Court Judgment Decision Support System Based on Medical Text Mining

Authors

Qing Zhu, International Business School of Shaanxi Normal University, Xi'an, 710000, China; Institute of Cross-Process Perception and Control, Shaanxi Normal University, Xi'an,710000, ChinaFollow
Kezhen Wei, International Business School of Shaanxi Normal University, Xi'an, 710000, China; Institute of Cross-Process Perception and Control, Shaanxi Normal University, Xi'an,710000, China
Lanlin Ding, Institute of Cross-Process Perception and Control, Shaanxi Normal University, Xi'an,710000, China;School of Economics and Finance, Xi'an Jiao Tong University, Xi'an,710000, China
Kin Keung Kin Keung Lai, Institute of Cross-Process Perception and Control, Shaanxi Normal University, Xi'an,710000, China; Department of Management Sciences, City University of Hong Kong, HK, China

Abstract

Medical damage is a common problem faced by hospitals around the world and is widely watched by countries and the World Health Organization. As the number of medical damage dispute lawsuit cases rapidly grows, many countries in the world face the problem how to improve the efficiency of the judicial system under the premise of guaranteeing the quality of the trial. Therefore, in addition to reforming the system, the decision support system will effectively improve judicial decisions. This paper takes medical damage judgment documents in China as example, and proposes a court judgment decision support system (CJ-DSS) based on medical text mining and the automatic classification technology. The system can predict the trail results of the new lawsuit documents according to the previous cases verdict - rejected and non-rejected. Combined with the cases, the study in this paper found that combined feature extraction method does improve the performance of three kinds of classifiers - Support Value Machine (SVM), Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN), the degree of improved performance is different from using DF-CHI combined feature extraction method. In addition, integrated learning algorithm also improves the classification performance of the overall system.

Recommended Citation

Zhu, Qing; Wei, Kezhen; Ding, Lanlin; and Kin Keung Lai, Kin Keung, "Court Judgment Decision Support System Based on Medical Text Mining" (2017). WHICEB 2017 Proceedings. 2.
https://aisel.aisnet.org/whiceb2017/2