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Prescription Information Extraction from Electronic Health Records using BiLSTM-CRF and Word Embeddings

Abstract Medical records are increasingly being recorded in the form of electronic health records (EHRs), with a significant amount of patient data recorded as unstructured natural language text. Consequently, being able to extract and utilize clinical data present within these records is an important step in furthering clinical care. One important aspect within these records is the presence of prescription information. Existing techniques for extracting prescription information — which includes medication names, dosages, frequencies, reasons for taking, and mode of administration — from unstructured text have focused on the application of rule- and classifier-based methods. While state-of-the-art systems can be effective in extracting many types of... (more)
Created Date 2018-05
Contributor Rawal, Samarth Chetan (Author) / Baral, Chitta (Thesis Director) / Anwar, Saadat (Committee Member) / Computer Science and Engineering Program / Barrett, The Honors College
Subject Natural Language Processing / Artificial Intelligence / Electronic Health Records / Machine Learning / Deep Learning
Series Academic Year 2017-2018
Type Text
Extent 21 pages
Language English
Reuse Permissions All Rights Reserved
Collaborating Institutions Barrett, the Honors College
Additional Formats MODS / OAI Dublin Core / RIS

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