Automated Encoding of Clinical Documents Based on Natural Language Processing

Friedman, Carol; Shagina, Lyudmila; Lussier, Yves A.; Hripcsak, George

doi:10.1197/jamia.m1552

Cited by 406 publications

(275 citation statements)

References 23 publications

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“…Quantitatively the precision of the tool presented here is on par with other similar tools such as MedLEE; 0.89 (Friedman 2004) and the tool presented in Meystre 2006; 0.76, despite that a relatively simple approach presented here.…”

Section: Fuzzy Mapping Featuressupporting

confidence: 48%

Negation scope and spelling variation for text-mining of Danish electronic patient records

Thomas¹,

Jensen²,

Werge³

et al. 2014

Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi)

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Electronic patient records are a potentially rich data source for knowledge extraction in biomedical research. Here we present a method based on the ICD10 system for text-mining of Danish health records. We have evaluated how adding functionalities to a baseline text-mining tool affected the overall performance. The purpose of the tool was to create enriched phenotypic profiles for each patient in a corpus consisting of records from 5,543 patients at a Danish psychiatric hospital, by assigning each patient additional ICD10 codes based on freetext parts of these records. The tool was benchmarked by manually curating a test set consisting of all records from 50 patients. The tool evaluated was designed to handle spelling and ending variations, shuffling of tokens within a term, and introduction of gaps in terms. In particular we investigated the importance of negation identification and negation scope. The most important functionality of the tool was handling of spelling variation, which greatly increased the number of phenotypes that could be identified in the records, without noticeably decreasing the precision. Further, our results show that different negations have different optimal scopes, some spanning only a few words, while others span up to whole sentences.

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Section: Fuzzy Mapping Featuressupporting

confidence: 48%

Negation scope and spelling variation for text-mining of Danish electronic patient records

Thomas¹,

Jensen²,

Werge³

et al. 2014

Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi)

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show abstract

“…Regarding biomedical text mining, tools like BioMedLEE [Friedman et al, 2004], MetaMap [Aronson and Lang, 2010] or SemRep [Liu et al, 2012] are closely related to our approach. The tools mostly focus on annotation of texts with concepts from standard biomedical vocabularies like UMLS which is very useful for many practical applications.…”

Section: Related Workmentioning

confidence: 99%

SKIMMR: Facilitating knowledge discovery in life sciences by machine-aided skim reading

Nováček

Burns

2014

Preprint

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Background: Unlike full reading, 'skim-reading' involves the process of looking quickly over information in an attempt to cover more material whilst still being able to retain a superficial view of the underlying content. Within this work, we specifically emulate this natural human activity by providing a dynamic graph-based view of entities automatically extracted from text. For the extraction, we use shallow parsing, co-occurrence analysis and semantic similarity computation techniques. Our main motivation is to assist biomedical researchers and clinicians in coping with increasingly large amounts of potentially relevant articles that are being published ongoingly in life sciences. Methods: To construct the high-level network overview of articles, we extract weighted binary statements from the text. We consider two types of these statements, co-occurrence and similarity, both organised in the same distributional representation (i.e., in a vectorspace model). For the co-occurrence weights, we use point-wise mutual information that indicates the degree of non-random association between two co-occurring entities. For computing the similarity statement weights, we use cosine distance based on the relevant co-occurrence vectors. These statements are used to build fuzzy indices of terms, statements and provenance article identifiers, which support fuzzy querying and subsequent result ranking. These indexing and querying processes are then used to construct a graphbased interface for searching and browsing entity networks extracted from articles, as well as articles relevant to the networks being browsed. Last but not least, we describe a methodology for automated experimental evaluation of the presented approach. The method uses formal comparison of the graphs generated by our tool to relevant gold standards based on manually curated PubMed, TREC challenge and MeSH data. Results: We provide a web-based prototype (called 'SKIMMR') that generates a network of inter-related entities from a set of documents which a user may explore through our interface. When a particular area of the entity network looks interesting to a user, the tool displays the documents that are the most relevant to those entities of interest currently shown in the network. We present this as a methodology for browsing a collection of research articles. PrePrints SKIMMR: Machine-Aided Skim Reading 2 report on the results of experimental evaluation using the two domains and one additional dataset based on the TREC challenge. The results show that the presented method for machine-aided skim reading outperforms tools like PubMed regarding focused browsing and informativeness of the browsing context. Conclusions: Both conceptually and practically, the SKIMMR methodology instantiates the notion of 'skim reading' within software. In preliminary trials, sample users find new, interesting and non-trivial facts with the tool. A comprehensive experimental evaluation of the SKIMMR prototype using simulations of various types of browsing behaviour show...

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“…The existing work on clinical document annotation focused on explicit entity mentions with contiguous phrases (Aronson, 2006) (Savova et al, 2010) (Friedman et al, 2004) (Fu and Ananiadou, 2014). Going one step beyond, the SemEval 2014 task 7 recognized the need for identifying discontiguous mentions of explicit entities .…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

2015

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will take place in Denver on June 4 and 5 and is colocated with SemEval and NAACL. As in 2014 at COLING, also on this occasion *SEM and SemEval chose to coordinate their programs by featuring a joint invited talk. In this way, *SEM aims to bring together the ACL SIGLEX and ACL SIGSEM communities.The acceptance rate of *SEM 2015 was quite competitive: out of 98 submissions, we accepted 36 papers for an overall acceptance of 37%. The acceptance rate of long paper that were accepted for oral presentation (18 out of 62) is 29%. The papers cover a wide range of topics including distributional semantics; lexical semantics and lexical acquisition; formal and linguistic semantics; discourse semantics; lexical resources, linked data and ontologies; semantics for applications; and extra-propositional semantics: sentiment and figurative meaning.The *SEM 2015 program consists of oral presentations for selected long papers and a poster session for long and short papers.Day One, June 4th:• Joint *SEM SemEval keynote talk by Marco Baroni;• Oral presentation sessions on distributional semantics, lexical semantics, and extra-propositional semantics;• Poster session.Day Two, June 5th:• Keynote talk by Preslav Natkov;• Oral presentation sessions on semantics for applications, lexical resources and ontologies, formal semantics, and discourse semantics;• *SEM Best Paper Award.We cannot finish without saying that *SEM 2015 would not have been possible without the considerable efforts of our area chairs, their reviewers, and the computational semantics community in general.We hope you will enjoy *SEM 2015, Distributional semantic methods have some a priori appeal as models of human meaning acquisition, because they induce word representations from contextual distributions naturally occurring in corpus data without need for supervision. However, learning the meaning of a (concrete) word also involves establishing a link between the word and its typical visual referents, which is beyond the scope of classic, text-based distributional semantics. Since recently several proposals have been put forward about how to induce multimodal word representations from linguistic and visual contexts, it is natural to ask if this line of work, besides its practical implications, can help us to develop more realistic, grounded models of human word learning within the distributional semantics framework.In my talk, I will report about two studies in which we used multimodal distributional semantics (MDS) to simulate human word learning. In one study, we first measured the ability of subjects to link a nonce word to relevant linguistic and visual associates when prompted only by exposure to minimal corpus evidence about it. We then simulated the same task with an MDS model, finding its behavior remarkably similar to that of subjects. In the second study, we constructed a corpus in which child-directed speech is aligned with real-life pictures of the objects mentioned by care-givers. We then trained our MDS model on these data, and inspected the generaliza...

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Automated Encoding of Clinical Documents Based on Natural Language Processing

Cited by 406 publications

References 23 publications

Negation scope and spelling variation for text-mining of Danish electronic patient records

Negation scope and spelling variation for text-mining of Danish electronic patient records

SKIMMR: Facilitating knowledge discovery in life sciences by machine-aided skim reading

Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

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