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Mining cancer-specific disease comorbidities from a large observational health database.

Chen Y, Xu R - Cancer Inform (2014)

Bottom Line: We stratified 3,354,043 patients based on age and gender, and developed a network-based approach to extract comorbidity patterns from each patient group.We applied our comorbidity mining approach on colorectal cancer and detected its comorbid associations with metabolic syndrome components, diabetes, and osteoporosis.Our results not only confirmed known cancer comorbidities but also generated novel hypotheses, which can illuminate the common pathophysiology between cancers and their co-occurring diseases.

View Article: PubMed Central - PubMed

Affiliation: Division of Medical Informatics, Case Western Reserve University, Cleveland, OH, USA.

ABSTRACT
Cancer comorbidities often reflect the complex pathogenesis of cancers and provide valuable clues to discover the underlying genetic mechanisms of cancers. In this study, we systematically mine and analyze cancer-specific comorbidity from the FDA Adverse Event Reporting System. We stratified 3,354,043 patients based on age and gender, and developed a network-based approach to extract comorbidity patterns from each patient group. We compared the comorbidity patterns among different patient groups and investigated the effect of age and gender on cancer comorbidity patterns. The results demonstrated that the comorbidity relationships between cancers and non-cancer diseases largely depend on age and gender. A few exceptions are depression, anxiety, and metabolic syndrome, whose comorbidity relationships with cancers are relatively stable among all patients. Literature evidences demonstrate that these stable cancer comorbidities reflect the pathogenesis of cancers. We applied our comorbidity mining approach on colorectal cancer and detected its comorbid associations with metabolic syndrome components, diabetes, and osteoporosis. Our results not only confirmed known cancer comorbidities but also generated novel hypotheses, which can illuminate the common pathophysiology between cancers and their co-occurring diseases.

No MeSH data available.


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Extract cancer comorbidities for each stratified patient group.
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f2-cin-suppl.1-2014-037: Extract cancer comorbidities for each stratified patient group.

Mentions: Using the patient–disease data in each stratified group, we mined cancer comorbidities by the following three steps (Fig. 2). First, we applied an association rule mining algorithm on patient–disease pairs, and mined strong co-occurrence patterns among all possible disease combinations. Then, we constructed a comorbidity network using the resulting patterns. Finally, to extract comorbidities for cancers, we initiated a random walk on the network from a set of interested cancer nodes, and ranked the non-cancer diseases with the probabilities of being reached by the random walk. After repeating the three steps for each patient group, we traced the changes of cancer comorbidities across different age or gender groups. The following subsections describe each step in detail.


Mining cancer-specific disease comorbidities from a large observational health database.

Chen Y, Xu R - Cancer Inform (2014)

Extract cancer comorbidities for each stratified patient group.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4216041&req=5

f2-cin-suppl.1-2014-037: Extract cancer comorbidities for each stratified patient group.
Mentions: Using the patient–disease data in each stratified group, we mined cancer comorbidities by the following three steps (Fig. 2). First, we applied an association rule mining algorithm on patient–disease pairs, and mined strong co-occurrence patterns among all possible disease combinations. Then, we constructed a comorbidity network using the resulting patterns. Finally, to extract comorbidities for cancers, we initiated a random walk on the network from a set of interested cancer nodes, and ranked the non-cancer diseases with the probabilities of being reached by the random walk. After repeating the three steps for each patient group, we traced the changes of cancer comorbidities across different age or gender groups. The following subsections describe each step in detail.

Bottom Line: We stratified 3,354,043 patients based on age and gender, and developed a network-based approach to extract comorbidity patterns from each patient group.We applied our comorbidity mining approach on colorectal cancer and detected its comorbid associations with metabolic syndrome components, diabetes, and osteoporosis.Our results not only confirmed known cancer comorbidities but also generated novel hypotheses, which can illuminate the common pathophysiology between cancers and their co-occurring diseases.

View Article: PubMed Central - PubMed

Affiliation: Division of Medical Informatics, Case Western Reserve University, Cleveland, OH, USA.

ABSTRACT
Cancer comorbidities often reflect the complex pathogenesis of cancers and provide valuable clues to discover the underlying genetic mechanisms of cancers. In this study, we systematically mine and analyze cancer-specific comorbidity from the FDA Adverse Event Reporting System. We stratified 3,354,043 patients based on age and gender, and developed a network-based approach to extract comorbidity patterns from each patient group. We compared the comorbidity patterns among different patient groups and investigated the effect of age and gender on cancer comorbidity patterns. The results demonstrated that the comorbidity relationships between cancers and non-cancer diseases largely depend on age and gender. A few exceptions are depression, anxiety, and metabolic syndrome, whose comorbidity relationships with cancers are relatively stable among all patients. Literature evidences demonstrate that these stable cancer comorbidities reflect the pathogenesis of cancers. We applied our comorbidity mining approach on colorectal cancer and detected its comorbid associations with metabolic syndrome components, diabetes, and osteoporosis. Our results not only confirmed known cancer comorbidities but also generated novel hypotheses, which can illuminate the common pathophysiology between cancers and their co-occurring diseases.

No MeSH data available.


Related in: MedlinePlus