Proposal of a Big Data Platform for Intelligent Antibiotic Surveillance in a Hospital

被引:1
作者
Morales, Antonio [1 ]
Canovas-Segura, Bernardo [1 ]
Campos, Manuel [1 ]
Juarez, Jose M. [1 ]
Palacios, Francisco [2 ]
机构
[1] Univ Murcia, Fac Comp Sci, Murcia, Spain
[2] Univ Hosp Getafe, Madrid, Spain
来源
ADVANCES IN ARTIFICIAL INTELLIGENCE, CAEPIA 2016 | 2016年 / 9868卷
关键词
Clinical decision support systems; Antibiotic surveillance; Big data; Knowledge representation and reasoning; Business intelligence; CRISIS;
D O I
10.1007/978-3-319-44636-3_24
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
From a technological point of view two kinds of requirements must be taken into account when implementing Clinical Decision Support Systems (CDSSs) for antibiotic surveillance in a hospital. First, Artificial Intelligence (AI) technologies are usually applied to represent and reason about existing clinical knowledge, but also to discover new one from raw data. Second, at a global decision level, representative applications of Business Intelligence (BI) must be also considered. The present work introduces the design and implementation of a CDSS platform that integrates both AI and BI technologies to assist clinicians in the rational use of antibiotics in a hospital. The choice of a Hadoop based Big Data architecture provides a suitable solution for the problem of integrating, processing and analysing large sets of clinical data. The platform facilitates the daily follow-up of antibiotic therapies and infections while offering various decision support modules at both patient and global level. The system is being tested and evaluated in a university hospital.
引用
收藏
页码:261 / 270
页数:10
相关论文
共 16 条
[1]  
[Anonymous], 2013, HOSP PHARM
[2]   Development of a clinical decision support system for antibiotic management in a hospital environment [J].
Cánovas-Segura B. ;
Campos M. ;
Morales A. ;
Juarez J.M. ;
Palacios F. .
Progress in Artificial Intelligence, 2016, 5 (03) :181-197
[3]   Use of Electronic Health Records and Clinical Decision Support Systems for Antimicrobial Stewardship [J].
Forrest, Graeme N. ;
Van Schooneveld, Trevor C. ;
Kullar, Ravina ;
Schulz, Lucas T. ;
Phu Duong ;
Postelnick, Michael .
CLINICAL INFECTIOUS DISEASES, 2014, 59 :S122-S133
[4]  
Garcia-caballero H., 2015, ACT 16 C AS ESP INT, P71
[5]  
Grover M., 2015, Hadoop application architectures
[6]  
Groves Peter., 2016, The Big Data Revolution in Healthcare: Accelerating Value and Innovation
[7]   Toward a Big Data Healthcare Analytics System: a Mathematical Modeling Perspective [J].
Khazaei, Hamzeh ;
McGregor, Carolyn ;
Eklund, Mikael ;
El-Khatib, Khalil ;
Thommandram, Anirudh .
2014 IEEE WORLD CONGRESS ON SERVICES (SERVICES), 2014, :208-215
[8]   Electronic Surveillance of Healthcare-Associated Infections with MONI-ICU-A Clinical Breakthrough Compared to Conventional Surveillance Systems [J].
Koller, Walter ;
Blacky, Alexander ;
Bauer, Claudia ;
Mandl, Harald ;
Adlassnig, Klaus-Peter .
MEDINFO 2010, PTS I AND II, 2010, 160 :432-436
[9]   Artificial intelligence techniques for monitoring dangerous infections [J].
Lamma, E ;
Mello, P ;
Nanetti, A ;
Riguzzi, F ;
Storari, S ;
Valastro, G .
IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, 2006, 10 (01) :143-155
[10]   EUCAST expert rules in antimicrobial susceptibility testing [J].
Leclercq, R. ;
Canton, R. ;
Brown, D. F. J. ;
Giske, C. G. ;
Heisig, P. ;
MacGowan, A. P. ;
Mouton, J. W. ;
Nordmann, P. ;
Rodloff, A. C. ;
Rossolini, G. M. ;
Soussy, C. -J. ;
Steinbakk, M. ;
Winstanley, T. G. ;
Kahlmeter, G. .
CLINICAL MICROBIOLOGY AND INFECTION, 2013, 19 (02) :141-160