scholarly journals Comparison of a semi-automatic annotation tool and a natural language processing application for the generation of clinical statement entries

2014 ◽  
Vol 22 (1) ◽  
pp. 132-142 ◽  
Author(s):  
Ching-Heng Lin ◽  
Nai-Yuan Wu ◽  
Wei-Shao Lai ◽  
Der-Ming Liou
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Automatic Annotation ◽  
Processing Application ◽  
Entry Level ◽  
Novel Approach ◽  
Clinical Document ◽  
F Measure ◽  
Document Architecture

Abstract Background and objective Electronic medical records with encoded entries should enhance the semantic interoperability of document exchange. However, it remains a challenge to encode the narrative concept and to transform the coded concepts into a standard entry-level document. This study aimed to use a novel approach for the generation of entry-level interoperable clinical documents. Methods Using HL7 clinical document architecture (CDA) as the example, we developed three pipelines to generate entry-level CDA documents. The first approach was a semi-automatic annotation pipeline (SAAP), the second was a natural language processing (NLP) pipeline, and the third merged the above two pipelines. We randomly selected 50 test documents from the i2b2 corpora to evaluate the performance of the three pipelines. Results The 50 randomly selected test documents contained 9365 words, including 588 Observation terms and 123 Procedure terms. For the Observation terms, the merged pipeline had a significantly higher F-measure than the NLP pipeline (0.89 vs 0.80, p<0.0001), but a similar F-measure to that of the SAAP (0.89 vs 0.87). For the Procedure terms, the F-measure was not significantly different among the three pipelines. Conclusions The combination of a semi-automatic annotation approach and the NLP application seems to be a solution for generating entry-level interoperable clinical documents.

scholarly journals Constituency Parser for Clinical Narratives using NLP

2020 ◽  
Vol 9 (3) ◽  
pp. 2277-2279
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Sentence Recall ◽  
Structured Report ◽  
Clinical Narrative ◽  
Medical Domain ◽  
Readable Format ◽  
F Measure

Clinical parsing is useful in medical domain .Clinical narratives are difficult to understand as it is in unstructured format .Medical Natural language processing systems are used to make these clinical narratives in readable format. Clinical Parser is the combination of natural language processing and medical lexicon .For making clinical narrative understandable parsing technique is used .In this paper we are discussing about constituency parser for clinical narratives, which is based on phrase structured grammar. This parser convert unstructured clinical narratives into structured report. This paper focus on clinical sentences which is in unstructured format after parsing convert into structured format. For each sentence recall ,precision and bracketing f- measure are calculated .

scholarly journals SentiMental: An emotional profiling algorithm for identifying affect patterns in text

2018 ◽  
Author(s):  
Massimo Stella
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Processing Algorithm ◽  
Copyright Holder ◽  
The Novel ◽  
Novel Approach ◽  
Technical Report ◽  
Potential Applications ◽  
Natural Language Processing Algorithm

This technical report outlines the mechanisms and potential applications of SentiMental, a suite of natural language processing algorithm designed and implemented by Massimo Stella, Complex Science Consulting. The following technical report briefly outlines the novel approach of SentiMental in performing sentiment and emotional analysis by directly harnessing the whole structure of the mental lexicon rather than by using affect norms. Furthermore, this technical report outlines the direct emotional profiling and the visualisations currently implemented in version 0.1 of SentiMental. Features under development and current limitations are also outlined and discussed.This technical report is not meant as a publication. The author holds full copyright and any reproduction of parts of this report must be authorised by the copyright holder. SentiMental represents a work in progress, so do not hesitate to get in touch with the author for any potential feedback.

A Composite Natural Language Processing and Information Retrieval Approach to Question Answering Using a Structured Knowledge Base

2017 ◽  
Vol 11 (03) ◽  
pp. 345-371
Author(s):  
Avani Chandurkar ◽  
Ajay Bansal
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Knowledge Base ◽  
Language Processing ◽  
Question Answering ◽  
Automated System ◽  
Free Form ◽  
Question Answering System ◽  
Novel Approach

With the inception of the World Wide Web, the amount of data present on the Internet is tremendous. This makes the task of navigating through this enormous amount of data quite difficult for the user. As users struggle to navigate through this wealth of information, the need for the development of an automated system that can extract the required information becomes urgent. This paper presents a Question Answering system to ease the process of information retrieval. Question Answering systems have been around for quite some time and are a sub-field of information retrieval and natural language processing. The task of any Question Answering system is to seek an answer to a free form factual question. The difficulty of pinpointing and verifying the precise answer makes question answering more challenging than simple information retrieval done by search engines. The research objective of this paper is to develop a novel approach to Question Answering based on a composition of conventional approaches of Information Retrieval (IR) and Natural Language processing (NLP). The focus is on using a structured and annotated knowledge base instead of an unstructured one. The knowledge base used here is DBpedia and the final system is evaluated on the Text REtrieval Conference (TREC) 2004 questions dataset.

NLP Applications as Cloud Based Services in Understanding Human Emotions

2020 ◽  
Vol 3 (11) ◽  
pp. 47-49
Author(s):  
Prasuryya Priyadarshan
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Emotional Expressions ◽  
Processing Application ◽  
Emotional Interaction ◽  
Unique Model ◽  
Human Emotions

This paper aims to throw light on the purpose of natural language processing applications and their future scope as cloud based services. It highlights certain features of cloud, along with a unique model of cloud hosted natural language processing application concept, to make the most out of emotional expressions in a person’s text or speech, which will prove effective to tackle diminishing human emotional interaction.

scholarly journals Tweet Classification Toward Twitter-Based Disease Surveillance: New Data, Methods, and Evaluations (Preprint)

2018 ◽  
Author(s):  
Shoko Wakamiya ◽  
Mizuki Morita ◽  
Yoshinobu Kano ◽  
Tomoko Ohkuma ◽  
Eiji Aramaki
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Disease Surveillance ◽  
Information Access ◽  
Related Information ◽  
Types Of Information ◽  
F Measure ◽  
Match Accuracy

BACKGROUND The amount of medical and clinical-related information on the Web is increasing. Among the different types of information available, social media–based data obtained directly from people are particularly valuable and are attracting significant attention. To encourage medical natural language processing (NLP) research exploiting social media data, the 13th NII Testbeds and Community for Information access Research (NTCIR-13) Medical natural language processing for Web document (MedWeb) provides pseudo-Twitter messages in a cross-language and multi-label corpus, covering 3 languages (Japanese, English, and Chinese) and annotated with 8 symptom labels (such as cold, fever, and flu). Then, participants classify each tweet into 1 of the 2 categories: those containing a patient’s symptom and those that do not. OBJECTIVE This study aimed to present the results of groups participating in a Japanese subtask, English subtask, and Chinese subtask along with discussions, to clarify the issues that need to be resolved in the field of medical NLP. METHODS In summary, 8 groups (19 systems) participated in the Japanese subtask, 4 groups (12 systems) participated in the English subtask, and 2 groups (6 systems) participated in the Chinese subtask. In total, 2 baseline systems were constructed for each subtask. The performance of the participant and baseline systems was assessed using the exact match accuracy, F-measure based on precision and recall, and Hamming loss. RESULTS The best system achieved exactly 0.880 match accuracy, 0.920 F-measure, and 0.019 Hamming loss. The averages of match accuracy, F-measure, and Hamming loss for the Japanese subtask were 0.720, 0.820, and 0.051; those for the English subtask were 0.770, 0.850, and 0.037; and those for the Chinese subtask were 0.810, 0.880, and 0.032, respectively. CONCLUSIONS This paper presented and discussed the performance of systems participating in the NTCIR-13 MedWeb task. As the MedWeb task settings can be formalized as the factualization of text, the achievement of this task could be directly applied to practical clinical applications.

scholarly journals Developing a natural language processing application for measuring the quality of colonoscopy procedures

2011 ◽  
Vol 18 (Supplement_1) ◽  
pp. i150-i156 ◽  
Author(s):  
Henk Harkema ◽  
Wendy W Chapman ◽  
Melissa Saul ◽  
Evan S Dellon ◽  
Robert E Schoen ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Processing Application

scholarly journals CAREER RECOMMENDER: A NOVEL APPROACH TO SUGGEST JOBS AND POST-GRADUATION STREAMS

2017 ◽  
Vol 10 (13) ◽  
pp. 365
Author(s):  
Prafful Nath Mathur ◽  
Abhishek Dixit ◽  
Sakkaravarthi Ramanathan
Keyword(s):  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Technical Skills ◽  
Web Crawler ◽  
Novel Approach ◽  
Current Location ◽  
Job Postings ◽  
Mining Algorithms

To implement a novel approach to recommend jobs and colleges based on résumé of freshly graduated students. Job postings are crawled from web using a web crawler and stored in a customized database. College lists are also retrieved for post-graduation streams and stored in a database. Student résumé is stored and parsed using natural language processing methods to form a résumé model. Text mining algorithms are applied on this model to extract useful information (i.e., degree, technical skills, extracurricular skills, current location, and hobbies). This information is used to suggest matching jobs and colleges to the candidate. 

scholarly journals Systematic community of Practice activities evaluation through Natural Language Processing: application to research projects

2019 ◽  
Vol 33 (02) ◽  
pp. 160-171
Author(s):  
Virginie Goepp ◽  
Nada Matta ◽  
Emmanuel Caillaud ◽  
Françoise Feugeas
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Community Of Practice ◽  
Language Processing ◽  
Speech Acts ◽  
Interaction Matrix ◽  
Efficiency Evaluation ◽  
Processing Application ◽  
Set Up ◽  
Definition Of

AbstractCommunity of Practice (CoP) efficiency evaluation is a great deal in research. Indeed, having the possibility to know if a given CoP is successful or not is essential to better manage it over time. The existing approaches for efficiency evaluation are difficult and time-consuming to put into action on real CoPs. They require either to evaluate subjective constructs making the analysis unreliable, either to work out a knowledge interaction matrix that is difficult to set up. However, these approaches build their evaluation on the fact that a CoP is successful if knowledge is exchanged between the members. It is the case if there are some interactions between the actors involved in the CoP. Therefore, we propose to analyze these interactions through the exchanges of emails thanks to Natural Language Processing. Our approach is systematic and semi-automated. It requires the e-mails exchanged and the definition of the speech-acts that will be retrieved. We apply it on a real project-based CoP: the SEPOLBE research project that involves different expertise fields. It allows us to identify the CoP core group and to emphasize learning processes between members with different backgrounds (Microbiology, Electrochemistry and Civil engineering).

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