Name
Title | ||
|---|---|---|
IDDAT: An ontology-driven decision support system for infectious disease diagnosis and therapy |
Abstract | ||
|---|---|---|
Decision Support Systems (DSS) has become increasingly important due to its broad applications in various domains. Significant progresses have been made on ensuring more precise decision-making by leveraging appropriate data and knowledge from knowledge bases. However, the current DSSs related to antibiotics consider only therapy rather than diagnosis, and they were developed from a physician's perspective. Based on these two points, this study presents IDDAT, an ontology-driven decision support system for aiding Infectious Disease Diagnosis and Antibiotic Therapy. Based on patient-entered information, this freely accessible system aims to identify infectious disease, and provide an antibiotic therapy specifically adapted to the patient. We show the effectiveness of IDDAT by applying it to a diagnosis classification task. Experimental results reveal the system's advantages in term of the area under the curve (AUC) of receiver operating characteristic (ROC) (89.91%). © 2018 IEEE. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICDMW.2018.00201 | IEEE International Conference on Data Mining Workshops, ICDMW |
Keywords | Field | DocType |
Decision Support System,diagnosis,infectious disease,ontology,therapy | Ontology (information science),Ontology,Receiver operating characteristic,Computer science,Decision support system,Diagnosis Classification,Artificial intelligence,Infectious disease (medical specialty),Machine learning,Infectious disease diagnosis | Conference |
Volume | Citations | PageRank |
2018-November | 0 | 0.34 |
References | Authors | |
0 | 8 | |