11,942 research outputs found
A survey on recent advances in named entity recognition
Named Entity Recognition seeks to extract substrings within a text that name
real-world objects and to determine their type (for example, whether they refer
to persons or organizations). In this survey, we first present an overview of
recent popular approaches, but we also look at graph- and transformer- based
methods including Large Language Models (LLMs) that have not had much coverage
in other surveys. Second, we focus on methods designed for datasets with scarce
annotations. Third, we evaluate the performance of the main NER implementations
on a variety of datasets with differing characteristics (as regards their
domain, their size, and their number of classes). We thus provide a deep
comparison of algorithms that are never considered together. Our experiments
shed some light on how the characteristics of datasets affect the behavior of
the methods that we compare.Comment: 30 page
CCheXR-Attention: Clinical concept extraction and chest x-ray reports classification using modified Mogrifier and bidirectional LSTM with multihead attention
Radiology reports cover different aspects, from radiological observation to the diagnosis of an imaging examination, such as X-rays, MRI, and CT scans. Abundant patient information presented in radiology reports poses a few major challenges. First, radiology reports follow a free-text reporting format, which causes the loss of a large amount of information in unstructured text. Second, the extraction of important features from these reports is a huge bottleneck for machine learning models. These challenges are important, particularly the extraction of key features such as symptoms, comparison/priors, technique, finding, and impression because they facilitate the decision-making on patients’ health. To alleviate this issue, a novel architecture CCheXR-Attention is proposed to extract the clinical features from the radiological reports and classify each report into normal and abnormal categories based on the extracted information. We have proposed a modified mogrifier LSTM model and integrated a multihead attention method to extract the more relevant features. Experimental outcomes on two benchmark datasets demonstrated that the proposed model surpassed state-of-the-art models
Enhancing Drug Overdose Mortality Surveillance through Natural Language Processing and Machine Learning
Epidemiological surveillance is key to monitoring and assessing the health of populations. Drug overdose surveillance has become an increasingly important part of public health practice as overdose morbidity and mortality has increased due in large part to the opioid crisis. Monitoring drug overdose mortality relies on death certificate data, which has several limitations including timeliness and the coding structure used to identify specific substances that caused death. These limitations stem from the need to analyze the free-text cause-of-death sections of the death certificate that are completed by the medical certifier during death investigation. Other fields, including clinical sciences, have utilized natural language processing (NLP) methods to gain insight from free-text data, but thus far, adoption of NLP methods in epidemiological surveillance has been limited. Through a narrative review of NLP methods currently used in public health surveillance and the integration of two NLP tasks, classification and named entity recognition, this dissertation enhances the capabilities of public health practitioners and researchers to perform drug overdose mortality surveillance. This dissertation advances both surveillance science and public health practice by integrating methods from bioinformatics into the surveillance pipeline which provides more timely and increased quality overdose mortality surveillance, which is essential to guiding effective public health response to the continuing drug overdose epidemic
On the Use of Parsing for Named Entity Recognition
[Abstract] Parsing is a core natural language processing technique that can be used to obtain the structure underlying sentences in human languages. Named entity recognition (NER) is the task of identifying the entities that appear in a text. NER is a challenging natural language processing task that is essential to extract knowledge from texts in multiple domains, ranging from financial to medical. It is intuitive that the structure of a text can be helpful to determine whether or not a certain portion of it is an entity and if so, to establish its concrete limits. However, parsing has been a relatively little-used technique in NER systems, since most of them have chosen to consider shallow approaches to deal with text. In this work, we study the characteristics of NER, a task that is far from being solved despite its long history; we analyze the latest advances in parsing that make its use advisable in NER settings; we review the different approaches to NER that make use of syntactic information; and we propose a new way of using parsing in NER based on casting parsing itself as a sequence labeling task.Xunta de Galicia; ED431C 2020/11Xunta de Galicia; ED431G 2019/01This work has been funded by MINECO, AEI and FEDER of UE through the ANSWER-ASAP project (TIN2017-85160-C2-1-R); and by Xunta de Galicia through a Competitive Reference Group grant (ED431C 2020/11). CITIC, as Research Center of the Galician University System, is funded by the ConsellerÃa de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF/FEDER) with 80%, the Galicia ERDF 2014-20 Operational Programme, and the remaining 20% from the SecretarÃa Xeral de Universidades (Ref. ED431G 2019/01). Carlos Gómez-RodrÃguez has also received funding from the European Research Council (ERC), under the European Union’s Horizon 2020 research and innovation programme (FASTPARSE, Grant No. 714150)
Application of Biomedical Text Mining
With the enormous volume of biological literature, increasing growth phenomenon due to the high rate of new publications is one of the most common motivations for the biomedical text mining. Aiming at this massive literature to process, it could extract more biological information for mining biomedical knowledge. Using the information will help understand the mechanism of disease generation, promote the development of disease diagnosis technology, and promote the development of new drugs in the field of biomedical research. Based on the background, this chapter introduces the rise of biomedical text mining. Then, it describes the biomedical text-mining technology, namely natural language processing, including the several components. This chapter emphasizes the two aspects in biomedical text mining involving static biomedical information recognization and dynamic biomedical information extraction using instance analysis from our previous works. The aim is to provide a way to quickly understand biomedical text mining for some researchers
Hi, how can I help you?: Automating enterprise IT support help desks
Question answering is one of the primary challenges of natural language
understanding. In realizing such a system, providing complex long answers to
questions is a challenging task as opposed to factoid answering as the former
needs context disambiguation. The different methods explored in the literature
can be broadly classified into three categories namely: 1) classification
based, 2) knowledge graph based and 3) retrieval based. Individually, none of
them address the need of an enterprise wide assistance system for an IT support
and maintenance domain. In this domain the variance of answers is large ranging
from factoid to structured operating procedures; the knowledge is present
across heterogeneous data sources like application specific documentation,
ticket management systems and any single technique for a general purpose
assistance is unable to scale for such a landscape. To address this, we have
built a cognitive platform with capabilities adopted for this domain. Further,
we have built a general purpose question answering system leveraging the
platform that can be instantiated for multiple products, technologies in the
support domain. The system uses a novel hybrid answering model that
orchestrates across a deep learning classifier, a knowledge graph based context
disambiguation module and a sophisticated bag-of-words search system. This
orchestration performs context switching for a provided question and also does
a smooth hand-off of the question to a human expert if none of the automated
techniques can provide a confident answer. This system has been deployed across
675 internal enterprise IT support and maintenance projects.Comment: To appear in IAAI 201
De-identifying Hospital Discharge Summaries: An End-to-End Framework using Ensemble of Deep Learning Models
Electronic Medical Records (EMRs) contain clinical narrative text that is of
great potential value to medical researchers. However, this information is
mixed with Personally Identifiable Information (PII) that presents risks to
patient and clinician confidentiality. This paper presents an end-to-end
de-identification framework to automatically remove PII from hospital discharge
summaries. Our corpus included 600 hospital discharge summaries which were
extracted from the EMRs of two principal referral hospitals in Sydney,
Australia. Our end-to-end de-identification framework consists of three
components: 1) Annotation: labelling of PII in the 600 hospital discharge
summaries using five pre-defined categories: person, address, date of birth,
identification number, phone number; 2) Modelling: training six named entity
recognition (NER) deep learning base-models on balanced and imbalanced
datasets; and evaluating ensembles that combine all six base-models, the three
base-models with the best F1 scores and the three base-models with the best
recall scores respectively, using token-level majority voting and stacking
methods; and 3) De-identification: removing PII from the hospital discharge
summaries. Our results showed that the ensemble model combined using the
stacking Support Vector Machine (SVM) method on the three base-models with the
best F1 scores achieved excellent results with a F1 score of 99.16% on the test
set of our corpus. We also evaluated the robustness of our modelling component
on the 2014 i2b2 de-identification dataset. Our ensemble model, which uses the
token-level majority voting method on all six base-models, achieved the highest
F1 score of 96.24% at strict entity matching and the highest F1 score of 98.64%
at binary token-level matching compared to two state-of-the-art methods. The
framework provides a robust solution to de-identifying clinical narrative text
safely
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