Fusion of clinical data: A case study to predict the type of treatment of bone fractures - Haq, Anam; Wilk, Szymon; Abelló, Alberto (Zielona Góra: Uniwersytet Zielonogórski)
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.
Keywords: clinical data; data fusion; combination of data; combination of interpretation; Prediction models; decision support