Paper Info
Title
Prediction of venous thromboembolism using semantic and sentiment analyses of clinical narratives.
Abstract
Venous thromboembolism (VTE) is the third most common cardiovascular disorder. It affects people of both genders at ages as young as 20 years. The increased number of VTE cases with a high fatality rate of 25% at first occurrence makes preventive measures essential. Clinical narratives are a rich source of knowledge and should be included in the diagnosis and treatment processes, as they may contain critical information on risk factors. It is very important to make such narrative blocks of information usable for searching, health analytics, and decision-making. This paper proposes a Semantic Extraction and Sentiment Assessment of Risk Factors (SESARF) framework. Unlike traditional machine-learning approaches, SESARF, which consists of two main algorithms, namely, ExtractRiskFactor and FindSeverity, prepares a feature vector as the input to a support vector machine (SVM) classifier to make a diagnosis. SESARF matches and maps the concepts of VTE risk factors and finds adjectives and adverbs that reflect their levels of severity. SESARF uses a semantic- and sentiment-based approach to analyze clinical narratives of electronic health records (EHR) and then predict a diagnosis of VTE.
Year
DOI
Venue
2018
10.1016/j.compbiomed.2017.12.026
Computers in Biology and Medicine
Keywords
Field
DocType
Venous thromboembolism,Risk factor assessment,Natural language processing,Semantic enrichment,Sentiment analysis,Prediction through classification,Support vector machine
Feature vector,Pattern recognition,Computer science,Sentiment analysis,Precision and recall,Support vector machine,Narrative,Case fatality rate,Artificial intelligence,Natural language processing,Classifier (linguistics),Analytics
Journal
Volume
ISSN
Citations 
94
0010-4825
3
PageRank 
References 
Authors
0.38
11
3
Name
Order
Citations
PageRank
Susan Sabra130.38
Khalid Mahmood Malik2269.32
Mazen Alobaidi3203.96