Paper Info
Title
Fusion Of Clinical Data: A Case Study To Predict The Type Of Treatment Of Bone Fractures
Abstract
A prominent characteristic of clinical data is their heterogeneity-such data include structured examination records and laboratory results, unstructured clinical notes, raw and tagged images, and genomic data. This heterogeneity poses a formidable challenge while constructing diagnostic and therapeutic decision models that are currently based on single modalities and are not able to use data in different formats and structures. This limitationmay be addressed using data fusion methods. In this paper, we describe a case study where we aimed at developing data fusion models that resulted in various therapeutic decision models for predicting the type of treatment (surgical vs. non-surgical) for patients with bone fractures. We considered six different approaches to integrate clinical data: one fusion model based on combination of data (COD) and five models based on combination of interpretation (COI). Experimental results showed that the decision model constructed following COI fusion models is more accurate than decision models employing COD. Moreover, statistical analysis using the one-way ANOVA test revealed that there were two groups of constructed decision models, each containing the set of three different models. The results highlighted that the behavior of models within a group can be similar, although it may vary between different groups.
Year
DOI
Venue
2017
10.2478/amcs-2019-0004
INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE
Keywords
Field
DocType
clinical data, data fusion, combination of data, combination of interpretation, prediction models, decision support
Information system,Data mining,Computer science,Decision support system,Sensor fusion,Decision model,Artificial intelligence,Predictive modelling,Machine learning
Conference
Volume
Issue
ISSN
29
1
1641-876X
Citations 
PageRank 
References 
1
0.40
5
Authors
2
Name
Order
Citations
PageRank
Anam Haq110.73
Szymon Wilk246140.94