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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chan, L.W.C. Chan, T. Cheng, L.F. Mak, W.S. |
| Copyright Year | 2010 |
| Description | Author affiliation: Department of Diagnostic Radiology, Princess Margaret Hospital, Hong Kong SAR, China (Cheng, L.F.; Mak, W.S.) || Department of Health Technology and Informatics, Hong Kong Polytechnic University, China (Chan, L.W.C.) || Department of Diagnostic Radiology, University of Hong Kong, China (Chan, T.) |
| Abstract | Identifying historical records of patients who are similar to the new patient could help to retrieve similar reference cases for predicting the clinical outcome of the new patient. Amongst different potential applications, this study illustrates use of patient similarity in predicting survival of patients suffering from hepatocellular carcinoma (HCC) treated with locoregional chemotherapy. This study used 14 similarity measures derived from relevant clinical and imaging parameters to classify the HCC patient pairs into two classes, namely the difference between their survival time being longer or no longer than 12 months. Furthermore, this paper proposes and presents a patient similarity algorithm for the classification, named SimSVM. With the 14 similarity measures as input, SimSVM outputs the predicted class and the degree of similarity or dissimilarity. A dataset was collected from 30 patients, forming 300 and 135 patient pairs as training and test datasets respectively. The trained SimSVM with linear kernel gave the best accuracy (66.7%), sensitivity (64.8%) and specificity (67.9%) on the test dataset. |
| Starting Page | 467 |
| Ending Page | 470 |
| File Size | 385803 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424483037 |
| e-ISBN | 9781424483044 |
| DOI | 10.1109/BIBMW.2010.5703846 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-12-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Training Accuracy Liver Polynomials Patient Similarity Support Vector Machine Kernel Survival Machine Learning Tumors Cancer |
| Content Type | Text |
| Resource Type | Article |
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