A data mining and survey study on diseases associated with paraesophageal hernia.

Jianji Yang, Judith (Judy) Logan

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Paraesophageal hernia is a severe form of hiatal hernia, characterized by the upward dislocation of the gastric fundus into the thoracic cavity. In this study, the 1999 National Inpatient Sample dataset of the Healthcare Cost and Utilization Project was analyzed using data mining techniques to explore disorders associated with paraesophageal hernia. The result of this data mining process was compared with a subsequent expert knowledge survey of 97 gastrointestinal tract surgeons. This two-step analysis showed that the results of data mining and expert knowledge are consistent in some factors that are highly associated with paraesophageal hernia: older age, other gastrointestinal tract disorders and obesity, for example. But the data mining approach revealed some other related disorders that were not known to the experts or reported in the literature, for example, hypertension, peritoneal adhesions and gall bladder/bile duct diseases. These findings lay a framework for subsequent hypothesis-driven research.

Original languageEnglish (US)
Pages (from-to)829-833
Number of pages5
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
StatePublished - 2006

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Hiatal Hernia
Data Mining
Gastrointestinal Tract
Bile Duct Diseases
Gastric Fundus
Thoracic Cavity
Health Care Costs
Inpatients
Urinary Bladder
Obesity
Hypertension
Surveys and Questionnaires
Research

ASJC Scopus subject areas

  • Medicine(all)

Cite this

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abstract = "Paraesophageal hernia is a severe form of hiatal hernia, characterized by the upward dislocation of the gastric fundus into the thoracic cavity. In this study, the 1999 National Inpatient Sample dataset of the Healthcare Cost and Utilization Project was analyzed using data mining techniques to explore disorders associated with paraesophageal hernia. The result of this data mining process was compared with a subsequent expert knowledge survey of 97 gastrointestinal tract surgeons. This two-step analysis showed that the results of data mining and expert knowledge are consistent in some factors that are highly associated with paraesophageal hernia: older age, other gastrointestinal tract disorders and obesity, for example. But the data mining approach revealed some other related disorders that were not known to the experts or reported in the literature, for example, hypertension, peritoneal adhesions and gall bladder/bile duct diseases. These findings lay a framework for subsequent hypothesis-driven research.",
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