scholarly journals Extraction of Temporal Structures for Clinical Events in Unlabeled Free-Text Electronic Health Records in Russian

2021 ◽  
Author(s):  
Anastasia A. Funkner ◽  
Dmitrii A. Zhurman ◽  
Sergey V. Kovalchuk
Keyword(s):  
Electronic Health Records ◽  
English Language ◽  
Hybrid Approach ◽  
Event Extraction ◽  
Free Form ◽  
Free Text ◽  
Clinical Events ◽  
Temporal Expressions ◽  
Temporal Structures ◽  
Syntactical Analysis

The important information about a patient is often stored in a free-form text to describe the events in the patient’s medical history. In this work, we propose and evaluate a hybrid approach based on rules and syntactical analysis to normalise temporal expressions and assess uncertainty depending on the remoteness of the event. A dataset of 500 sentences was manually labelled to measure the accuracy. On this dataset, the accuracy of extracting temporal expressions is 95,5%, and the accuracy of normalization is 94%. The event extraction accuracy is 74.80%. The essential advantage of this work is the implementation of the considered approach for the non-English language where NLP tools are limited.

scholarly journals De-identifying Spanish medical texts - named entity recognition applied to radiology reports

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Irene Pérez-Díez ◽  
Raúl Pérez-Moraga ◽  
Adolfo López-Cerdán ◽  
Jose-Maria Salinas-Serrano ◽  
María de la Iglesia-Vayá
Keyword(s):  
Electronic Health Records ◽  
English Language ◽  
Personal Information ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Medical Texts ◽  
Health Records ◽  
Named Entity ◽  
Radiology Reports ◽  
Electronic Health

Abstract Background Medical texts such as radiology reports or electronic health records are a powerful source of data for researchers. Anonymization methods must be developed to de-identify documents containing personal information from both patients and medical staff. Although currently there are several anonymization strategies for the English language, they are also language-dependent. Here, we introduce a named entity recognition strategy for Spanish medical texts, translatable to other languages. Results We tested 4 neural networks on our radiology reports dataset, achieving a recall of 97.18% of the identifying entities. Alongside, we developed a randomization algorithm to substitute the detected entities with new ones from the same category, making it virtually impossible to differentiate real data from synthetic data. The three best architectures were tested with the MEDDOCAN challenge dataset of electronic health records as an external test, achieving a recall of 69.18%. Conclusions The strategy proposed, combining named entity recognition tasks with randomization of entities, is suitable for Spanish radiology reports. It does not require a big training corpus, thus it could be easily extended to other languages and medical texts, such as electronic health records.

scholarly journals Structured Two-Stream Attention Network for Video Question Answering

2019 ◽  
Vol 33 ◽  
pp. 6391-6398 ◽  
Author(s):  
Lianli Gao ◽  
Pengpeng Zeng ◽  
Jingkuan Song ◽  
Yuan-Fang Li ◽  
Wu Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Question Answering ◽  
Free Form ◽  
Image Region ◽  
Attention Network ◽  
Natural Language Question ◽  
Temporal Structures ◽  
Text Features ◽  
Video Input ◽  
Language Question

To date, visual question answering (VQA) (i.e., image QA and video QA) is still a holy grail in vision and language understanding, especially for video QA. Compared with image QA that focuses primarily on understanding the associations between image region-level details and corresponding questions, video QA requires a model to jointly reason across both spatial and long-range temporal structures of a video as well as text to provide an accurate answer. In this paper, we specifically tackle the problem of video QA by proposing a Structured Two-stream Attention network, namely STA, to answer a free-form or open-ended natural language question about the content of a given video. First, we infer rich longrange temporal structures in videos using our structured segment component and encode text features. Then, our structured two-stream attention component simultaneously localizes important visual instance, reduces the influence of background video and focuses on the relevant text. Finally, the structured two-stream fusion component incorporates different segments of query and video aware context representation and infers the answers. Experiments on the large-scale video QA dataset TGIF-QA show that our proposed method significantly surpasses the best counterpart (i.e., with one representation for the video input) by 13.0%, 13.5%, 11.0% and 0.3 for Action, Trans., TrameQA and Count tasks. It also outperforms the best competitor (i.e., with two representations) on the Action, Trans., TrameQA tasks by 4.1%, 4.7%, and 5.1%.

scholarly journals Graph node rank based important keyword detection from Twitter

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mukesh Kumar ◽  
Palak Rehan
Keyword(s):  
Real Time ◽  
English Language ◽  
Hybrid Approach ◽  
Levenshtein Distance ◽  
Social Media Networks ◽  
The Social ◽  
Keyword Detection ◽  
Product Launches ◽  
Process Detection ◽  
Good Resource

Social media networks like Twitter, Facebook, WhatsApp etc. are most commonly used medium for sharing news, opinions and to stay in touch with peers. Messages on twitter are limited to 140 characters. This led users to create their own novel syntax in tweets to express more in lesser words. Free writing style, use of URLs, markup syntax, inappropriate punctuations, ungrammatical structures, abbreviations etc. makes it harder to mine useful information from them. For each tweet, we can get an explicit time stamp, the name of the user, the social network the user belongs to, or even the GPS coordinates if the tweet is created with a GPS-enabled mobile device. With these features, Twitter is, in nature, a good resource for detecting and analyzing the real time events happening around the world. By using the speed and coverage of Twitter, we can detect events, a sequence of important keywords being talked, in a timely manner which can be used in different applications like natural calamity relief support, earthquake relief support, product launches, suspicious activity detection etc. The keyword detection process from Twitter can be seen as a two step process: detection of keyword in the raw text form (words as posted by the users) and keyword normalization process (reforming the users’ unstructured words in the complete meaningful English language words). In this paper a keyword detection technique based upon the graph, spanning tree and Page Rank algorithm is proposed. A text normalization technique based upon hybrid approach using Levenshtein distance, demetaphone algorithm and dictionary mapping is proposed to work upon the unstructured keywords as produced by the proposed keyword detector. The proposed normalization technique is validated using the standard lexnorm 1.2 dataset. The proposed system is used to detect the keywords from Twiter text being posted at real time. The detected and normalized keywords are further validated from the search engine results at later time for detection of events.

A Framework for Systematic Assessment of Clinical Trial Population Representativeness Using Electronic Health Records Data

2021 ◽  
Vol 12 (04) ◽  
pp. 816-825
Author(s):  
Yingcheng Sun ◽  
Alex Butler ◽  
Ibrahim Diallo ◽  
Jae Hyun Kim ◽  
Casey Ta ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Electronic Health Records ◽  
The United States ◽  
Design Stage ◽  
Common Data Model ◽  
Free Text ◽  
Eligibility Criteria ◽  
Health Records ◽  
Electronic Health

Abstract Background Clinical trials are the gold standard for generating robust medical evidence, but clinical trial results often raise generalizability concerns, which can be attributed to the lack of population representativeness. The electronic health records (EHRs) data are useful for estimating the population representativeness of clinical trial study population. Objectives This research aims to estimate the population representativeness of clinical trials systematically using EHR data during the early design stage. Methods We present an end-to-end analytical framework for transforming free-text clinical trial eligibility criteria into executable database queries conformant with the Observational Medical Outcomes Partnership Common Data Model and for systematically quantifying the population representativeness for each clinical trial. Results We calculated the population representativeness of 782 novel coronavirus disease 2019 (COVID-19) trials and 3,827 type 2 diabetes mellitus (T2DM) trials in the United States respectively using this framework. With the use of overly restrictive eligibility criteria, 85.7% of the COVID-19 trials and 30.1% of T2DM trials had poor population representativeness. Conclusion This research demonstrates the potential of using the EHR data to assess the clinical trials population representativeness, providing data-driven metrics to inform the selection and optimization of eligibility criteria.

Method for automatic key concepts extraction

2019 ◽  
Vol 37 (1) ◽  
pp. 2-15 ◽  
Author(s):  
Sudarsana Desul ◽  
Madurai Meenachi N. ◽  
Thejas Venkatesh ◽  
Vijitha Gunta ◽  
Gowtham R. ◽  
...  
Keyword(s):  
Coming Out ◽  
English Language ◽  
Hybrid Approach ◽  
Ontology Learning ◽  
Specific Domain ◽  
Content Type ◽  
Concept Extraction ◽  
Nuclear Domain ◽  
Wide Range ◽  
Key Concepts

PurposeOntology of a domain mainly consists of a set of concepts and their semantic relations. It is typically constructed and maintained by using ontology editors with substantial human intervention. It is desirable to perform the task automatically, which has led to the development of ontology learning techniques. One of the main challenges of ontology learning from the text is to identify key concepts from the documents. A wide range of techniques for key concept extraction have been proposed but are having the limitations of low accuracy, poor performance, not so flexible and applicability to a specific domain. The propose of this study is to explore a new method to extract key concepts and to apply them to literature in the nuclear domain.Design/methodology/approachIn this article, a novel method for key concept extraction is proposed and applied to the documents from the nuclear domain. A hybrid approach was used, which includes a combination of domain, syntactic name entity knowledge and statistical based methods. The performance of the developed method has been evaluated from the data obtained using two out of three voting logic from three domain experts by using 120 documents retrieved from SCOPUS database.FindingsThe work reported pertains to extracting concepts from the set of selected documents and aids the search for documents relating to given concepts. The results of a case study indicated that the method developed has demonstrated better metrics than Text2Onto and CFinder. The method described has the capability of extracting valid key concepts from a set of candidates with long phrases.Research limitations/implicationsThe present study is restricted to literature coming out in the English language and applied to the documents from nuclear domain. It has the potential to extend to other domains also.Practical implicationsThe work carried out in the current study has the potential of leading to updating International Nuclear Information System thesaurus for ontology in the nuclear domain. This can lead to efficient search methods.Originality/valueThis work is the first attempt to automatically extract key concepts from the nuclear documents. The proposed approach will address and fix the most of the problems that are existed in the current methods and thereby increase the performance.

scholarly journals Researchers’ perceptions of malaria eradication: findings from a mixed-methods analysis of a large online survey

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Joe Brew ◽  
Menno Pradhan ◽  
Jacqueline Broerse ◽  
Quique Bassat
Keyword(s):  
Online Survey ◽  
Cost Benefit ◽  
Time Frame ◽  
Free Form ◽  
Free Text ◽  
Specific Investments ◽  
Uncertain Outcome ◽  
Malaria Eradication ◽  
Global Eradication

Abstract Background The value of malaria eradication, the permanent reduction to zero of the worldwide incidence of malaria infection caused by human malaria parasites, would be enormous. However, the expected value of an investment in an intended, but uncertain, outcome hinges on the probability of, and time until, its fulfilment. Though the long-term benefits of global malaria eradication promise to be large, the upfront costs and uncertainty regarding feasibility and timeframe make it difficult for policymakers and researchers to forecast the return on investment. Methods A large online survey of 844 peer-reviewed malaria researchers of different scientific backgrounds administered in order to estimate the probability and time frame of eradication. Adjustments were made for potential selection bias, and thematic analysis of free text comments was carried out. Results The average perceived likelihood of global eradication among malaria researchers approximates the number of years into the future: approximately 10% of researchers believe that eradication will occur in the next 10 years, 30% believe it will occur in the next 30 years, and half believe eradication will require 50 years or more. Researchers who gave free form comments highlighted systemic challenges and the need for innovation as chief among obstacles to achieving global malaria eradication. Conclusions The findings highlight the difficulty and complexity of malaria eradication, and can be used in prospective cost–benefit analyses to inform stakeholders regarding the likely return on eradication-specific investments.

scholarly journals Validity of acute cardiovascular outcome diagnoses in European electronic health records: a systematic review protocol

BMJ Open ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. e031373 ◽  
Author(s):  
Jennifer Anne Davidson ◽  
Amitava Banerjee ◽  
Rutendo Muzambi ◽  
Liam Smeeth ◽  
Charlotte Warren-Gash
Keyword(s):  
Systematic Review ◽  
Electronic Health Records ◽  
Predictive Value ◽  
Grey Literature ◽  
Cochrane Library ◽  
Free Text ◽  
Health Records ◽  
Coronary Syndrome ◽  
Validation Measure ◽  
Electronic Health

IntroductionCardiovascular diseases (CVDs) are among the leading causes of death globally. Electronic health records (EHRs) provide a rich data source for research on CVD risk factors, treatments and outcomes. Researchers must be confident in the validity of diagnoses in EHRs, particularly when diagnosis definitions and use of EHRs change over time. Our systematic review provides an up-to-date appraisal of the validity of stroke, acute coronary syndrome (ACS) and heart failure (HF) diagnoses in European primary and secondary care EHRs.Methods and analysisWe will systematically review the published and grey literature to identify studies validating diagnoses of stroke, ACS and HF in European EHRs. MEDLINE, EMBASE, SCOPUS, Web of Science, Cochrane Library, OpenGrey and EThOS will be searched from the dates of inception to April 2019. A prespecified search strategy of subject headings and free-text terms in the title and abstract will be used. Two reviewers will independently screen titles and abstracts to identify eligible studies, followed by full-text review. We require studies to compare clinical codes with a suitable reference standard. Additionally, at least one validation measure (sensitivity, specificity, positive predictive value or negative predictive value) or raw data, for the calculation of a validation measure, is necessary. We will then extract data from the eligible studies using standardised tables and assess risk of bias in individual studies using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Data will be synthesised into a narrative format and heterogeneity assessed. Meta-analysis will be considered when a sufficient number of homogeneous studies are available. The overall quality of evidence will be assessed using the Grading of Recommendations, Assessment, Development and Evaluation tool.Ethics and disseminationThis is a systematic review, so it does not require ethical approval. Our results will be submitted for peer-review publication.PROSPERO registration numberCRD42019123898

scholarly journals De-identifying Free Text of Japanese Dummy Electronic Health Records

2018 ◽  
Author(s):  
Kohei Kajiyama ◽  
Hiromasa Horiguchi ◽  
Takashi Okumura ◽  
Mizuki Morita ◽  
Yoshinobu Kano
Keyword(s):  
Electronic Health Records ◽  
Free Text ◽  
Health Records ◽  
Electronic Health

Abstract MP21: Feasibility of Electronic Health Records-based community surveillance of cardiovascular disease: Findings from the Atherosclerosis Risk in Communities Study.

Circulation ◽  
2018 ◽  
Vol 137 (suppl_1) ◽  
Author(s):  
Brittany M Bogle ◽  
Wayne D Rosamond ◽  
Aaron R Folsom ◽  
Paul Sorlie ◽  
Elsayed Z Soliman ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Electronic Health Records ◽  
Cardiac Biomarkers ◽  
Free Text ◽  
Health Records ◽  
Efficient System ◽  
Atherosclerosis Risk In Communities ◽  
Atherosclerosis Risk ◽  
Electronic Health ◽  
Aric Study

Background: Accurate community surveillance of cardiovascular disease requires hospital record abstraction, which is typically a manual process. The costly and time-intensive nature of manual abstraction precludes its use on a regional or national scale in the US. Whether an efficient system can accurately reproduce traditional community surveillance methods by processing electronic health records (EHRs) has not been established. Objective: We sought to develop and test an EHR-based system to reproduce abstraction and classification procedures for acute myocardial infarction (MI) as defined by the Atherosclerosis Risk in Communities (ARIC) Study. Methods: Records from hospitalizations in 2014 within ARIC community surveillance areas were sampled using a broad set of ICD discharge codes likely to harbor MI. These records were manually abstracted by ARIC study personnel and used to classify MI according to ARIC protocols. We requested EHRs in a unified data structure for the same hospitalizations at 6 hospitals and built programs to convert free text and structured data into the ARIC criteria elements necessary for MI classification. Per ARIC protocol, MI was classified based on cardiac biomarkers, cardiac pain, and Minnesota-coded electrocardiogram abnormalities. We compared MI classified from manually abstracted data to (1) EHR-based classification and (2) final ICD-9 coded discharge diagnoses (410-414). Results: These preliminary results are based on hospitalizations from 1 hospital. Of 684 hospitalizations, 355 qualified for full manual abstraction; 83 (23%) of these were classified as definite MI and 78 (22%) as probable MI. Our EHR-based abstraction is sensitive (>75%) and highly specific (>83%) in classifying ARIC-defined definite MI and definite or probable MI (Table). Conclusions: Our results support the potential of a process to extract comprehensive sets of data elements from EHR from different hospitals, with completeness and accuracy sufficient for a standardized definition of hospitalized MI.

scholarly journals Incorporating natural language processing to improve classification of axial spondyloarthritis using electronic health records

Rheumatology ◽  
2019 ◽  
Vol 59 (5) ◽  
pp. 1059-1065 ◽  
Author(s):  
Sizheng Steven Zhao ◽  
Chuan Hong ◽  
Tianrun Cai ◽  
Chang Xu ◽  
Jie Huang ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Predictive Value ◽  
Area Under The Curve ◽  
Free Text ◽  
Text Data ◽  
Health Records ◽  
Disease Concepts ◽  
Icd Codes ◽  
Electronic Health

Abstract Objectives To develop classification algorithms that accurately identify axial SpA (axSpA) patients in electronic health records, and compare the performance of algorithms incorporating free-text data against approaches using only International Classification of Diseases (ICD) codes. Methods An enriched cohort of 7853 eligible patients was created from electronic health records of two large hospitals using automated searches (⩾1 ICD codes combined with simple text searches). Key disease concepts from free-text data were extracted using NLP and combined with ICD codes to develop algorithms. We created both supervised regression-based algorithms—on a training set of 127 axSpA cases and 423 non-cases—and unsupervised algorithms to identify patients with high probability of having axSpA from the enriched cohort. Their performance was compared against classifications using ICD codes only. Results NLP extracted four disease concepts of high predictive value: ankylosing spondylitis, sacroiliitis, HLA-B27 and spondylitis. The unsupervised algorithm, incorporating both the NLP concept and ICD code for AS, identified the greatest number of patients. By setting the probability threshold to attain 80% positive predictive value, it identified 1509 axSpA patients (mean age 53 years, 71% male). Sensitivity was 0.78, specificity 0.94 and area under the curve 0.93. The two supervised algorithms performed similarly but identified fewer patients. All three outperformed traditional approaches using ICD codes alone (area under the curve 0.80–0.87). Conclusion Algorithms incorporating free-text data can accurately identify axSpA patients in electronic health records. Large cohorts identified using these novel methods offer exciting opportunities for future clinical research.

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