Extracting supporting evidence from medical negligence claim texts


Bevany, R, Torrisiy, A, Bollegalay, D, Coeneny, F and Atkinsony, K (2019) Extracting supporting evidence from medical negligence claim texts. .

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Abstract

The number of medical negligence claims filed in the UK each year has increased significantly over the past decade [NHS, 2018]. When filing a medical negligence claim, electronic health records act as a legally valid important source of evidence. Patients often undergo different and complex treatments over many months or years, easily resulting in hundreds of pages of electronically available medical records. Therefore, it is a non-trivial task to read all the related electronic health records and identify the supporting evidence to establish a legal case. Currently, the process of identifying evidence is carried out by humans who are experts in both medical negligence law and medicine. In this paper, we compare different methods of automatically extracting relevant statements from medical negligence claim texts, to move towards building a method for extracting relevant sections from electronic health records with the aim of expediting the litigation process and reducing the manual efforts involved. Specifically, we annotate a dataset containing medical negligence claim texts and train conditional random field (CRF) and long short-term memory (LSTM) network models for extracting information relevant to cases. Our evaluation shows that each model class has its merits in this task: the CRF models were significantly more effective in identifying full sequences, while the LSTMs were significantly better at assigning tags to tokens. We found both approaches were able to identify information that is key to the litigation process.

Item Type: Conference or Workshop Item (Unspecified)
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 20 Jan 2022 08:50
Last Modified: 23 Nov 2023 22:56
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URI: https://livrepository.liverpool.ac.uk/id/eprint/3147183
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