Heterogeneous Network-Based Chronic Disease Progression Mining
Healthcare insurance fraud has caused billions of dollars in losses in public healthcare funds around the world. In particular, healthcare insurance fraud in chronic diseases is especially rampant. Understanding disease progression can help investigators detect healthcare insurance frauds early on....
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2019-03-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2018.9020009 |
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| author | Chenfei Sun Qingzhong Li Lizhen Cui Hui Li Yuliang Shi |
| author_facet | Chenfei Sun Qingzhong Li Lizhen Cui Hui Li Yuliang Shi |
| author_sort | Chenfei Sun |
| collection | DOAJ |
| description | Healthcare insurance fraud has caused billions of dollars in losses in public healthcare funds around the world. In particular, healthcare insurance fraud in chronic diseases is especially rampant. Understanding disease progression can help investigators detect healthcare insurance frauds early on. Existing disease progression methods often ignore complex relations, such as the time-gap and pattern of disease occurrence. They also do not take into account the different medication stages of the same chronic disease, which is of great help when conducting healthcare insurance fraud detection and reducing healthcare costs. In this paper, we propose a heterogeneous network-based chronic disease progression mining method to improve the current understanding on the progression of chronic diseases, including orphan diseases. The method also considers the different medication stages of the same chronic disease. Extensive experiments show that our method can outperform the existing methods by 20% in terms of F-measure. |
| format | Article |
| id | doaj-art-317745379f634a3fb8aaee1326a8e305 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2019-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-317745379f634a3fb8aaee1326a8e3052025-02-02T05:59:19ZengTsinghua University PressBig Data Mining and Analytics2096-06542019-03-0121253410.26599/BDMA.2018.9020009Heterogeneous Network-Based Chronic Disease Progression MiningChenfei Sun0Qingzhong Li1Lizhen Cui2Hui Li3Yuliang Shi4<institution content-type="dept">Research Center of Software and Data Engineering</institution>, <institution>Shandong University</institution>, <city>Jinan</city> <postal-code>250101</postal-code>, <country>China</country>.<institution content-type="dept">Research Center of Software and Data Engineering</institution>, <institution>Shandong University</institution>, <city>Jinan</city> <postal-code>250101</postal-code>, <country>China</country>.<institution content-type="dept">Research Center of Software and Data Engineering</institution>, <institution>Shandong University</institution>, <city>Jinan</city> <postal-code>250101</postal-code>, <country>China</country>.<institution content-type="dept">Research Center of Software and Data Engineering</institution>, <institution>Shandong University</institution>, <city>Jinan</city> <postal-code>250101</postal-code>, <country>China</country>.<institution content-type="dept">Research Center of Software and Data Engineering</institution>, <institution>Shandong University</institution>, <city>Jinan</city> <postal-code>250101</postal-code>, <country>China</country>.Healthcare insurance fraud has caused billions of dollars in losses in public healthcare funds around the world. In particular, healthcare insurance fraud in chronic diseases is especially rampant. Understanding disease progression can help investigators detect healthcare insurance frauds early on. Existing disease progression methods often ignore complex relations, such as the time-gap and pattern of disease occurrence. They also do not take into account the different medication stages of the same chronic disease, which is of great help when conducting healthcare insurance fraud detection and reducing healthcare costs. In this paper, we propose a heterogeneous network-based chronic disease progression mining method to improve the current understanding on the progression of chronic diseases, including orphan diseases. The method also considers the different medication stages of the same chronic disease. Extensive experiments show that our method can outperform the existing methods by 20% in terms of F-measure.https://www.sciopen.com/article/10.26599/BDMA.2018.9020009disease progressionheterogeneous networkhealthcare insurance fraud |
| spellingShingle | Chenfei Sun Qingzhong Li Lizhen Cui Hui Li Yuliang Shi Heterogeneous Network-Based Chronic Disease Progression Mining Big Data Mining and Analytics disease progression heterogeneous network healthcare insurance fraud |
| title | Heterogeneous Network-Based Chronic Disease Progression Mining |
| title_full | Heterogeneous Network-Based Chronic Disease Progression Mining |
| title_fullStr | Heterogeneous Network-Based Chronic Disease Progression Mining |
| title_full_unstemmed | Heterogeneous Network-Based Chronic Disease Progression Mining |
| title_short | Heterogeneous Network-Based Chronic Disease Progression Mining |
| title_sort | heterogeneous network based chronic disease progression mining |
| topic | disease progression heterogeneous network healthcare insurance fraud |
| url | https://www.sciopen.com/article/10.26599/BDMA.2018.9020009 |
| work_keys_str_mv | AT chenfeisun heterogeneousnetworkbasedchronicdiseaseprogressionmining AT qingzhongli heterogeneousnetworkbasedchronicdiseaseprogressionmining AT lizhencui heterogeneousnetworkbasedchronicdiseaseprogressionmining AT huili heterogeneousnetworkbasedchronicdiseaseprogressionmining AT yuliangshi heterogeneousnetworkbasedchronicdiseaseprogressionmining |