scholarly journals Using Knowledge Graphs to Plausibly Infer Missing Associations in EMR Data

2021 ◽  
Author(s):  
William Van Woensel ◽  
Chad Armstrong ◽  
Malavan Rajaratnam ◽  
Vaibhav Gupta ◽  
Syed Sibte Raza Abidi
Keyword(s):  
Medical Records ◽  
Medical Knowledge ◽  
Data Entry ◽  
Clinical Encounter ◽  
Snomed Ct ◽  
Record Keeping ◽  
Domain Specific ◽  
Knowledge Based ◽  
Domain Specific Knowledge ◽  
Entry Restrictions

Electronic Medical Records (EMRs) are increasingly being deployed at primary points of care and clinics for digital record keeping, increasing productivity and improving communication. In practice, however, there still exists an often incomplete picture of patient profiles, not only because of disconnected EMR systems but also due to incomplete EMR data entry – often caused by clinician time constraints and lack of data entry restrictions. To complete a patient’s partial EMR data, we plausibly infer missing causal associations between medical EMR concepts, such as diagnoses and treatments, for situations that lack sufficient raw data to enable machine learning methods. We follow a knowledge-based approach, where we leverage open medical knowledge sources such as SNOMED-CT and ICD, combined with knowledge-based reasoning with explainable inferences, to infer clinical encounter information from incomplete medical records. To bootstrap this process, we apply a semantic Extract-Transform-Load process to convert an EMR database into an enriched domain-specific Knowledge Graph.

scholarly journals Quality assurance and enrichment of biological and biomedical ontologies and terminologies

2020 ◽  
Vol 20 (S10) ◽  
Author(s):  
Ankur Agrawal ◽  
Licong Cui
Keyword(s):  
Quality Assurance ◽  
Cancer Registries ◽  
Supplement Issue ◽  
Biomedical Ontologies ◽  
Snomed Ct ◽  
Unified Medical Language System ◽  
Domain Specific ◽  
Healthcare Settings ◽  
Domain Specific Knowledge ◽  
Enrichment Techniques

AbstractBiological and biomedical ontologies and terminologies are used to organize and store various domain-specific knowledge to provide standardization of terminology usage and to improve interoperability. The growing number of such ontologies and terminologies and their increasing adoption in clinical, research and healthcare settings call for effective and efficient quality assurance and semantic enrichment techniques of these ontologies and terminologies. In this editorial, we provide an introductory summary of nine articles included in this supplement issue for quality assurance and enrichment of biological and biomedical ontologies and terminologies. The articles cover a range of standards including SNOMED CT, National Cancer Institute Thesaurus, Unified Medical Language System, North American Association of Central Cancer Registries and OBO Foundry Ontologies.

scholarly journals ONTOLOGY-BASED WEB TOOLS FOR RETRIEVING PHOTOGRAMMETRIC CULTURAL HERITAGE MODELS

2019 ◽  
Vol XLII-2/W10 ◽  
pp. 31-38 ◽  
Author(s):  
M. Ben Ellefi ◽  
P. Drap ◽  
O. Papini ◽  
D. Merad ◽  
J. P. Royer ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Knowledge Based ◽  
Web Tools ◽  
Domain Specific Knowledge ◽  
Visualization Systems ◽  
User Friendly ◽  
Machine Readable ◽  
Associated Knowledge

<p><strong>Abstract.</strong> A key challenge in cultural heritage (CH) sites visualization is to provide models and tools that effectively integrate the content of a CH data with domain-specific knowledge so that the users can query, interpret and consume the visualized information. Moreover, it is important that the intelligent visualization systems are interoperable in the semantic web environment and thus, capable of establishing a methodology to acquire, integrate, analyze, generate and share numeric contents and associated knowledge in human and machine-readable Web. In this paper, we present a model, a methodology and a software Web-tools that support the coupling of the 2D/3D Web representation with the knowledge graph database of <i>Xlendi</i> shipwreck. The Web visualization tools and the knowledge-based techniques are married into a photogrammetry driven ontological model while at the same time, user-friendly web tools for querying and semantic consumption of the shipwreck information are introduced.</p>

Human Cognitive Processes

2009 ◽  
pp. 1-33
Author(s):  
Slava Kalyuga
Keyword(s):  
Working Memory ◽  
Cognitive Architecture ◽  
Human Cognition ◽  
Long Term Memory ◽  
Domain Specific ◽  
Knowledge Based ◽  
Domain Specific Knowledge ◽  
And Performance ◽  
Memory Knowledge ◽  
High Level

One of the major components of our cognitive architecture, working memory, becomes overloaded if more than a few chunks of information are processed simultaneously. For example, we all experience this cognitive overload when trying to keep in memory an unfamiliar telephone number or add two four-digit numbers in the absence of a pen and paper. Similar in nature processing limitations of working memory represent a major factor influencing the effectiveness of human learning and performance, particularly in complex environments that require concurrent performance of multiple tasks. The learner prior domain-specific knowledge structures and associated levels of expertise are considered as means of reducing these limitations and guiding high-level knowledge-based cognitive activities. One of the most important results of studies in human cognition is that the available knowledge is a single most significant learner cognitive characteristic that influences learning and cognitive performance. Understanding the key role of long-term memory knowledge base in our cognition is important to the successful management of cognitive load in multimedia learning.

Multi-Agent Architecture for Knowledge-Driven Decision Support

2010 ◽  
pp. 28-46
Author(s):  
Rahul Singh
Keyword(s):  
Decision Support ◽  
Intelligent Agents ◽  
Agent Architecture ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Knowledge Based ◽  
Intelligent Software Agents ◽  
Intelligent Decision ◽  
Domain Specific Knowledge ◽  
Multi Agent

Organizations use knowledge-driven systems to deliver problem-specific knowledge over Internet-based distributed platforms to decision-makers. Increasingly, artificial intelligence (AI) techniques for knowledge representation are being used to deliver knowledge-driven decision support in multiple forms. In this chapter, we present an Architecture for knowledge-based decision support, delivered through a Multi-Agent Architecture. We illustrate how to represent and exchange domain-specific knowledge in XML-format through intelligent agents to create exchange and use knowledge to provide intelligent decision support. We show the integration of knowledge discovery techniques to create knowledge from organizational data; and knowledge repositories (KR) to store, manage and use data by intelligent software agents for effective knowledge-driven decision support. Implementation details of the architecture, its business implications and directions for further research are discussed.

The Pipeline for Standardizing Russian Unstructured Allergy Anamnesis Using FHIR AllergyIntolerance Resource

2021 ◽  
Author(s):  
Iuliia D. Lenivtceva ◽  
Georgy Kopanitsa
Keyword(s):  
Medical Records ◽  
Data Exchange ◽  
Medical Knowledge ◽  
Data Preprocessing ◽  
Semantic Interoperability ◽  
International Standards ◽  
Free Text ◽  
Snomed Ct ◽  
Process Standardization ◽  
Specific Medication

Abstract Background The larger part of essential medical knowledge is stored as free text which is complicated to process. Standardization of medical narratives is an important task for data exchange, integration, and semantic interoperability. Objectives The article aims to develop the end-to-end pipeline for structuring Russian free-text allergy anamnesis using international standards. Methods The pipeline for free-text data standardization is based on FHIR (Fast Healthcare Interoperability Resources) and SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) to ensure semantic interoperability. The pipeline solves common tasks such as data preprocessing, classification, categorization, entities extraction, and semantic codes assignment. Machine learning methods, rule-based, and dictionary-based approaches were used to compose the pipeline. The pipeline was evaluated on 166 randomly chosen medical records. Results AllergyIntolerance resource was used to represent allergy anamnesis. The module for data preprocessing included the dictionary with over 90,000 words, including specific medication terms, and more than 20 regular expressions for errors correction, classification, and categorization modules resulted in four dictionaries with allergy terms (total 2,675 terms), which were mapped to SNOMED CT concepts. F-scores for different steps are: 0.945 for filtering, 0.90 to 0.96 for allergy categorization, 0.90 and 0.93 for allergens reactions extraction, respectively. The allergy terminology coverage is more than 95%. Conclusion The proposed pipeline is a step to ensure semantic interoperability of Russian free-text medical records and could be effective in standardization systems for further data exchange and integration.

A domain-specific knowledge-based heuristic for the Blocks Relocation Problem

2014 ◽  
Vol 28 (4) ◽  
pp. 327-343 ◽  
Author(s):  
Christopher Expósito-Izquierdo ◽  
Belén Melián-Batista ◽  
J. Marcos Moreno-Vega
Keyword(s):  
Specific Knowledge ◽  
Domain Specific ◽  
Knowledge Based ◽  
Domain Specific Knowledge

scholarly journals Clinical reasoning in the health professions, 2nd Edition.

2003 ◽  
Vol 1 (3) ◽  
Author(s):  
Bill Lord
Keyword(s):  
Decision Making ◽  
Clinical Reasoning ◽  
Medical Records ◽  
Prehospital Care ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Effective Delivery ◽  
Domain Specific Knowledge ◽  
Time Critical ◽  
Extensive Clinical Experience

Decisions relating to the timely and effective delivery of prehospital care depend on “clinical reasoning”, a skill that integrates extensive clinical experience and domain specific knowledge to support the collection and evaluation of clinical data, and the synthesis of information to guide patient management decisions. In contrast to health care centred within an institution where decisions can be supported by expert opinion, access to medical records, and advanced diagnostic tests, patient care in the prehospital domain usually requires independent, time critical decision making in an environment where similar support is unavailable or incomplete. Hence sound and reliable thinking and decision-making skills are fundamental to prehospital practice.

A KNOWLEDGE-BASED METHOD FOR DERIVING CLASSES AND OBJECTS

1999 ◽  
Vol 08 (02) ◽  
pp. 239-251
Author(s):  
ALAN LIU ◽  
ARTHUR M. D. SHR
Keyword(s):  
Software Development ◽  
Object Oriented ◽  
Development Work ◽  
Software Systems ◽  
Specific Knowledge ◽  
Development Method ◽  
Domain Specific ◽  
Knowledge Based ◽  
Domain Specific Knowledge ◽  
Special Domain

Identifying classes and objects in an object-oriented (OO) software development method requires a great amount of domain-specific knowledge and OO developing experiences to achieve the work. Experienced developers always have heuristic solutions to different problems. However, novice developers have difficulties developing their desired OO software systems. We propose a method that uses a knowledge-based system with the identification knowledge to support developers to obtain classes and objects that are suitable for one special domain problem. With the help of the identification knowledge, the developers can model the system easily and complete the rest of development work quickly.

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