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A Supervised Named-Entity Extraction System for Medical Text
2013
Conference and Labs of the Evaluation Forum
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We developed a supervised CRF model that based on a rich set of features learns to predict disorder named entities. ...
We present our participation in Task 1a of the 2013 CLEF-eHEALTH Challenge, whose goal was the identification of disorder named entities from electronic medical records. ...
For example, named-entity extraction tools process the plain text of EMRs and extract instances of named entities (i.e., noun phrases) that can be classified into a certain semantic category. ...
dblp:conf/clef/BodnariDLNZ13
fatcat:6j2gyrtpqnajdoffz2ih7uq65y
FABLE: A Semi-Supervised Prescription Information Extraction System
2018
AMIA Annual Symposium Proceedings
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In this paper, we describe FABLE, a system for automatically extracting prescription information from discharge summaries. ...
As a result, narratives of EHRs need to be processed with natural language processing (NLP) methods that can extract medication and prescription information from free text. ...
Additionally, sparsity of some medication entity categories (such as medication names, reasons, and durations) that exhibit high lexical variation presents a challenge for supervised machine learning systems ...
pmid:30815199
pmcid:PMC6371278
fatcat:ycwiiuwa4bddbdgnvdev36w6um
Learning for clinical named entity recognition without manual annotations
2018
Informatics in Medicine Unlocked
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A B S T R A C T Background: Named entity recognition (NER) systems are commonly built using supervised methods that use machine learning to learn from corpora manually annotated with named entities. ...
It only requires a raw text corpus and a resource like UMLS that can give a list of named entities along with their semantic types. ...
Acknowledgement We thank the organizers of SemEval 2014 Task 7 and i2b2 2010 shared-task for creating and providing the data which was used in this work. ...
doi:10.1016/j.imu.2018.10.011
fatcat:tevpmyr3njgffmqhaypbxyehpq
Revisiting Medical Entity Recognition through the Guidelines of the Aurora Initiative
2016
International Journal of Bio-Science and Bio-Technology
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Named entity recognition (NER) is a subtask of Clinical documentation processing which is important not only for text analysis but knowledge extraction. ...
Although there are a number of clinical named entity recognition systems, they lack user flexibility and NER scalability. ...
Literature Review There are many different techniques used by researchers to recognise the named entities in unstructured texts namely supervised, unsupervised and semi-supervised. ...
doi:10.14257/ijbsbt.2016.8.4.13
fatcat:amthxl4m4zd5pdtahwl2qu76ry
Using Local Grammar for Entity Extraction from Clinical Reports
2015
International Journal of Interactive Multimedia and Artificial Intelligence
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Hence, a system for extracting this information in a structured form can benefits healthcare professionals. ...
The work presented in this paper uses a local grammar approach to extract medical named entities from French patient clinical reports. ...
Local Grammar based Approach for Extracting Named Medical Entities In this work we study French CR to extract medical named entities using local grammar. ...
doi:10.9781/ijimai.2015.332
fatcat:i7q5tilqk5cozds5vz4b4eyqgq
Knowledge-guided Text Structuring in Clinical Trials
[article]
2019
arXiv
pre-print
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Previous research has focused on extracting information from eligibility criteria, with usually a single pair of medical entity and attribute, but seldom considering other kinds of free text with multiple ...
entities, attributes and relations that are more complex for parsing. ...
Example of free text annotated by entity, attribute and relation between them "Body Mass Index" is the medical entity, often identified as Unified Medical Language System (UMLS) concept with an unique ...
arXiv:1912.12380v1
fatcat:6d4bj5pprfew3dhwkjnuy2pske
Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering
[article]
2024
arXiv
pre-print
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2023 pre-print arXiv:2303.16416v2 fatcat:2ijb26rg6bbuvjopeut5fkd46aOther Versions
Objective: This study quantifies the capabilities of GPT-3.5 and GPT-4 for clinical named entity recognition (NER) tasks and proposes task-specific prompts to improve their performance. ...
extraction shared task, and (2) identifying nervous system disorder-related adverse events from safety reports in the vaccine adverse event reporting system (VAERS). ...
To avoid penalizing the model for such Entity Prompt-1 Prompt-2 Medical Problem Extract without rephrasing all medical problem entities from the following note in a list format: Extract without rephrasing ...
arXiv:2303.16416v3
fatcat:hpqkuxzvavbjbgoevejzk4nd6u
Overview of CCKS 2020 Task 3: Named Entity Recognition and Event Extraction in Chinese Electronic Medical Records
2021
Data Intelligence
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The China Conference on Knowledge Graph and Semantic Computing (CCKS) 2020 Evaluation Task 3 presented clinical named entity recognition and event extraction for the Chinese electronic medical records. ...
Two annotated data sets and some other additional resources for these two subtasks were provided for participators. ...
It only requires a raw text corpus and a resource like Unified Medical Language System (UMLS) that can give a list of named entities along with their semantic types. ...
doi:10.1162/dint_a_00093
fatcat:dv4hcft77zenrhhwlg7gycuhhq
Information Extraction from the Text Data on Traditional Chinese Medicine: A Review on Tasks, Challenges, and Methods from 2010 to 2021
2022
Evidence-Based Complementary and Alternative Medicine
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Developing a method of information extraction (IE) from these sources to generate a cohesive data set would be a great contribution to the medical field. ...
In the future, IE work should be promoted by extracting more existing entities and relations, constructing gold standard data sets, and exploring IE methods based on a small amount of labeled data. ...
Acknowledgments e authors thank International Science Editing (http:// www.internationalscienceediting.com) for editing this manuscript. is work was supported by the National Natural Science Foundation ...
doi:10.1155/2022/1679589
pmid:35600940
pmcid:PMC9122692
fatcat:r7sj7sdoubhwfhcscj227neoiy
A Survey on Recent Named Entity Recognition and Relationship Extraction Techniques on Clinical Texts
2021
Applied Sciences
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Named Entity Recognition (NER) and Relationship Extraction (RE) are key components of information extraction tasks in the clinical domain. ...
This huge amount of clinical text data has motivated the development of new information extraction and text mining techniques. ...
In the following year, relationship extraction from the large plain text was conducted, where a system named Snowball introduced novel strategies for pattern generation [22] . ...
doi:10.3390/app11188319
fatcat:notb6zimcvfxhhuaik73t75sje
novel deep neural network framework for biomedical named entity recognition
2022
International Journal of Health Sciences
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Entity Recognition (ER) meant to extract and recognize the entities from any text. ...
Biomedical Named Entity Recognition (BNER) gets more and more attention from the researchers since it is a fundamental task in biomedical information extraction. ...
Recognition in Biomedical domain Named entity recognition(NER) is a sub-task of NLP,the purpose of identifying named entities based in articles into predefined classes.A typical Bio-NER system is shown ...
doi:10.53730/ijhs.v6ns5.9557
fatcat:pzmgdcgg7fcploqwjz7otidi5q
A Silver Standard Biomedical Corpus for Arabic Language
2020
Complexity
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Therefore, in this work, we present a method to develop a silver standard medical corpus for the Arabic language with a dictionary as a minimal supervision tool. ...
The corpus contains 49,856 sentences tagged with 13 entity types corresponding to a subset of UMLS (Unified Medical Language System) concept types. ...
use of the corpus, which can be for Named Entity Recognition (NER) or Relation Extraction (RE) tasks that both serve information extraction. ...
doi:10.1155/2020/8896659
fatcat:6gqmp6hzbzhppprr6bbi2hb7de
Biomedical Relation Extraction Using Distant Supervision
2020
Scientific Programming
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This study presents an overview of relation extraction using distant supervision, providing a generalized architecture of this task based on the state-of-the-art work that proposed this method. ...
Besides, it surveys the methods used in the literature targeting this topic with a description of different knowledge bases used in the process along with the corpora, which can be helpful for beginner ...
from unstructured medical data using information extraction systems. ...
doi:10.1155/2020/8893749
fatcat:okfqki7lazbo7ilffs2eafzoru
Building Structured Databases of Factual Knowledge from Massive Text Corpora
2017
Proceedings of the 2017 ACM International Conference on Management of Data - SIGMOD '17
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State-of-the-art information extraction systems have strong reliance on large amounts of task/corpus-specific labeled data (usually created by domain experts). ...
In this tutorial, we introduce data-driven methods on mining structured facts (i.e., entities and their relations/attributes for types of interest) from massive text corpora, to construct structured databases ...
Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon. ...
doi:10.1145/3035918.3054781
dblp:conf/sigmod/RenJSH17
fatcat:3mrzx3e3yzapnknx5ciwcwm4i4
Learning for Biomedical Information Extraction: Methodological Review of Recent Advances
[article]
2016
arXiv
pre-print
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Unlike existing reviews covering a holistic view on BioIE, this review focuses on mainly recent advances in learning based approaches, by systematically summarizing them into different aspects of methodological ...
In addition, we dive into open information extraction and deep learning, two emerging and influential techniques and envision next generation of BioIE. ...
+mining OR text+processing OR natural+language+processing) AND (information+extraction OR named+entity+detection OR named+entity+recognition OR relation+extraction OR event+extraction)". ...
arXiv:1606.07993v1
fatcat:7d5om7zxxzhoviiriasrfwg3xi
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