge extracted from CPG text in this way may be useful to inform gaps in guideline recommendations regarding MCC and therefore identify potential opportunities for guideline improvement.

No MeSH data available.


Related in: MedlinePlus