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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

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.

Item Type: Conference or Workshop Item (Paper)


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.

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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
Depositing User: Kathy Clawson


Item ID: 11994
Official URL:

Users with ORCIDS

ORCID for Kathy Clawson: ORCID iD

Catalogue record

Date Deposited: 12 May 2020 18:04
Last Modified: 30 Sep 2020 10:47


Author: Kathy Clawson ORCID iD
Author: Omar Kawi
Author: Paul Dunn
Author: Daniel Knight
Author: Jonathan Hodgson
Author: Yonghong Peng

University Divisions

Faculty of Technology > School of Computer Science


Computing > Data Science
Computing > Artificial Intelligence
Computing > Information Systems

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