Clinical Decision Support Systems in Practice: Current Status and Challenges

被引:0
作者
Jovic, A. [1 ]
Stancin, I [1 ]
Friganovic, K. [1 ]
Cifrek, M. [1 ]
机构
[1] Univ Zagreb, Fac Elect Engn & Comp, Unska 3, Zagreb 10000, Croatia
来源
2020 43RD INTERNATIONAL CONVENTION ON INFORMATION, COMMUNICATION AND ELECTRONIC TECHNOLOGY (MIPRO 2020) | 2020年
关键词
decision support system; clinical decision support system; healthcare; artificial intelligence; deep learning; ARTIFICIAL-INTELLIGENCE; CLASSIFICATION;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Decision support systems (DSS) are computer programs based on artificial intelligence methods that contribute to reaching a correct decision in an often-narrow domain of interest. Clinical decision support systems (CDSS) are such DSSs that may be used by medical professionals in clinics and hospitals. They are used for diagnosis, treatment protocol recommendations, treatment outcome predictions and other tasks. CDSS are constructed based on symbolic and machine learning (including deep learning) approaches to represent and infer medical knowledge. The aim of this work is to provide an overview of past and current methods in designing a successful CDSS. The study considers the systems that were claimed to be implemented in clinical practice. Currently, the development of a CDSS is mostly pursued in two directions: 1) a more traditional approach based on rules, ontologies, probabilistic models, and the use of standards; 2) machine learning based approach. Both approaches may be used complementary within a healthcare information system. This work seeks to provide an objective view on the advantages and limitations of the approaches as well to discuss future research avenues that could lead to more accurate and trustworthy CDSS and improved healthcare.
引用
收藏
页码:355 / 360
页数:6
相关论文
共 32 条
[1]  
[Anonymous], 2018, BRIEF BIOINFORM, DOI DOI 10.1093/bib/bbx044
[2]  
[Anonymous], 2018, EUROPEAN UNION GEN D
[3]  
Baader F., V5689, P1
[4]  
Castaneda Christian, 2015, J Clin Bioinforma, V5, P4, DOI 10.1186/s13336-015-0019-3
[5]   Using recurrent neural network models for early detection of heart failure onset [J].
Choi, Edward ;
Schuetz, Andy ;
Stewart, Walter F. ;
Sun, Jimeng .
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2017, 24 (02) :361-370
[6]  
Dwyer DB, 2018, ANNU REV CLIN PSYCHO, V14, P91, DOI [10.1146/annurev-clinpsy-032816045037, 10.1146/annurev-clinpsy-032816-045037]
[7]   Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study [J].
Faes, Livia ;
Wagner, Siegfried K. ;
Fu, Dun Jack ;
Liu, Xiaoxuan ;
Korot, Edward ;
Ledsam, Joseph R. ;
Back, Trevor ;
Chopra, Reena ;
Pontikos, Nikolas ;
Kern, Christoph ;
Moraes, Gabriella ;
Schmid, Martin K. ;
Sim, Dawn ;
Balaskas, Konstantinos ;
Bachmann, Lucas M. ;
Denniston, Alastair K. ;
Keane, Pearse A. .
LANCET DIGITAL HEALTH, 2019, 1 (05) :E232-E242
[8]   Combining expert knowledge and knowledge automatically acquired from electronic data sources for continued ontology evaluation and improvement [J].
Gordon, Claire L. ;
Weng, Chunhua .
JOURNAL OF BIOMEDICAL INFORMATICS, 2015, 57 :42-52
[9]   Artificial intelligence in medicine [J].
Hamet, Pavel ;
Tremblay, Johanne .
METABOLISM-CLINICAL AND EXPERIMENTAL, 2017, 69 :S36-S40
[10]   Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network [J].
Hannun, Awni Y. ;
Rajpurkar, Pranav ;
Haghpanahi, Masoumeh ;
Tison, Geoffrey H. ;
Bourn, Codie ;
Turakhia, Mintu P. ;
Ng, Andrew Y. .
NATURE MEDICINE, 2019, 25 (01) :65-+