Abstract
In this paper we present a system for automatic generation of summaries of patients’ unstructured medical reports. The system employs Natural Language Processing techniques in order to determine the most interesting points and uses the MetaMap module for recognizing the medical concepts in a medical report. Afterwards the sentences that do not contain interesting concepts are removed and a summary is generated which contains URL links to the Linked Life Data pages of the identified medical concepts, enabling both medical doctors and patients to further explore what is reported in. Such integration also allows the tool to interface with other semantic web-based applications. The performance of the tool were also evaluated, achieving remarkable results in sentence identification, polarity detection and concept recognition. Moreover, the accuracy of the generated summaries was evaluated by five medical doctors, proving that the summaries keep the same relevant information as the medical reports, despite being much more concise.
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Giordano, D., Kavasidis, I., Spampinato, C. (2015). Automatic Summary Creation by Applying Natural Language Processing on Unstructured Medical Records. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9257. Springer, Cham. https://doi.org/10.1007/978-3-319-23117-4_33
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DOI: https://doi.org/10.1007/978-3-319-23117-4_33
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