Medical Formulation Recognition (MFR) using Deep Feature Learning and One Class SVM

Kawi, Omar, Clawson, Kathy, Dunn, Paul, Knight, Daniel, Hodgson, Jonathan and Peng, Yonghong (2020) Medical Formulation Recognition (MFR) using Deep Feature Learning and One Class SVM. In: The International Joint Conference on Neural Networks (IJCNN), July 19-24, Glasgow, UK.

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Abstract

Specials medications are personalized formulations manufactured on demand for patients with unique prescription requirements and constitute an essential component of patient treatment. Specials are becoming increasingly in demand due to the need for personalized and precision medicine. The timely provision of optimal personalized medicine, however, is challenging, subject to strict regulatory processes, and is expert intensive. In this paper, we propose a new medical formulation engine (MFE) that performs semantic search across multiple disparate formulations archives to enable data driven formulation intelligence. We develop a new platform for medical formulations recognition (MFR) that curates a new dataset comprising formulations and non-formulations (clinical) text and uses a novel pipeline encompassing deep feature extraction and one-class support vector machine learning. The proposed MFR framework demonstrates promising performance and can be used as a benchmark for future research in formulations recognition.

Item Type: Conference or Workshop Item (Paper)
Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Subjects: Computing > Data Science
Computing > Artificial Intelligence
Computing > Information Systems
Computing
Divisions: Faculty of Technology > School of Computer Science
Depositing User: Kathy Clawson
Date Deposited: 12 May 2020 18:04
Last Modified: 03 Aug 2020 15:40
URI: http://sure.sunderland.ac.uk/id/eprint/11994
ORCID for Kathy Clawson: ORCID iD orcid.org/0000-0001-8431-1524

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