A Network-Based Approach on Big Data for the Comorbidities of Urticaria
This study investigates the network properties of urticaria comorbidity. Comorbidities are the presence of one or more additional disorders or diseases that co-occur with a primary disease or disorder. The purpose of this study is to identify diseases tha
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Department of Healthcare Management, Oriental Institute of Technology, New Taipei City 22061, Taiwan [email protected] 2 Medical Affairs Office, West Garden Hospital, Taipei City 10864, Taiwan [email protected] School of Nursing, National Taipei University of Nursing and Health Sciences, Taipei City 11219, Taiwan [email protected]
Abstract. This study investigates the network properties of urticaria comor‐ bidity. Comorbidities are the presence of one or more additional disorders or diseases that co-occur with a primary disease or disorder. The purpose of this study is to identify diseases that co-occur with urticaria. Research data was collected from 1,154,534 urticaria outpatient department medical records out of 163,141,270 outpatient department medical records from 1997 to 2010 in Taiwan. Through the phenotypic disease network (PDN), this study has identified the diseases that are associated with urticaria. It has been discovered that the PDN has a complex structure where some diseases are highly connected while others are barely connected at all. While not conclusive, these findings can explain that the more connected the diseases are, the higher the mortality rate is, as patients developing highly connected diseases are more likely to be diagnosed at an advanced stage of the disease, which can be reached through multiple paths in the PDN. Keywords: Medical records · Big data · Urticaria · Comorbidities · The human phenotypic disease network (PDN)
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Introduction
The medical record is a systematic file that provides a chronicle of a patient’s medical history and care. Physicians, nurses and other members of the health care team may make entries in the medical record. Hospitals, over the years, have generated large amounts of data, driven by record keeping, compliance and regulatory requirements, and patient care. To meet the mandatory requirements and the goal to improve the quality of healthcare service, these massive quantities of big data hold the promise of supporting a wide range of medical and healthcare functions, including clinical decision support, disease monitor, and public health management.
© Springer Nature Singapore Pte Ltd. 2017 K. Kim and N. Joukov (eds.), Information Science and Applications 2017, Lecture Notes in Electrical Engineering 424, DOI 10.1007/978-981-10-4154-9_58
A Network-Based Approach on Big Data
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For many diseases, there are no definite boundaries, as diseases can have multiple causes and can be related in different dimensions. From a genetic point of view, a pair of diseases can be related because they have both been associated with the same gene, whereas from a proteomic perspective, diseases can be related because disease- associ‐ ated proteins act on the same pathway [1]. Over the past half-decade, several resources have been constructed to help under‐ stand the entangled origins of many diseases. Many of these resources have been presented as networks in which interactions among disease-associated genes, proteins, and expression patterns have been summari
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