Textual extraction and classification for medical risk management: a new Risk Management Platform to manage undesired medical events

被引：0

作者：

Zidi, Salah ^{[1
]}

Julien, Thibaut ^{[2
]}

Mjirda, Anis ^{[3
]}

Maaloul, Fouad ^{[2
]}

机构：

[1] Qassim Univ, CBE, MIS Dept, POB 6633, Qasim 14452, Saudi Arabia

[2] SAS BIOMEDIQA, F-59650 Villeneuve Dascq, France

[3] Univ Valenciennes & Hainaut Cambrsis, LAMIH, Valenciennes, France

来源：

2015 4TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED LOGISTICS AND TRANSPORT (ICALT) | 2015年

关键词：

Risk Management; Undesired events; Medical field; Textual extraction; Classifications; Machine learning; Support Vector Machines;

D O I：

暂无

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

In this paper, we propose a semi-automatic system to deal with the undesired events that may happen in the medical field. Based on algorithms of textual extraction and classifications, it performs a preprocessing of undesired events' declarations. The proposed system is currently running on different hospitals and some medical institutions in France. Computational study were made on a real-case instances and experimentations shows good results for the proposed approaches.

引用

页码：298 / 302

页数：5