The use of a Domain Ontology for the Management of Essential Hypertension (Preprint)

2020 ◽  
Author(s):  
Emma Chavez ◽  
Vanessa Perez ◽  
Angélica Urrutia
Keyword(s):  
Decision Making ◽  
Essential Hypertension ◽  
Medical History ◽  
Medical Records ◽  
Medical Training ◽  
Relevant Information ◽  
Domain Ontology ◽  
Free Text ◽  
Snomed Ct ◽  
History Of

BACKGROUND : Currently, hypertension is one of the diseases with greater risk of mortality in the world. Particularly in Chile, 90% of the population with this disease has idiopathic or essential hypertension. Essential hypertension is characterized by high blood pressure rates and it´s cause is unknown, which means that every patient might requires a different treatment, depending on their history and symptoms. Different data, such as history, symptoms, exams, etc., are generated for each patient suffering from the disease. This data is presented in the patient’s medical record, in no order, making it difficult to search for relevant information. Therefore, there is a need for a common, unified vocabulary of the terms that adequately represent the diseased, making searching within the domain more effective. OBJECTIVE The objective of this study is to develop a domain ontology for essential hypertension , therefore arranging the more significant data within the domain as tool for medical training or to support physicians’ decision making will be provided. METHODS The terms used for the ontology were extracted from the medical history of de-identified medical records, of patients with essential hypertension. The Snomed-CT’ collection of medical terms, and clinical guidelines to control the disease were also used. Methontology was used for the design, classes definition and their hierarchy, as well as relationships between concepts and instances. Three criteria were used to validate the ontology, which also helped to measure its quality. Tests were run with a dataset to verify that the tool was created according to the requirements. RESULTS An ontology of 310 instances classified into 37 classes was developed. From these, 4 super classes and 30 relationships were obtained. In the dataset tests, 100% correct and coherent answers were obtained for quality tests (3). CONCLUSIONS The development of this ontology provides a tool for physicians, specialists, and students, among others, that can be incorporated into clinical systems to support decision making regarding essential hypertension. Nevertheless, more instances should be incorporated into the ontology by carrying out further searched in the medical history or free text sections of the medical records of patients with this disease.

scholarly journals Documentation of ethically relevant information in out-of-hospital resuscitation is rare: a Danish nationwide observational study of 16,495 out-of-hospital cardiac arrests

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Louise Milling ◽  
Lars Grassmé Binderup ◽  
Caroline Schaffalitzky de Muckadell ◽  
Erika Frischknecht Christensen ◽  
Annmarie Lassen ◽  
...  
Keyword(s):  
Decision Making ◽  
Observational Study ◽  
Medical Records ◽  
Best Practice ◽  
Qualitative Content Analysis ◽  
Relevant Information ◽  
Free Text ◽  
Do Not Resuscitate ◽  
Nationwide Observational Study ◽  
Hospital Cardiac Arrest

Abstract Background Decision-making in out-of-hospital cardiac arrest should ideally include clinical and ethical factors. Little is known about the extent of ethical considerations and their influence on prehospital resuscitation. We aimed to determine the transparency in medical records regarding decision-making in prehospital resuscitation with a specific focus on ethically relevant information and consideration in resuscitation providers’ documentation. Methods This was a Danish nationwide retrospective observational study of out-of-hospital cardiac arrests from 2016 through 2018. After an initial screening using broadly defined inclusion criteria, two experienced philosophers performed a qualitative content analysis of the included medical records according to a preliminary codebook. We identified ethically relevant content in free-text fields and categorised the information according to Beauchamp and Childress’ four basic bioethical principles: autonomy, non-maleficence, beneficence, and justice. Results Of 16,495 medical records, we identified 759 (4.6%) with potentially relevant information; 710 records (4.3%) contained ethically relevant information, whereas 49 did not. In general, the documentation was vague and unclear. We identified four kinds of ethically relevant information: patients’ wishes and perspectives on life; relatives’ wishes and perspectives on patients’ life; healthcare professionals’ opinions and perspectives on resuscitation; and do-not-resuscitate orders. We identified some “best practice” examples that included all perspectives of decision-making. Conclusions There is sparse and unclear evidence on ethically relevant information in the medical records documenting resuscitation after out-of-hospital cardiac arrests. However, the “best practice” examples show that providing sufficient documentation of decision-making is, in fact, feasible. To ensure transparency surrounding prehospital decisions in cardiac arrests, we believe that it is necessary to ensure more systematic documentation of decision-making in prehospital resuscitation.

Scotland, Malawi and Medicine: Livingstone's Legacy, I Presume? An Historical Perspective.

2007 ◽  
Vol 52 (3) ◽  
pp. 36-44
Author(s):  
John Lwanda
Keyword(s):  
Medical History ◽  
Historical Perspective ◽  
Medical Training ◽  
Medical Services ◽  
Future Role ◽  
The Past ◽  
History Of

In this personal short historical perspective I reflect on aspects of the medical history of Malawi, formerly Nyasaland, highlighting the role of Scotland and its people in the development of the Malawi medical services in both the colonial as well as the postcolonial period which began in 1964. The paper, after discussing the history of medical training in Malawi and current constraints and challenges, goes on to make some suggestions - based on historical lessons - about future role of Scottish involvement in Malawi's medical development. It would be unfortunate if, in a rush to ‘help or do something’ the mistakes of the past are repeated.

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.

scholarly journals Natural Language Processing Based Instrument for Classification of Free Text Medical Records

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Manana Khachidze ◽  
Magda Tsintsadze ◽  
Maia Archuadze
Keyword(s):  
Language Processing ◽  
Medical Records ◽  
Nearest Neighbor ◽  
Health Management ◽  
Support Vector ◽  
Free Text ◽  
Features Selection ◽  
Second Stage ◽  
History Of

According to the Ministry of Labor, Health and Social Affairs of Georgia a new health management system has to be introduced in the nearest future. In this context arises the problem of structuring and classifying documents containing all the history of medical services provided. The present work introduces the instrument for classification of medical records based on the Georgian language. It is the first attempt of such classification of the Georgian language based medical records. On the whole 24.855 examination records have been studied. The documents were classified into three main groups (ultrasonography, endoscopy, and X-ray) and 13 subgroups using two well-known methods: Support Vector Machine (SVM) andK-Nearest Neighbor (KNN). The results obtained demonstrated that both machine learning methods performed successfully, with a little supremacy of SVM. In the process of classification a “shrink” method, based on features selection, was introduced and applied. At the first stage of classification the results of the “shrink” case were better; however, on the second stage of classification into subclasses 23% of all documents could not be linked to only one definite individual subclass (liver or binary system) due to common features characterizing these subclasses. The overall results of the study were successful.

scholarly journals Knowledge discovery for Deep Phenotyping serious mental illness from Electronic Mental Health records

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 210 ◽  
Author(s):  
Richard Jackson ◽  
Rashmi Patel ◽  
Sumithra Velupillai ◽  
George Gkotsis ◽  
David Hoyle ◽  
...  
Keyword(s):  
Mental Health ◽  
Mental Illness ◽  
Serious Mental Illness ◽  
Real World ◽  
Large Body ◽  
Relevant Information ◽  
Knowledge Bases ◽  
Free Text ◽  
Snomed Ct ◽  
Deep Phenotyping

Background: Deep Phenotyping is the precise and comprehensive analysis of phenotypic features in which the individual components of the phenotype are observed and described. In UK mental health clinical practice, most clinically relevant information is recorded as free text in the Electronic Health Record, and offers a granularity of information beyond what is expressed in most medical knowledge bases. The SNOMED CT nomenclature potentially offers the means to model such information at scale, yet given a sufficiently large body of clinical text collected over many years, it is difficult to identify the language that clinicians favour to express concepts. Methods: By utilising a large corpus of healthcare data, we sought to make use of semantic modelling and clustering techniques to represent the relationship between the clinical vocabulary of internationally recognised SMI symptoms and the preferred language used by clinicians within a care setting. We explore how such models can be used for discovering novel vocabulary relevant to the task of phenotyping Serious Mental Illness (SMI) with only a small amount of prior knowledge.  Results: 20 403 terms were derived and curated via a two stage methodology. The list was reduced to 557 putative concepts based on eliminating redundant information content. These were then organised into 9 distinct categories pertaining to different aspects of psychiatric assessment. 235 concepts were found to be expressions of putative clinical significance. Of these, 53 were identified having novel synonymy with existing SNOMED CT concepts. 106 had no mapping to SNOMED CT. Conclusions: We demonstrate a scalable approach to discovering new concepts of SMI symptomatology based on real-world clinical observation. Such approaches may offer the opportunity to consider broader manifestations of SMI symptomatology than is typically assessed via current diagnostic frameworks, and create the potential for enhancing nomenclatures such as SNOMED CT based on real-world expressions.

Contradictory coroners? Decision-making in death investigations

2017 ◽  
Vol 70 (9) ◽  
pp. 787-791 ◽  
Author(s):  
Maxwell Mclean
Keyword(s):  
Decision Making ◽  
Medical History ◽  
Cause Of Death ◽  
Computer Software ◽  
Free Text ◽  
Information Order ◽  
Clinical Scenarios ◽  
Decision Making Processes ◽  
Personal Attitudes ◽  
Decision Board

AimNational coroner data demonstrate differences in the rates at which coroners across England and Wales choose to investigate reported deaths and the frequency by which they record certain conclusions. This study sought to examine how decisions are made by coroners and whether they differed when faced with identical case information.MethodsThree different clinical scenarios were circulated via a web link to all senior coroners. The case information was contained within a ‘Decision Board’ displayed on screen. Each scenario had nine consistent categories of information, such as the cause of death and the medical history. Participants were asked to indicate an inquest conclusion (verdict) using free text and to provide comments. The way in which participants accessed the case information (order, frequency, etc) was recorded by the computer software.Results35 coroners responded. There was little consensus as to conclusion with scenarios 1 and 2 generating four different outcomes and scenario 3 generating an extraordinary eight different conclusions among respondents. Despite coming to widely different conclusions, coroners demonstrated very similar decision-making processes. Conclusions were robustly defended yet proffered alternatives were plentiful. The comments made indicated a difference in the personal attitudes of coroners towards case information.ConclusionsDifferent coroners faced with identical case information arrived at widely different case outcomes ranging from no further investigation to finding numerous alternative verdicts. Disparity appeared to be a product of differing personal attitudes among coroners. National coroner consensus to achieve a shared inference from available evidence is urgently needed.

scholarly journals Knowledge discovery for Deep Phenotyping serious mental illness from Electronic Mental Health records

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 210 ◽  
Author(s):  
Richard Jackson ◽  
Rashmi Patel ◽  
Sumithra Velupillai ◽  
George Gkotsis ◽  
David Hoyle ◽  
...  
Keyword(s):  
Mental Health ◽  
Mental Illness ◽  
Serious Mental Illness ◽  
Real World ◽  
Large Body ◽  
Relevant Information ◽  
Knowledge Bases ◽  
Free Text ◽  
Snomed Ct ◽  
Deep Phenotyping

Background: Deep Phenotyping is the precise and comprehensive analysis of phenotypic features, where the individual components of the phenotype are observed and described. In UK mental health clinical practice, most clinically relevant information is recorded as free text in the Electronic Health Record, and offers a granularity of information beyond that expressed in most medical knowledge bases. The SNOMED CT nomenclature potentially offers the means to model such information at scale, yet given a sufficiently large body of clinical text collected over many years, it’s difficult to identify the language that clinicians favour to express concepts. Methods: Vector space models of language seek to represent the relationship between words in a corpus in terms of cosine distance between a series of vectors. When utilising a large corpus of healthcare data and combined with appropriate clustering techniques and manual curation, we explore how such models can be used for discovering vocabulary relevant to the task of phenotyping Serious Mental Illness (SMI) with only a small amount of prior knowledge. Results: 20 403 n-grams were derived and curated via a two stage methodology. The list was reduced to 557 putative concepts based on eliminating redundant information content. These were then organised into 9 distinct categories pertaining to different aspects of psychiatric assessment. 235 (42%) concepts were found to be depictions of putative clinical significance. Of these, 53 (10%) were identified having novel synonymy with existing SNOMED CT concepts. 106 (19%) had no mapping to SNOMED CT. Conclusions: We demonstrate a scalable approach to discovering new depictions of SMI symptomatology based on real world clinical observation. Such approaches may offer the opportunity to consider broader manifestations of SMI symptomatology than is typically assessed via current diagnostic frameworks, and create the potential for enhancing nomenclatures such as SNOMED CT based on real world depictions.

Elaborating the role of reflection and individual differences in the study of folk-economic beliefs

2018 ◽  
Vol 41 ◽  
Author(s):  
Kevin Arceneaux
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Cognitive Processes ◽  
Evolutionary History ◽  
Behavioral Responses ◽  
History Of ◽  
Economic Beliefs

AbstractIntuitions guide decision-making, and looking to the evolutionary history of humans illuminates why some behavioral responses are more intuitive than others. Yet a place remains for cognitive processes to second-guess intuitive responses – that is, to be reflective – and individual differences abound in automatic, intuitive processing as well.

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