Paper Info
Title
A Semantic Extraction and Sentimental Assessment of Risk Factors (SESARF): An NLP Approach for Precision Medicine: A Medical Decision Support Tool for Early Diagnosis from Clinical Notes
Abstract
Clinical notes contain information that is crucial for the diagnosis process. However, it is usually not properly manually analyzed due to the tremendous efforts and time it takes. Hence, an automated approach is eagerly needed to maximize clinical knowledge management and reduce cost. In this paper, we propose a framework SESARF: a Semantic Extractor to identify hidden risk factors in clinical notes and a Sentimental Analyzer to assess the severity levels associated with the identified Risk Factors. This tool can be customized to any disease using Linked Open Data (LOD) by selecting a specific disease and collecting its risk factors list from medical ontologies. The extracted knowledge can serve two purposes: 1) a feature vector is prepared, for any classifier in machine learning, containing risk factors and their weights based on our semantic enrichment and sentimental analyzer and 2) a proper comparison of the extracted information with wearable body sensors that can alert any major changes in a patient's health status to personalize treatment.
Year
DOI
Venue
2017
10.1109/COMPSAC.2017.34
2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC)
Keywords
Field
DocType
semantic enrichment,sentimental analysis,automated early diagnosis,linked open data,risk assessment,medical decision support,precision medicine
Data science,Precision medicine,Systems engineering,Computer science,Linked data,Artificial intelligence,Classifier (linguistics),Ontology (information science),Feature vector,Wearable computer,Decision support system,Machine learning,Semantics
Conference
Volume
ISSN
ISBN
2
0730-3157
978-1-5386-0368-0
Citations 
PageRank 
References 
0
0.34
8
Authors
3
Name
Order
Citations
PageRank
Susan Sabra141.23
Khalid Mahmood Malik283.60
Mazen Alobaidi3203.96