Abstract
The rapid growth of medical recording data has increased the demand for automated analysis. An important problem in recent medical research is automated medical diagnosis, which is to infer likely diseases for the observed symptoms. Existing approaches typically perform the inference on a sparse bipartite graph with two sets of nodes representing diseases and symptoms, respectively. By using this graph, existing methods basically assume no direct dependency exists between diseases (or symptoms), which may not be true in practice. To address this limitation, we propose to integrate two domain networks encoding similarities between diseases and those between symptoms to avoid information loss as well as to alleviate the sparsity problem of the bipartite graph. Another limitation of the existing methods is that they usually output a ranked list of diseases mixed from very different etiologies which greatly limits their practical usefulness. An ideal method should allow a clustered structure in the disease ranking list so that both similar and different diseases can be easily identified. Therefore, we formulate automated diagnosis as a novel cross-domain cluster ranking problem, which identifies and ranks the disease clusters simultaneously in the symptom-disease network. Our formulation employs a joint learning scheme in which the dual procedures of cluster finding and cluster ranking are coupled and mutually reinforced. Experimental results on real-world datasets demonstrate the effectiveness of our method.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 17th IEEE International Conference on Data Mining, ICDM 2017 |
| Editors | George Karypis, Srinivas Alu, Vijay Raghavan, Xindong Wu, Lucio Miele |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1009-1014 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538638347 |
| DOIs | |
| State | Published - Dec 15 2017 |
| Event | 17th IEEE International Conference on Data Mining, ICDM 2017 - New Orleans, United States Duration: Nov 18 2017 → Nov 21 2017 |
Publication series
| Name | Proceedings - IEEE International Conference on Data Mining, ICDM |
|---|---|
| Volume | 2017-November |
| ISSN (Print) | 1550-4786 |
Other
| Other | 17th IEEE International Conference on Data Mining, ICDM 2017 |
|---|---|
| Country | United States |
| City | New Orleans |
| Period | 11/18/17 → 11/21/17 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
Cite this
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Automated medical diagnosis by ranking clusters across the symptom-disease network. / Ni, Jingchao; Fei, Hongliang; Fan, Wei; Zhang, Xiang.
Proceedings - 17th IEEE International Conference on Data Mining, ICDM 2017. ed. / George Karypis; Srinivas Alu; Vijay Raghavan; Xindong Wu; Lucio Miele. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1009-1014 (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2017-November).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Automated medical diagnosis by ranking clusters across the symptom-disease network
AU - Ni, Jingchao
AU - Fei, Hongliang
AU - Fan, Wei
AU - Zhang, Xiang
PY - 2017/12/15
Y1 - 2017/12/15
N2 - The rapid growth of medical recording data has increased the demand for automated analysis. An important problem in recent medical research is automated medical diagnosis, which is to infer likely diseases for the observed symptoms. Existing approaches typically perform the inference on a sparse bipartite graph with two sets of nodes representing diseases and symptoms, respectively. By using this graph, existing methods basically assume no direct dependency exists between diseases (or symptoms), which may not be true in practice. To address this limitation, we propose to integrate two domain networks encoding similarities between diseases and those between symptoms to avoid information loss as well as to alleviate the sparsity problem of the bipartite graph. Another limitation of the existing methods is that they usually output a ranked list of diseases mixed from very different etiologies which greatly limits their practical usefulness. An ideal method should allow a clustered structure in the disease ranking list so that both similar and different diseases can be easily identified. Therefore, we formulate automated diagnosis as a novel cross-domain cluster ranking problem, which identifies and ranks the disease clusters simultaneously in the symptom-disease network. Our formulation employs a joint learning scheme in which the dual procedures of cluster finding and cluster ranking are coupled and mutually reinforced. Experimental results on real-world datasets demonstrate the effectiveness of our method.
AB - The rapid growth of medical recording data has increased the demand for automated analysis. An important problem in recent medical research is automated medical diagnosis, which is to infer likely diseases for the observed symptoms. Existing approaches typically perform the inference on a sparse bipartite graph with two sets of nodes representing diseases and symptoms, respectively. By using this graph, existing methods basically assume no direct dependency exists between diseases (or symptoms), which may not be true in practice. To address this limitation, we propose to integrate two domain networks encoding similarities between diseases and those between symptoms to avoid information loss as well as to alleviate the sparsity problem of the bipartite graph. Another limitation of the existing methods is that they usually output a ranked list of diseases mixed from very different etiologies which greatly limits their practical usefulness. An ideal method should allow a clustered structure in the disease ranking list so that both similar and different diseases can be easily identified. Therefore, we formulate automated diagnosis as a novel cross-domain cluster ranking problem, which identifies and ranks the disease clusters simultaneously in the symptom-disease network. Our formulation employs a joint learning scheme in which the dual procedures of cluster finding and cluster ranking are coupled and mutually reinforced. Experimental results on real-world datasets demonstrate the effectiveness of our method.
UR - http://www.scopus.com/inward/record.url?scp=85043974492&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85043974492&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2017.130
DO - 10.1109/ICDM.2017.130
M3 - Conference contribution
AN - SCOPUS:85043974492
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 1009
EP - 1014
BT - Proceedings - 17th IEEE International Conference on Data Mining, ICDM 2017
A2 - Karypis, George
A2 - Alu, Srinivas
A2 - Raghavan, Vijay
A2 - Wu, Xindong
A2 - Miele, Lucio
PB - Institute of Electrical and Electronics Engineers Inc.
ER -