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Implicit Entity Recognition in Clinical Documents

Sujan Perera, Pablo Mendes, Amit Sheth, Krishnaprasad Thirunarayan, Adarsh Alex, Christopher Heid, Greg Mott
2015 Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics  
We term those implicit entity mentions and introduce the problem of implicit entity recognition (IER) in clinical documents.  ...  However, clinical documents often contain phrases that indicate the entities but do not contain their names.  ...  Acknowledgement We acknowledge the medical students Logan Markins, Kara Joseph, and Robert Beaulieu of Wright State University for their assistance in creating the gold-standard corpus.  ... 
doi:10.18653/v1/s15-1028 dblp:conf/starsem/PereraMSTAHM15 fatcat:h54xjsnj2bd7he6hlrqov35sda

Implicit Information Extraction from Clinical Notes

Sujan Perera, Amit Sheth
2015 2015 International Conference on Healthcare Informatics  
Here we introduce the problem of 'implicit entity recognition in clinical notes', propose a knowledge driven approach to address this problem and demonstrate the results of our initial experiments.  ...  We address the problem of extracting implicit information from the unstructured clinical notes.  ...  These tools recognize the named entity recognition and linking as a primary task in clinical notes. Clinical notes consist of entities indicated in both explicit and implicit manner.  ... 
doi:10.1109/ichi.2015.88 dblp:conf/ichi/PereraS15 fatcat:p5vhkckrh5fszm7qpagepvotbe

Inferring Implicit Causal Relationships in Biomedical Literature

Halil Kilicoglu
2016 Proceedings of the 15th Workshop on Biomedical Natural Language Processing  
In this paper, we present a study of textual characteristics that contribute to expression of implicit causal relations across sentence boundaries.  ...  Biomedical relations are often expressed between entities occurring within the same sentence through syntactic means.  ...  Whether the chemical en-tity and the disease entity may be document topics are also included as features. We simply included all entities that appear in the title of the article as document topics.  ... 
doi:10.18653/v1/w16-2906 dblp:conf/bionlp/Kilicoglu16 fatcat:dvixzmljz5e5bmnriu274nt4uy

Knowledge will propel machine understanding of content

Amit Sheth, Sujan Perera, Sanjaya Wijeratne, Krishnaprasad Thirunarayan
2017 Proceedings of the International Conference on Web Intelligence - WI '17  
, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media.  ...  In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully.  ...  Points of view or opinions in this document are those of the authors and do not necessarily represent the official position or policies of the NIH or NSF.  ... 
doi:10.1145/3106426.3109448 pmid:29962511 pmcid:PMC6021355 dblp:conf/webi/ShethPWT17 fatcat:rirrbhctsreilbuyy32uzmuyhy

Mining the pharmacogenomics literature--a survey of the state of the art

U. Hahn, K. B. Cohen, Y. Garten, N. H. Shah
2012 Briefings in Bioinformatics  
dealing with the automatic recognition of relevant named entities (e.g. genes, gene variants and proteins, diseases and other pathological phenomena, drugs and other chemicals relevant for medical treatment  ...  Therefore, publications span the intersection of research in genotypes, phenotypes and pharmacology, a topic that has increasingly become a focus of active research in recent years.This survey covers efforts  ...  In the latest 2010 Challenge, i2b2 has moved its thematic scope to account for clinical named entity recognition and relation extraction tasks [96] .  ... 
doi:10.1093/bib/bbs018 pmid:22833496 pmcid:PMC3404399 fatcat:por4dnthkrcxjdsir6uc64kdaq

Extracting Biomarker Information Applying Natural Language Processing and Machine Learning

Md. Tawhidul Islam, Mostafa Shaikh, Abhaya Nayak, Shoba Ranganathan
2010 Bioinformatics and Biomedical Engineering (iCBBE), International Conference on  
to bio-medical entities (i.e., semantic category recognition).  ...  In this paper, we detail an approach to a very specific task of information extraction namely, extracting biomarker information in biomedical literature.  ...  The task of the semantic category recognition SVM is to find and label the evaluative sentence in a document. The features used for this case are discussed in section II.  ... 
doi:10.1109/icbbe.2010.5514717 fatcat:jeuiavsuibfq3exbcbisimkebe

Detecting mentions of pain and acute confusion in Finnish clinical text

Hans Moen, Kai Hakala, Farrokh Mehryary, Laura-Maria Peltonen, Tapio Salakoski, Filip Ginter, Sanna Salanterä
2017 BioNLP 2017  
In the first approach this is treated as a traditional mention-level named-entity recognition task, while the second approach treats it as a sentencelevel multi-label classification task.  ...  Our results suggest that the mentionlevel named-entity recognition approach outperforms sentence-level classification overall, but the latter approach still manages to achieve the best prediction scores  ...  We would also like to thank Juho Heimonen for assisting us in preprocessing the clinical text.  ... 
doi:10.18653/v1/w17-2347 dblp:conf/bionlp/MoenHMPSGS17 fatcat:lcoxur2375finhhtswieabjmoe

Drug Side Effect Detection as Implicit Opinion from Medical Reviews (Research Article)

Monireh Ebrahimi, Amir Hossein Yazdavar, Naomie Binti Salim, Shirin Noekhah, Deborah Libu Paris
2016 Research Journal of Applied Sciences Engineering and Technology  
Extracting explicit and implicit opinions is one of the main tasks in the opinion mining area.  ...  As implicit opinion mining is a complicated task, limited work has been done on it, especially in the medical domain, as implicit opinion is a domain dependent task.  ...  The primary entity recognition systems use rule-based methods to extract data from medical documents (Zweigenbaum et al., 2007) .  ... 
doi:10.19026/rjaset.12.2850 fatcat:6emgwxt6xfckhon3pglmgkiahm

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples [article]

Amit Sheth, Sujan Perera, Sanjaya Wijeratne
2019 arXiv   pre-print
) are not available or labor intensive to create, (ii) the objects (particularly text) to be recognized are complex (i.e., beyond simple entity-person/location/organization names), such as implicit entities  ...  In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition of utilizing knowledge whenever it is available or can be created purposefully.  ...  State-of-the-art named entity recognition applications do not capture implicit entities [24] .  ... 
arXiv:1610.07708v2 fatcat:pjiibdsxabfdhchx6xnpuaej4m

PharmaCoNER: Pharmacological Substances, Compounds and proteins Named Entity Recognition track

Aitor Gonzalez Agirre, Montserrat Marimon, Ander Intxaurrondo, Obdulia Rabal, Marta Villegas, Martin Krallinger
2019 Proceedings of The 5th Workshop on BioNLP Open Shared Tasks  
Taking into account the growing amount of medical publications and clinical records written in Spanish, we have organized the first shared task on detecting drug and chemical entities in Spanish medical  ...  documents.  ...  We would also like to thank Siamak Barzegar for his help in setting up PharmaCoNER at CodaLab.  ... 
doi:10.18653/v1/d19-5701 dblp:conf/bionlp/Gonzalez-Agirre19 fatcat:epkf2zw5f5adpktuzypltv2q2m

Semantic Annotation of Medical Documents in CDA Context [chapter]

Diego Monti, Maurizio Morisio
2016 Lecture Notes in Computer Science  
In this work we develop the concept of a system designed to convert legacy medical documents into a standard and interoperable format compliant with the Clinical Document Architecture model by the means  ...  The goal of this work is to recover semantic and structural information from medical documents in electronic format.  ...  The problem of discovering entities in a text is known in computational linguistic as Named Entity Recognition (NER).  ... 
doi:10.1007/978-3-319-43949-5_12 fatcat:d5yq42e6pzd4tctf6g3twraieq

Understanding Clinical Trial Reports: Extracting Medical Entities and Their Relations [article]

Benjamin E. Nye, Jay DeYoung, Eric Lehman, Ani Nenkova, Iain J. Marshall, Byron C. Wallace
2022 arXiv   pre-print
The best evidence concerning comparative treatment effectiveness comes from clinical trials, the results of which are reported in unstructured articles.  ...  Here we consider the end-to-end task of both (a) extracting treatments and outcomes from full-text articles describing clinical trials (entity identification) and, (b) inferring the reported results for  ...  This model yielded superior performance on the task, presumably due to its implicit incorporation of domain knowledge concerning how trial reports are structured.  ... 
arXiv:2010.03550v3 fatcat:anlyo7kn2fhjnlkgbxmc4zafty

MedTime: A temporal information extraction system for clinical narratives

Yu-Kai Lin, Hsinchun Chen, Randall A. Brown
2013 Journal of Biomedical Informatics  
It achieved a micro-averaged f-measure of 0.88 in both the recognitions of clinical events and temporal expressions.  ...  We proposed and evaluated three time normalization strategies to normalize relative time expressions in clinical texts.  ...  We extend and modify JChronic to better handle the implicit and relative TEs in the clinical narratives.  ... 
doi:10.1016/j.jbi.2013.07.012 pmid:23911344 fatcat:qewpk55nvvcspfeqmoxoftnq2q

The Generation of a Corpus for Clinical Sentiment Analysis [chapter]

Yihan Deng, Thierry Declerck, Piroska Lendvai, Kerstin Denecke
2016 Lecture Notes in Computer Science  
Clinical care providers express their judgments and observations towards the patient status in clinical narratives.  ...  In contrast to sentiment expressions in general domains targeted by language technology, clinical sentiments are influenced by related medical events such as clinical precondition or outcome of a treatment  ...  Neither sentiment analysis nor stance detection have been thoroughly scrutinized in the clinical domain yet -one of the first studies on sentiment in clinical documents that we are aware of is from Denecke  ... 
doi:10.1007/978-3-319-47602-5_46 fatcat:26gebmecy5fkpmarjmh4te7prq

Detecting Adverse Drug Events with Rapidly Trained Classification Models

Alec B. Chapman, Kelly S. Peterson, Patrick R. Alba, Scott L. DuVall, Olga V. Patterson
2019 Drug Safety  
Key Points Narrative clinical notes in electronic health records are frequently the only documentation of an occurred adverse drug event (ADE).  ...  It employs a conditional random field (CRF) model for named entity recognition (NER) and a random forest model for relation extraction (RE).  ...  The set of features included in the final model are as follows: Relation Extraction (RE) Once entities are detected in clinical documents, appropriate entities have to be linked in a relationship that  ... 
doi:10.1007/s40264-018-0763-y fatcat:nvvbvjrlezb4xar5ekp2j4h6f4
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