scholarly journals Prevalence of Financial Considerations Documented in Primary Care Encounters as Identified by Natural Language Processing Methods

2019 ◽  
Vol 2 (8) ◽  
pp. e1910399
Author(s):  
Meliha Skaljic ◽  
Ihsaan H. Patel ◽  
Amelia M. Pellegrini ◽  
Victor M. Castro ◽  
Roy H. Perlis ◽  
...  
Keyword(s):  
Primary Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Processing Methods

Fast Neural Network Engine for Natural Science Language Processing: A Drug-Search Case.

2020 ◽  
Author(s):  
Vadim V. Korolev ◽  
Artem Mitrofanov ◽  
Kirill Karpov ◽  
Valery Tkachenko
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Natural Science ◽  
Therapeutic Agent ◽  
Semantic Relations ◽  
Chemical Data ◽  
Processing Methods ◽  
Modern Natural

The main advantage of modern natural language processing methods is a possibility to turn an amorphous human-readable task into a strict mathematic form. That allows to extract chemical data and insights from articles and to find new semantic relations. We propose a universal engine for processing chemical and biological texts. We successfully tested it on various use-cases and applied to a case of searching a therapeutic agent for a COVID-19 disease by analyzing PubMed archive.

scholarly journals Natural language processing for automated annotation of medication mentions in primary care visit conversations

JAMIA Open ◽  
2021 ◽  
Vol 4 (3) ◽  
Author(s):  
Craig H Ganoe ◽  
Weiyi Wu ◽  
Paul J Barr ◽  
William Haslett ◽  
Michelle D Dannenberg ◽  
...  
Keyword(s):  
Primary Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Information Extraction ◽  
Language Processing ◽  
Primary Care Visit ◽  
Data Set ◽  
Test Set ◽  
Medication Information ◽  
Care Visit

Abstract Objectives The objective of this study is to build and evaluate a natural language processing approach to identify medication mentions in primary care visit conversations between patients and physicians. Materials and Methods Eight clinicians contributed to a data set of 85 clinic visit transcripts, and 10 transcripts were randomly selected from this data set as a development set. Our approach utilizes Apache cTAKES and Unified Medical Language System controlled vocabulary to generate a list of medication candidates in the transcribed text and then performs multiple customized filters to exclude common false positives from this list while including some additional common mentions of the supplements and immunizations. Results Sixty-five transcripts with 1121 medication mentions were randomly selected as an evaluation set. Our proposed method achieved an F-score of 85.0% for identifying the medication mentions in the test set, significantly outperforming existing medication information extraction systems for medical records with F-scores ranging from 42.9% to 68.9% on the same test set. Discussion Our medication information extraction approach for primary care visit conversations showed promising results, extracting about 27% more medication mentions from our evaluation set while eliminating many false positives in comparison to existing baseline systems. We made our approach publicly available on the web as an open-source software. Conclusion Integration of our annotation system with clinical recording applications has the potential to improve patients’ understanding and recall of key information from their clinic visits, and, in turn, to positively impact health outcomes.

A Proof of Concept for Assessing Emergency Room Use with Primary Care Data and Natural Language Processing

2013 ◽  
Vol 52 (01) ◽  
pp. 33-42 ◽  
Author(s):  
M.-H. Kuo ◽  
P. Gooch ◽  
J. St-Maurice
Keyword(s):  
Mental Health ◽  
Primary Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Emergency Room ◽  
Language Processing ◽  
Free Text ◽  
Proof Of Concept ◽  
Term Extraction ◽  
Primary Care Data

SummaryObjective: The objective of this study was to undertake a proof of concept that demonstrated the use of primary care data and natural language processing and term extraction to assess emergency room use. The study extracted biopsychosocial concepts from primary care free text and related them to inappropriate emergency room use through the use of odds ratios.Methods: De-identified free text notes were extracted from a primary care clinic in Guelph, Ontario and analyzed with a software toolkit that incorporated General Architecture for Text Engineering (GATE) and MetaMap components for natural language processing and term extraction.Results: Over 10 million concepts were extracted from 13,836 patient records. Codes found in at least 1% percent of the sample were regressed against inappropriate emergency room use. 77 codes fell within the realm of biopsychosocial, were very statistically significant (p < 0.001) and had an OR > 2.0. Thematically, these codes involved mental health and pain related concepts.Conclusions: Analyzed thematically, mental health issues and pain are important themes; we have concluded that pain and mental health problems are primary drivers for inappropriate emergency room use. Age and sex were not significant. This proof of concept demonstrates the feasibly of combining natural language processing and primary care data to analyze a system use question. As a first work it supports further research and could be applied to investigate other, more complex problems.

scholarly journals Natural Language Processing methods for Document Matching

2020 ◽  
Vol 6 (12) ◽  
pp. 379-383
Author(s):  
Maitri Patel and Dr Hemant D Vasava
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Original Work ◽  
Educational Systems ◽  
Processing Methods ◽  
Similar Test ◽  
Subject Examination ◽  
Different Types ◽  
Higher Educational

Data,Information or knoweldge,in this rapidly moving and growing world.we can find any kind of information on Internet.And this can be too useful,however for acedemic world too it is useful but along with it plagarism is highly in practice.Which makes orginality of work degrade and fraudly using someones original work and later not acknowleging them is becoming common.And some times teachers or professors could not identify the plagarised information provided.So higher educational systems nowadays use different types of tools to compare.Here we have an idea to match no of different documents like assignments of students to compare with each other to find out, did they copied each other’s work?Also an idea to compare ideal answeer sheet of particular subject examination to similar test sheets of students.Idea is to compare and on similarity basis we can rank them.Both approach is one kind and that is to compare documents.To identify plagarism there are many methods used already.So we could compare and develop them if needed.

scholarly journals Topic Modeling for Keyword Extraction: using Natural Language Processing methods for keyword extraction in Portal Min@s

2015 ◽  
Vol 23 (3) ◽  
pp. 695 ◽  
Author(s):  
Arnaldo Candido Junior ◽  
Célia Magalhães ◽  
Helena Caseli ◽  
Régis Zangirolami
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Keyword Extraction ◽  
Processing Methods ◽  
Dirichlet Allocation

<p style="margin-bottom: 0cm; line-height: 100%;" align="justify"> </p><p>Este artigo tem o objetivo da avaliar a aplicação de dois métodos automáticos eficientes na extração de palavras-chave, usados pelas comunidades da Linguística de <em>Corpus </em>e do Processamento da Língua Natural para gerar palavras-chave de textos literários: o <em>WordSmith Tools </em>e o <em>Latent Dirichlet Allocation </em>(LDA). As duas ferramentas escolhidas para este trabalho têm suas especificidades e técnicas diferentes de extração, o que nos levou a uma análise orientada para a sua performance. Objetivamos entender, então, como cada método funciona e avaliar sua aplicação em textos literários. Para esse fim, usamos análise humana, com conhecimento do campo dos textos usados. O método LDA foi usado para extrair palavras-chave por meio de sua integração com o <em>Portal Min@s: Corpora de Fala e Escrita</em>, um sistema geral de processamento de <em>corpora</em>, concebido para diferentes pesquisas de Linguística de <em>Corpus</em>. Os resultados do experimento confirmam a eficácia do WordSmith Tools e do LDA na extração de palavras-chave de um <em>corpus </em>literário, além de apontar que é necessária a análise humana das listas em um estágio anterior aos experimentos para complementar a lista gerada automaticamente, cruzando os resultados do WordSmith Tools e do LDA. Também indicam que a intuição linguística do analista humano sobre as listas geradas separadamente pelos dois métodos usados neste estudo foi mais favorável ao uso da lista de palavras-chave do WordSmith Tools.</p>

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