<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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 limitation may 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.</p></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 limitation may 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.

Fusion of clinical data: A case study to predict the type of treatment of bone fractures

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A prominent characteristic of clinical data is their heterogeneity—such data include structured examination records and laboratory results, unstructured...

Fusion of clinical data: A case study to predict the type of treatment of bone fractures

Published Online: Mar 29, 2019

Page range: 51 - 67

Received: Mar 19, 2018

Accepted: Dec 06, 2018

DOI: https://doi.org/10.2478/amcs-2019-0004

Keywords
clinical data, data fusion, combination of data, combination of interpretation, prediction models, decision support

© 2019 Anam Haq et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Fusion of clinical data: A case study to predict the type of treatment of bone fractures

Published Online: Mar 29, 2019

Page range: 51 - 67

Received: Mar 19, 2018

Accepted: Dec 06, 2018

DOI: https://doi.org/10.2478/amcs-2019-0004

Keywordsclinical data, data fusion, combination of data, combination of interpretation, prediction models, decision support

© 2019 Anam Haq et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
clinical data, data fusion, combination of data, combination of interpretation, prediction models, decision support