scholarly journals An Integrated Approach to Biomedical Term Identification Systems

2020 ◽  
Vol 10 (5) ◽  
pp. 1726 ◽  
Author(s):  
Pilar López-Úbeda ◽  
Manuel Carlos Díaz-Galiano ◽  
Arturo Montejo-Ráez ◽  
María-Teresa Martín-Valdivia ◽  
L. Alfonso Ureña-López
Keyword(s):  
Language Processing ◽  
Integrated Approach ◽  
Knowledge Bases ◽  
Sources Of Information ◽  
Medical Texts ◽  
Medical Term ◽  
Conceptual Graph ◽  
Medical Concepts ◽  
Fine Tune ◽  
Term Identification

In this paper a novel architecture to build biomedical term identification systems is presented. The architecture combines several sources of information and knowledge bases to provide practical and exploration-enabled biomedical term identification systems. We have implemented a system to evidence the convenience of the different modules considered in the architecture. Our system includes medical term identification, retrieval of specialized literature and semantic concept browsing from medical ontologies. By applying several Natural Language Processing (NLP) technologies, we have developed a prototype that offers an easy interface for helping to understand biomedical specialized terminology present in Spanish medical texts. The result is a system that performs term identification of medical concepts over any textual document written in Spanish. It is possible to perform a sub-concept selection using the previously identified terms to accomplish a fine-tune retrieval process over resources like SciELO, Google Scholar and MedLine. Moreover, the system generates a conceptual graph which semantically relates all the terms found in the text. In order to evaluate our proposal on medical term identification, we present the results obtained by our system using the MANTRA corpus and compare its performance with the Freeling-Med tool.

GRAPHICAL INDUCTION OF QUALIFIED MEDICAL KNOWLEDGE

2013 ◽  
Vol 07 (04) ◽  
pp. 377-405 ◽  
Author(s):  
TRAVIS GOODWIN ◽  
SANDA M. HARABAGIU
Keyword(s):  
Language Processing ◽  
Clinical Data ◽  
Medical Knowledge ◽  
Knowledge Bases ◽  
Clinical Knowledge ◽  
Biomedical Knowledge ◽  
Clinical Text ◽  
Relational Information ◽  
Processing Techniques ◽  
Medical Concepts

The introduction of electronic medical records (EMRs) enabled the access of unprecedented volumes of clinical data, both in structured and unstructured formats. A significant amount of this clinical data is expressed within the narrative portion of the EMRs, requiring natural language processing techniques to unlock the medical knowledge referred to by physicians. This knowledge, derived from the practice of medical care, complements medical knowledge already encoded in various structured biomedical ontologies. Moreover, the clinical knowledge derived from EMRs also exhibits relational information between medical concepts, derived from the cohesion property of clinical text, which is an attractive attribute that is currently missing from the vast biomedical knowledge bases. In this paper, we describe an automatic method of generating a graph of clinically related medical concepts by considering the belief values associated with those concepts. The belief value is an expression of the clinician's assertion that the concept is qualified as present, absent, suggested, hypothetical, ongoing, etc. Because the method detailed in this paper takes into account the hedging used by physicians when authoring EMRs, the resulting graph encodes qualified medical knowledge wherein each medical concept has an associated assertion (or belief value) and such qualified medical concepts are spanned by relations of different strengths, derived from the clinical contexts in which concepts are used. In this paper, we discuss the construction of a qualified medical knowledge graph (QMKG) and treat it as a BigData problem addressed by using MapReduce for deriving the weighted edges of the graph. To be able to assess the value of the QMKG, we demonstrate its usage for retrieving patient cohorts by enabling query expansion that produces greatly enhanced results against state-of-the-art methods.

Representation of Medical Concepts of the Thyroid Gland by Physicians in Anatomy and Pathology

1994 ◽  
Vol 33 (04) ◽  
pp. 382-389 ◽  
Author(s):  
C. Bastien ◽  
M. Roux ◽  
L. Pellegrin
Keyword(s):  
Thyroid Gland ◽  
Language Processing ◽  
Expert Knowledge ◽  
Medical Knowledge ◽  
Hierarchical Organization ◽  
Anatomic Pathology ◽  
Medical Computer ◽  
Conceptual Graph ◽  
Medical Reports ◽  
Medical Concepts

Abstract:An experimental study in cognitive psychology is described, concerning the categorization of medical concepts into specific classes, expressed by physicians specialized in anatomic pathology consultations of the thyroid gland. This study belongs to a medical computer science project, called ARISTOTLE, concerning Natural Language Processing of specialized medical reports in anatomic pathology of the thyroid gland. This research has been done for two reasons: first, to specify the characteristics of human expert categorization in an area of medical knowledge and, secondly, to validate the hierarchical organization of a prototype declarative knowledge base. In this experiment, physicians were asked to categorize 121 concepts into 10 proposed classes. These classes and concepts belong to expert knowledge represented in a conceptual graph that was constructed before the experiment. Results show variable semantic distances between concepts of a same class, and dynamic variations of these distances due to contextual representation.

scholarly journals Interpretable segmentation of medical free-text records based on word embeddings

2021 ◽  
Author(s):  
Adam Gabriel Dobrakowski ◽  
Agnieszka Mykowiecka ◽  
Małgorzata Marciniak ◽  
Wojciech Jaworski ◽  
Przemysław Biecek
Keyword(s):  
Language Processing ◽  
Visual Exploration ◽  
Free Text ◽  
Medical Doctors ◽  
Medical Terminology ◽  
Medical Texts ◽  
Medical Diagnoses ◽  
Clinical Records ◽  
Vector Representations ◽  
Medical Concepts

AbstractMedical free-text records store a lot of useful information that can be exploited in developing computer-supported medicine. However, extracting the knowledge from the unstructured text is difficult and depends on the language. In the paper, we apply Natural Language Processing methods to process raw medical texts in Polish and propose a new methodology for clustering of patients’ visits. We (1) extract medical terminology from a corpus of free-text clinical records, (2) annotate data with medical concepts, (3) compute vector representations of medical concepts and validate them on the proposed term analogy tasks, (4) compute visit representations as vectors, (5) introduce a new method for clustering of patients’ visits and (6) apply the method to a corpus of 100,000 visits. We use several approaches to visual exploration that facilitate interpretation of segments. With our method, we obtain stable and separated segments of visits which are positively validated against final medical diagnoses. In this paper we show how algorithm for segmentation of medical free-text records may be used to aid medical doctors. In addition to this, we share implementation of described methods with examples as open-source package .

scholarly journals Efficient Embedded Decoding of Neural Network Language Models in a Machine Translation System

2018 ◽  
Vol 28 (09) ◽  
pp. 1850007
Author(s):  
Francisco Zamora-Martinez ◽  
Maria Jose Castro-Bleda
Keyword(s):  
Neural Network ◽  
Machine Translation ◽  
Language Processing ◽  
Traditional Approach ◽  
Computational Cost ◽  
Integrated Approach ◽  
Language Models ◽  
Translation System ◽  
Neural Net ◽  
Network Language

Neural Network Language Models (NNLMs) are a successful approach to Natural Language Processing tasks, such as Machine Translation. We introduce in this work a Statistical Machine Translation (SMT) system which fully integrates NNLMs in the decoding stage, breaking the traditional approach based on [Formula: see text]-best list rescoring. The neural net models (both language models (LMs) and translation models) are fully coupled in the decoding stage, allowing to more strongly influence the translation quality. Computational issues were solved by using a novel idea based on memorization and smoothing of the softmax constants to avoid their computation, which introduces a trade-off between LM quality and computational cost. These ideas were studied in a machine translation task with different combinations of neural networks used both as translation models and as target LMs, comparing phrase-based and [Formula: see text]-gram-based systems, showing that the integrated approach seems more promising for [Formula: see text]-gram-based systems, even with nonfull-quality NNLMs.

Probing Patient Messages Enhanced by Natural Language Processing: A Top-Down Message Corpus Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
George Mastorakos ◽  
Aditya Khurana ◽  
Ming Huang ◽  
Sunyang Fu ◽  
Ahmad P. Tafti ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Corpus Analysis ◽  
Entity Recognition ◽  
Message Content ◽  
Named Entity ◽  
Medical Concepts ◽  
Insight Into

Background. Patients increasingly use asynchronous communication platforms to converse with care teams. Natural language processing (NLP) to classify content and automate triage of these messages has great potential to enhance clinical efficiency. We characterize the contents of a corpus of portal messages generated by patients using NLP methods. We aim to demonstrate descriptive analyses of patient text that can contribute to the development of future sophisticated NLP applications. Methods. We collected approximately 3,000 portal messages from the cardiology, dermatology, and gastroenterology departments at Mayo Clinic. After labeling these messages as either Active Symptom, Logistical, Prescription, or Update, we used NER (named entity recognition) to identify medical concepts based on the UMLS library. We hierarchically analyzed the distribution of these messages in terms of departments, message types, medical concepts, and keywords therewithin. Results. Active Symptom and Logistical content types comprised approximately 67% of the message cohort. The “Findings” medical concept had the largest number of keywords across all groupings of content types and departments. “Anatomical Sites” and “Disorders” keywords were more prevalent in Active Symptom messages, while “Drugs” keywords were most prevalent in Prescription messages. Logistical messages tended to have the lower proportions of “Anatomical Sites,”, “Disorders,”, “Drugs,”, and “Findings” keywords when compared to other message content types. Conclusions. This descriptive corpus analysis sheds light on the content and foci of portal messages. The insight into the content and differences among message themes can inform the development of more robust NLP models.

scholarly journals Extending semantic context analysis using machine learning services to process unstructured data

2021 ◽  
Vol 102 ◽  
pp. 02001
Author(s):  
Anja Wilhelm ◽  
Wolfgang Ziegler
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Knowledge Bases ◽  
Semantic Context ◽  
Unstructured Data ◽  
Context Analysis ◽  
Scientific Publications ◽  
Starting Point ◽  
Processing And Storage ◽  
Modelling Process

The primary focus of technical communication (TC) in the past decade has been the system-assisted generation and utilization of standardized, structured, and classified content for dynamic output solutions. Nowadays, machine learning (ML) approaches offer a new opportunity to integrate unstructured data into existing knowledge bases without the need to manually organize information into topic-based content enriched with semantic metadata. To make the field of artificial intelligence (AI) more accessible for technical writers and content managers, cloud-based machine learning as a service (MLaaS) solutions provide a starting point for domain-specific ML modelling while unloading the modelling process from extensive coding, data processing and storage demands. Therefore, information architects can focus on information extraction tasks and on prospects to include pre-existing knowledge from other systems into the ML modelling process. In this paper, the capability and performance of a cloud-based ML service, IBM Watson, are analysed to assess their value for semantic context analysis. The ML model is based on a supervised learning method and features deep learning (DL) and natural language processing (NLP) techniques. The subject of the analysis is a corpus of scientific publications on the 2019 Coronavirus disease. The analysis focuses on information extractions regarding preventive measures and effects of the pandemic on healthcare workers.

scholarly journals Talking in another person’s shoes: Incremental perspective-taking in language processing

2011 ◽  
Vol 2 (1) ◽  
pp. 11-33 ◽  
Author(s):  
Sarah Brown-Schmidt ◽  
Joy E. Hanna
Keyword(s):  
Language Processing ◽  
Perspective Taking ◽  
Careful Consideration ◽  
Probabilistic Constraints ◽  
Sources Of Information ◽  
Visual World ◽  
Integration Model ◽  
Knowledge And Beliefs ◽  
On Line ◽  
Methodological Advance

Language use in conversation is fundamentally incremental, and is guided by the representations that interlocutors maintain of each other’s knowledge and beliefs. While there is a consensus that interlocutors represent the perspective of others, three candidate models, a Perspective-Adjustment model, an Anticipation-Integration model, and a Constraint-Based model, make conflicting predictions about the role of perspective information during on-line language processing. Here we review psycholinguistic evidence for incrementality in language processing, and the recent methodological advance that has fostered its investigation—the use of eye-tracking in the visual world paradigm. We present visual world studies of perspective-taking, and evaluate each model's account of the data. We argue for a Constraint-Based view in which perspective is one of multiple probabilistic constraints that guide language processing decisions. Addressees combine knowledge of a speaker’s perspective with rich information from the discourse context to arrive at an interpretation of what was said. Understanding how these sources of information combine to influence interpretation requires careful consideration of how perspective representations were established, and how they are relevant to the communicative context.

scholarly journals BIM and DfMA: A Paradigm of New Opportunities

2021 ◽  
Vol 13 (17) ◽  
pp. 9591
Author(s):  
Sepehr Abrishami ◽  
Rocío Martín-Durán
Keyword(s):  
Literature Review ◽  
Conceptual Framework ◽  
Energy Use ◽  
Integrated Approach ◽  
Design Stage ◽  
Extensive Literature ◽  
Sources Of Information ◽  
Construction Methods ◽  
Wide Range

The main goal of this study is to explore the adoption of a design for manufacturing and assembly (DfMA) and building information management (BIM) approach during the whole lifecycle of assets. This approach aims to tackle issues inherent in the design of traditional construction methods, such as low productivity and quality, poor predictability and building performance, and energy use, through the implementation of a BIM library of off-site components. In recent years, a renewed interest has been directed to the attempt to provide solutions to these urgent problems through the adoption of new advancements in technologies. However, while there are studies focussing on a BIM-DfMA approach, there is a lack of research regarding how this approach should be adopted during the whole lifecycle of the assets. Furthermore, to the best of our knowledge, defining an efficient way of developing a component-based BIM object library has not yet been included in any of the available studies. A mixed methodology approach has been used in this research. A conceptual framework was developed as the result of an extensive literature review to investigate new advancements in the AEC sector. Following the literature review, the framework was tested and validated through a case study based on the production and adoption of a BIM library of off-site components at the design stage of an asset. The architecture, engineering, and construction (AEC) industry has recognised the necessity of a new approach that helps to resolve the well-known issues presented in traditional methods of construction. The conceptual framework and case study proposed presents a valuable new method of construction that support the implementation of a BIM and DfMA approach, highlighting their benefits. This framework has been created using many valuable and reliable sources of information. The result of this research supports the idea of a novel new construction method that focuses on a manufacturing-digital-driven industry, with the use of DfMA in a BIM-integrated approach. This novel method will add significance and be beneficial for a wide range of aspects in the construction sector, contributing to the theoretical and practical domain.

Harnessing Knowledge for Sustainable Development

2017 ◽  
pp. 234-251
Author(s):  
Abdelkader Djeflat
Keyword(s):  
Sustainable Development ◽  
Knowledge Economy ◽  
Mineral Resources ◽  
Integrated Approach ◽  
Knowledge Bases ◽  
Arab Countries ◽  
Point Of View ◽  
The World ◽  
Physical Resources ◽  
World Development

Arab countries face two major challenges resulting from increasing competition from the rest of the world and persistent reliance on mineral resources for their growth. At the same time, sustainable development is increasingly becoming a major concern for world development. In this respect, and from a sustainability point of view, knowledge economy opens up new and more accessible opportunities through the ‘substitution' of physical resources by immaterial resources. This situation raises two fundamental questions: the first one relates to the opportunity of ensuring sustainable development while the knowledge base remains rather weak and policies often short-sighted. The second one is how an integrated approach based on knowledge can strengthen existing knowledge bases and create new ones to further sustainable development. Looking at a sample of advanced countries and Arab countries, this chapter argues that sustainability of growth rests fundamentally on the capability of properly harnessing knowledge.

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