scholarly journals Minimalistic Approach to Coreference Resolution in Lithuanian Medical Records

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Voldemaras Žitkus ◽  
Rita Butkienė ◽  
Rimantas Butleris ◽  
Rytis Maskeliūnas ◽  
Robertas Damaševičius ◽  
...  
Keyword(s):  
Language Processing ◽  
Medical Records ◽  
Healthcare Services ◽  
Endangered Languages ◽  
Coreference Resolution ◽  
Language Resources ◽  
Health Records ◽  
Linguistic Resources ◽  
Other Information ◽  
Rare And Endangered

Coreference resolution is a challenging part of natural language processing (NLP) with applications in machine translation, semantic search and other information retrieval, and decision support systems. Coreference resolution requires linguistic preprocessing and rich language resources for automatically identifying and resolving such expressions. Many rarer and under-resourced languages (such as Lithuanian) lack the required language resources and tools. We present a method for coreference resolution in Lithuanian language and its application for processing e-health records from a hospital reception. Our novelty is the ability to process coreferences with minimal linguistic resources, which is important in linguistic applications for rare and endangered languages. The experimental results show that coreference resolution is applicable to the development of NLP-powered online healthcare services in Lithuania.

scholarly journals Building natural language processing tools for Runyakitara

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Fridah Katushemererwe ◽  
Andrew Caines ◽  
Paula Buttery
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Learning ◽  
Language Processing ◽  
Primary Data ◽  
Computer Assisted ◽  
Endangered Languages ◽  
Test Case ◽  
Short Supply ◽  
Linguistic Resources

AbstractThis paper describes an endeavour to build natural language processing (NLP) tools for Runyakitara, a group of four closely related Bantu languages spoken in western Uganda. In contrast with major world languages such as English, for which corpora are comparatively abundant and NLP tools are well developed, computational linguistic resources for Runyakitara are in short supply. First therefore, we need to collect corpora for these languages, before we can proceed to the design of a spell-checker, grammar-checker and applications for computer-assisted language learning (CALL). We explain how we are collecting primary data for a new Runya Corpus of speech and writing, we outline the design of a morphological analyser, and discuss how we can use these new resources to build NLP tools. We are initially working with Runyankore–Rukiga, a closely-related pair of Runyakitara languages, and we frame our project in the context of NLP for low-resource languages, as well as CALL for the preservation of endangered languages. We put our project forward as a test case for the revitalization of endangered languages through education and technology.

scholarly journals Merging Data Diversity of Clinical Medical Records to Improve Effectiveness

2019 ◽  
Vol 16 (5) ◽  
pp. 769 ◽  
Author(s):  
Berit I. Helgheim ◽  
Rui Maia ◽  
Joao C. Ferreira ◽  
Ana Lucia Martins
Keyword(s):  
Language Processing ◽  
Medical Records ◽  
Healthcare Services ◽  
Data Sources ◽  
Digital Data ◽  
External Data ◽  
Knowledge Area ◽  
Effective Use ◽  
Technical Solutions

Medicine is a knowledge area continuously experiencing changes. Every day, discoveries and procedures are tested with the goal of providing improved service and quality of life to patients. With the evolution of computer science, multiple areas experienced an increase in productivity with the implementation of new technical solutions. Medicine is no exception. Providing healthcare services in the future will involve the storage and manipulation of large volumes of data (big data) from medical records, requiring the integration of different data sources, for a multitude of purposes, such as prediction, prevention, personalization, participation, and becoming digital. Data integration and data sharing will be essential to achieve these goals. Our work focuses on the development of a framework process for the integration of data from different sources to increase its usability potential. We integrated data from an internal hospital database, external data, and also structured data resulting from natural language processing (NPL) applied to electronic medical records. An extract-transform and load (ETL) process was used to merge different data sources into a single one, allowing more effective use of these data and, eventually, contributing to more efficient use of the available resources.

Use and Reuse of Electronic Health Records

2015 ◽  
pp. 212-226
Author(s):  
Michele Ceruti ◽  
Silvio Geninatti ◽  
Roberta Siliquini
Keyword(s):  
Medical Records ◽  
Healthcare Services ◽  
Single Individual ◽  
Health Issues ◽  
Validity And Reliability ◽  
Health Records ◽  
Related Information ◽  
Large Populations ◽  
Health Related ◽  
Electronic Health

Electronic Health Record (EHR) is a term with several meanings, even if its very definition allows distinguishing it from other electronic records of healthcare interest, such as Electronic Medical Records (EMR) and Personal Health Records (PHR). EMR is the electronic evolution of paper-based medical records, while PHR is mainly the collection of health-related information of a single individual. All of these have many points in common, but the interchangeable use of the terms leads to several misunderstandings and may threaten the validity and reliability of EHR applications. EHRs are more structured and conform to interoperability standards, and include a huge quantity of data of very large populations. Thus, they have proven to be useful for both theoretical and practical purposes, especially for Public Health issues. In this chapter, the authors argue that the appropriate use of EHR requires a realistic comprehensive concept of e-health by all the involved professions. They also show that a change in the “thinking” of e-health is necessary in order to achieve tangible results of improvement in healthcare services through the use of EHR.

scholarly journals Cancer Treatment Classification with Electronic Medical Health Records (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13981-13982
Author(s):  
Jiaming Zeng ◽  
Imon Banerjee ◽  
Michael Gensheimer ◽  
Daniel Rubin
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Cancer Treatment ◽  
Electronic Medical Records ◽  
Language Processing ◽  
Medical Records ◽  
Language Model ◽  
Health Records ◽  
Treatment Information ◽  
Medical Health

We built a natural language processing (NLP) language model that can be used to extract cancer treatment information using structured and unstructured electronic medical records (EMR). Our work appears to be the first that combines EMR and NLP for treatment identification.

scholarly journals NORMALIZATION OF HEALTH RECORDS IN THE SERBIAN LANGUAGE WITH THE AIM OF SMART HEALTH SERVICES REALIZATION

2020 ◽  
pp. 825
Author(s):  
Aldina R. Avdić ◽  
Ulfeta A. Marovac ◽  
Dragan S. Janković
Keyword(s):  
Medical Records ◽  
Smart Cities ◽  
Healthcare Services ◽  
Health Records ◽  
Smart Healthcare ◽  
Textual Data ◽  
Efficient Data ◽  
Electronic Recording ◽  
Smart Health ◽  
Textual Form

The development of information technology increases its use in various spheres of human activity, including healthcare. Bundles of data and reports are generated and stored in textual form, such as symptoms, medical history, and doctor’s observations of patients' health. Electronic recording of patient data not only facilitates day-to-day work in hospitals, enables more efficient data management and reduces material costs, but can also be used for further processing and to gain knowledge to improve public health. Publicly available health data would contribute to the development of telemedicine, e-health, epidemic control, and smart healthcare within smart cities. This paper describes the importance of textual data normalization for smart healthcare services. An algorithm for normalizing medical data in Serbian is proposed in order to prepare them for further processing (F1-score=0,816), in this case within the smart health framework. By applying this algorithm, in addition to the normalized medical records, corpora of keywords and stop words, which are specific to the medical domain, are also obtained and can be used to improve the results in the normalization of medical textual data. 

Use and Reuse of Electronic Health Records

2016 ◽  
pp. 961-975 ◽  
Author(s):  
Michele Ceruti ◽  
Silvio Geninatti ◽  
Roberta Siliquini
Keyword(s):  
Medical Records ◽  
Healthcare Services ◽  
Single Individual ◽  
Health Issues ◽  
Validity And Reliability ◽  
Health Records ◽  
Related Information ◽  
Large Populations ◽  
Health Related ◽  
Electronic Health

Electronic Health Record (EHR) is a term with several meanings, even if its very definition allows distinguishing it from other electronic records of healthcare interest, such as Electronic Medical Records (EMR) and Personal Health Records (PHR). EMR is the electronic evolution of paper-based medical records, while PHR is mainly the collection of health-related information of a single individual. All of these have many points in common, but the interchangeable use of the terms leads to several misunderstandings and may threaten the validity and reliability of EHR applications. EHRs are more structured and conform to interoperability standards, and include a huge quantity of data of very large populations. Thus, they have proven to be useful for both theoretical and practical purposes, especially for Public Health issues. In this chapter, the authors argue that the appropriate use of EHR requires a realistic comprehensive concept of e-health by all the involved professions. They also show that a change in the “thinking” of e-health is necessary in order to achieve tangible results of improvement in healthcare services through the use of EHR.

scholarly journals Factors Associated in the Implementation and Adoption of Electronic Health Records (EHR) in Private Healthcare

2018 ◽  
Vol 7 (3.7) ◽  
pp. 257
Author(s):  
Noor Syahirah Mohamad Mobin ◽  
Saiful Farik Mat Yatin ◽  
Mohd Razilan Abdul Kadir ◽  
Siti Noraini Mohd Tobi ◽  
Nur Atiqaf Mahathir ◽  
...  
Keyword(s):  
Medical Records ◽  
Health Informatics ◽  
Electronic System ◽  
Healthcare Services ◽  
Substantial Evidence ◽  
Patient History ◽  
Health Records ◽  
Factors Associated ◽  
Integrated Method ◽  
Care Giving

The health industry is undergoing a fast transition from its conventional method of care-giving. E-health or Health Informatics is an ICT-integrated method adopted by the hospitals for providing healthcare services to the patients anytime, anywhere without any restriction of location or facility. Many countries now follow suit to improve efficiency and accuracy in their healthcare systems. Nowadays, many countries including Malaysia still face challenges in the implementation of the healthcare electronic system. Substantial evidence suggests that paper medical records do not provide reliable and updated information on patients. Health physicians provide medical services based on patient history. In cases where this information is inaccurate and/or inaccessible, chances of medical errors due to improper prescriptions remain high.  

Impairment Tutorial: Spinal Cord Stimulation and Intraspinal Opioids:Impairment Rating Issues

2000 ◽  
Vol 5 (5) ◽  
pp. 4-5
Author(s):  
James B. Talmage ◽  
Leon H. Ensalada
Keyword(s):  
Medical Records ◽  
Evaluation Process ◽  
Current Approach ◽  
Satisfaction Survey ◽  
Start Time ◽  
Medical Evaluation ◽  
Independent Medical Evaluation ◽  
Other Information ◽  
Physician Reviews ◽  
Written Informed Consent

Abstract Evaluators must understand the complex overall process that makes up an independent medical evaluation (IME), whether the purpose of the evaluation is to assess impairment or other care issues. Part 1 of this article provides an overview of the process, and Part 2 [in this issue] reviews the pre-evaluation process in detail. The IME process comprises three phases: pre-evaluation, evaluation, and postevaluation. Pre-evaluation begins when a client requests an IME and provides the physician with medical records and other information. The following steps occur at the time of an evaluation: 1) patient is greeted; arrival time is noted; 2) identity of the examinee is verified; 3) the evaluation process is explained and written informed consent is obtained; 4) questions or inventories are completed; 5) physician reviews radiographs or diagnostic studies; 6) physician records start time and interviews examinee; 7) physician may dictate the history in the presence of the examinee; 8) physician examines examinee with staff member in attendance, documenting negative, physical, and nonphysiologic findings; 9) physician concludes evaluation, records end time, and provides a satisfaction survey to examinee; 10) examinee returns satisfaction survey before departure. Postevaluation work includes preparing the IME report, which is best done immediately after the evaluation. To perfect the IME process, examiners can assess their current approach to IMEs, identify strengths and weaknesses, and consider what can be done to improve efficiency and quality.

Similarity detection of English text and teaching evaluation based on improved TCUSS clustering algorithm

2020 ◽  
pp. 1-11
Author(s):  
Yu Wang
Keyword(s):  
Language Processing ◽  
Calculation Method ◽  
Clustering Algorithm ◽  
Research Result ◽  
Structural Features ◽  
English Text ◽  
Support Vector ◽  
Similarity Calculation ◽  
Other Information ◽  
Calculation Task

The semantic similarity calculation task of English text has important influence on other fields of natural language processing and has high research value and application prospect. At present, research on the similarity calculation of short texts has achieved good results, but the research result on long text sets is still poor. This paper proposes a similarity calculation method that combines planar features with structured features and uses support vector regression models. Moreover, this paper uses PST and PDT to represent the syntax, semantics and other information of the text. In addition, through the two structural features suitable for text similarity calculation, this paper proposes a similarity calculation method combining structural features with Tree-LSTM model. Experiments show that this method provides a new idea for interest network extraction.

scholarly journals Collecting specialty-related medical terms: Development and evaluation of a resource for Spanish

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pilar López-Úbeda ◽  
Alexandra Pomares-Quimbaya ◽  
Manuel Carlos Díaz-Galiano ◽  
Stefan Schulz
Keyword(s):  
Language Processing ◽  
Classification Problem ◽  
Snomed Ct ◽  
Language Resources ◽  
Clinical Specialty ◽  
Controlled Vocabularies ◽  
Clinical Text ◽  
Domain Specific ◽  
Medical Terms ◽  
Core Vocabulary

Abstract Background Controlled vocabularies are fundamental resources for information extraction from clinical texts using natural language processing (NLP). Standard language resources available in the healthcare domain such as the UMLS metathesaurus or SNOMED CT are widely used for this purpose, but with limitations such as lexical ambiguity of clinical terms. However, most of them are unambiguous within text limited to a given clinical specialty. This is one rationale besides others to classify clinical text by the clinical specialty to which they belong. Results This paper addresses this limitation by proposing and applying a method that automatically extracts Spanish medical terms classified and weighted per sub-domain, using Spanish MEDLINE titles and abstracts as input. The hypothesis is biomedical NLP tasks benefit from collections of domain terms that are specific to clinical subdomains. We use PubMed queries that generate sub-domain specific corpora from Spanish titles and abstracts, from which token n-grams are collected and metrics of relevance, discriminatory power, and broadness per sub-domain are computed. The generated term set, called Spanish core vocabulary about clinical specialties (SCOVACLIS), was made available to the scientific community and used in a text classification problem obtaining improvements of 6 percentage points in the F-measure compared to the baseline using Multilayer Perceptron, thus demonstrating the hypothesis that a specialized term set improves NLP tasks. Conclusion The creation and validation of SCOVACLIS support the hypothesis that specific term sets reduce the level of ambiguity when compared to a specialty-independent and broad-scope vocabulary.

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