Medical Thesaurus Research Articles

Analysis of Health Data for the Elderly based on Medical Website Mining

The development of Internet medicine provides a convenient platform for doctor-patient communication, and provides an important data source for paying attention to the health of the elderly. In this study, a large number of consultation records obtained from the medical website was screened and cleaned, and the medical thesaurus was generated by training of its own data to solve the problem of inaccurate professional terminology segmentation caused by using default jieba segmentation. At the same time, we use the trained medical thesaurus to conduct topic mining of the consultation data, and it is found that the most concerned problems in the field of elderly health are cerebro-cardiovascular, pulmonary and stomach diseases, so as to provide further medical advice and targeted services.

Implementation of artificial intelligence in speech (voice input) in the conditions of a high-flow endoscopy

Artificial intelligence (AI) and digitalization are used in all spheres of life, including medicine: in making diagnoses, in working with patients, in creating and using an Electronic Medical Record (EMC) for fast and accessible document management. Attempts have been made repeatedly to facilitate work with medical protocols. Technologies related to speech recognition are a promising direction in this area. The purpose of the research work was to optimize the time of writing the protocol using voice input. The object of the study was a template protocol of endoscopic examination. This work was carried out on the basis of 2 large endoscopic centers in Moscow, created as part of the pilot project of the DM within the framework of the colorectal cancer screening program in the Russian Federation in 2021. The software of the Voice2Med Speech Technology Center (MDG) was used to fill out the research protocol. Using mathematical calculations, 4 metrics were created to evaluate the work of the software: the time of protocol formation; the proportion of protocol formation time from the study; the level of speech recognition accuracy; satisfaction of doctors when working with technology. Standardized protocol templates for various nosologies related to voice commands have been developed; medical thesaurus. In the course of the work, the accuracy of speech recognition was investigated and improved. Control measurements of the time of writing the protocol were carried out. The results of the evaluation of the use of the technology by endoscopists were obtained - the time of protocol formation decreased by 29%, the evaluation of the work by endoscopists was positive. Thus, the use of speech technologies related to the formation of a doctor’s protocol “from the voice” is a promising direction for the further development of the IT sphere in practical medicine.

On the necessity in unified normative medical lexicon within state language of Russia

Introduction. Unlike other sciences, medicine has not developed a single consistent vocabulary equally understood by all medical specialists. It complicates corporate and professional communication between physicians. Aim. To analyze the use of the Russian language as a state language in medicine and health care. Material and methods. Analysis of publications on the problem, including existing regulations and laws and scientific articles and books. A survey of 75 medical doctors, including experienced specialists and novice physicians, on using medical terminology in their professional activities and corporate communication. Results. The Federal Law does not explicitly stipulate the Russian literary language in professional scientific oral and written speech. However, difficulties inevitably arise in civil and social life when drawing up and reading medical instructions, advertisements, and health insurance contracts. Our survey of medical workers confirmed the reality and urgency of this problem. The absence of normative vocabularies of the professional medical Russian sub-language complicates physicians’ and teachers’ professional activities; negatively affects their communication with each other and with patients/students, reducing the effectiveness of clinical, scientific, and educational medical activities. Conclusion. The lack of normative reference publications establishing uniform requirements for a medical sub-language at the state level complicates medical practice, reduces the research effectiveness, and affects the education quality. The task is to bring Russian medical terminology in line with the current global medical thesaurus and unify the interpretation of words and concepts by physicians of different specialties, generations, and schools.

Hospital Scenes in Dostoevsky’s “Notes from a Dead House”

Dostoevsky conceived “Notes from a Dead House” as a memoir, but the concept was transformed, leading to a change in the chronology and circumstances of the author’s biography. The writer not only described, but also composed his life, placed it in the context of world history. Hospital scenes are important in the structure of “Notes.” Dostoevsky gave an almost professional description of physical illnesses and their treatment. He passes competent judgement on hospital medicine, is fluent in the medical thesaurus, and uses medical Latin. His descriptions are accompanied by judgments and stories about crimes and corporal punishment of prisoners, about executioners and executioners. He believes that medicine in Russia is hostile and does not correspond to the spirit of the people, their habits. The author’s descriptions are factual. They recreate an empirical reality that receives metaphysical interpretation. The means of transforming the empirical into the sacred is Dante’s “Divine Comedy,” which influenced the concept of hell, the poetics and structure of Dostoevsky’s “Notes from a Dead House.” Dante saw hell, purgatory, paradise; his visions became the subject of poetry. Dostoevsky lived in hell, in a “Dead House,” this is his personal experience. There are two narrators in the “Notes”: the author’s alter ego and the fictional Alexander Petrovich Goryanchikov, a kind of literary mask of the author. They have different roles, which are often difficult to distinguish. The hero’s name and patronymic are most often heard in conversations with other characters: he asks questions, and they tell him about themselves. It is not the author who asks tactless questions to the heroes — Alexander Petrovich, a knowledgeable prisoner of the “Dead House,” does it for him. He is Dostoevsky’s Virgil. Alexander Petrovich has no future, his hellish toils without purgatory and paradise continued in the “hellish life on the outside.” Not purgatory, but Easter is the act of transformation of the author, who has risen from the dead, found freedom, a new life and a future.

The iCRF Generator: Generating interoperable electronic case report forms using online codebooks

Semantic interoperability of clinical data is essential to preserve its meaning and intent when the data is exchanged, re-used or integrated with other data. Achieving semantic operability requires the use of a communication standard, such as HL7, as well as (functional) information standards. Manually mapping clinical data to a medical thesaurus, such as SNOMED CT, is complicated and requires expert knowledge of both the dataset, including its context, and the thesaurus. As an alternative, the (re-)use of codebooks, data definitions which may already have been mapped to a thesaurus, can be a viable approach. We've developed the iCRF Generator, a Java program that can generate the core of an interoperable electronic case report form (iCRF) for several of the major electronic data capture systems (EDCs). To build their CRFs, users can select one or more items from established codebooks, available from an online system called ART-DECOR. By providing an easy to use method to create CRFs for multiple EDCs based on the same codebooks, interoperability can be more easily attained.

The iCRF Generator: Generating interoperable electronic case report forms using online codebooks

Semantic interoperability of clinical data is essential to preserve its meaning and intent when the data is exchanged, re-used or integrated with other data. Achieving semantic operability requires the use of a communication standard, such as HL7, as well as (functional) information standards. Manually mapping clinical data to a medical thesaurus, such as SNOMED CT, is complicated and requires expert knowledge of both the dataset, including its context, and the thesaurus. As an alternative, the (re-)use of codebooks, data definitions which may already have been mapped to a thesaurus, can be a viable approach. We’ve developed the iCRF Generator, a Java program that can generate the core of an interoperable electronic case report form (iCRF) for several of the major electronic data capture systems (EDCs). To build their CRFs, users can select one or more items from established codebooks, available from an online system called ART-DECOR. By providing an easy to use method to create CRFs for multiple EDCs based on the same codebooks, interoperability can be more easily attained.

The iCRF Generator: Generating interoperable electronic case report forms using online codebooks.

Semantic interoperability of clinical data is essential to preserve its meaning and intent when the data is exchanged, re-used or integrated with other data. Achieving semantic operability requires the use of a communication standard, such as HL7, as well as (functional) information standards. Manually mapping clinical data to a medical thesaurus, such as SNOMED CT, is complicated and requires expert knowledge of both the dataset, including its context, and the thesaurus. As an alternative, the (re-)use of codebooks, data definitions which may already have been mapped to a thesaurus, can be a viable approach. We've developed the iCRF Generator, a Java program that can generate the core of an interoperable electronic case report form (iCRF) for several of the major electronic data capture systems (EDCs). To build their CRFs, users can select one or more items from established codebooks, available from an online system called ART-DECOR. By providing an easy to use method to create CRFs for multiple EDCs based on the same codebooks, interoperability can be more easily attained.

Resources for assigning MeSH IDs to Japanese medical terms.

Medical Subject Headings (MeSH), a medical thesaurus created by the National Library of Medicine (NLM), is a useful resource for natural language processing (NLP). In this article, the current status of the Japanese version of Medical Subject Headings (MeSH) is reviewed. Online investigation found that Japanese-English dictionaries, which assign MeSH information to applicable terms, but use them for NLP, were found to be difficult to access, due to license restrictions. Here, we investigate an open-source Japanese-English glossary as an alternative method for assigning MeSH IDs to Japanese terms, to obtain preliminary data for NLP proof-of-concept.

Selection of a geographic area of interest for syndromic surveillance

Selection of a geographic area of interest for syndromic surveillance

Mining and standardizing chinese consumer health terms

BackgroundHealth professionals and consumers use different terms to express medical events or concerns, which makes the communication barriers between the professionals and consumers. This may lead to bias in the diagnosis or treatment due to the misunderstanding or incomplete understanding. To solve the issue, a consumer health vocabulary was developed to map the consumer-used health terms to professional-used medical terms.MethodsIn this study, we extracted Chinese consumer health terms from both online health forum and patient education monographs, and manually mapped them to medical terms used by professionals (terms in medical thesauri or in medical books). To ensure the above annotation quality, we developed annotation guidelines.ResultsWe applied our method to extract consumer-used disease terms in endocrinology, cardiology, gastroenterology and dermatology. In this study, we identified 1349 medical mentions from 8436 questions posted in an online health forum and 1428 articles for patient education monographs. After manual annotation and review, we released 1036 Chinese consumer health terms with mapping to 480 medical terms. Four annotators worked on the manual annotation work following the Chinese consumer health term annotation guidelines. Their average inter-annotator agreement (IAA) score was 93.91% ensuring high consistency of the released terms.ConclusionsWe extracted Chinese consumer health terms from online forum and patient education monographs, and mapped them to medical terms used by professionals. Manual annotation efforts have been made for term annotating and mapping. Our study may contribute to the Chinese consumer health vocabulary construction. In addition, our annotated corpus, both the contexts of consumer health terms and consumer-professional term mapping, would be a useful resource for automatic methodology development. The dataset of the Chinese consumer health terms (CHT) is publicly available at http://www.phoc.org.cn/cht/.

Increase of Scarlet fever in March 2017 in France: right or wrong signal?

Objective: Describe a case study of validation of a scarlet fever outbreak using syndromic surveillance data sources.Introduction: Since 2004, the French syndromic surveillance system SurSaUD® [1] coordinated by the French Public Health Agency (Sante publique France) daily collects morbidity data from two data sources: the emergency departments (ED) network Oscour® and the emergency general practitioners’ associations SOS Médecins. Almost 92% of the French ED attendances are recorded by the system. SOS Médecins network is a group of 62 associations of general practitioners, dispatched all over the territory. Sante publique France received data from 61 out of 62 associations. Both data sources collect medical diagnosis, using ICD10 codes in the ED network and specific medical thesaurus in SOS Médecins associations.These data are routinely analyzed to detect and follow-up various expected or unusual public health events all over the territory [2]. The system is also used for reassurance of decision makers. In that framework, in March 2017, the French Ministry of Health requested Santé publique France to validate a potential scarlet fever outbreak in France.Methods: ED attendances for scarlet fever were identified using the ICD10 code “A38”. SOS Médecins visits with the specific code corresponding to “scarlet fever” were considered.The weekly numbers of ED attendances and SOS Médecins visits for scarlet fever were analyzed from 02/01/2017 (week 5) to 03/31/2017 (week 13) by age group (all ages and less than 15 years old, scarlet fever affecting mainly children) and were compared to the numbers of attendances and visits registered during the same period of the two previous years.Analysis was conducted both at national and regional levels. In order to take into account the improvement of data quality during the study period, we also calculated proportion of attendances and visits for scarlet fever among the overall attendances (respectively visits) with medical coded information.Results: The number of SOS Médecins visits for scarlet fever started to increase in week 9 of 2017. Almost 95% of visits concerned children aged less than 15 years old. SOS Médecins visits for scarlet fever represented 0.24% of the overall visits for the 2 age groups for weeks 11, 13 and 14. This proportion was never reached in 2015 and was observed twice in 2016, but later in the year (weeks 25 and 26).The regional analysis showed that all French metropolitan regions contributed to the increase, even if Paris region was the most impacted. More specifically, cases were mainly located in the east part of the Paris region (in Seine-et-Marne).In the OSCOUR® network, the analysis of the number of attendances for scarlet fever at the national level shows a limited increase from week 9 to week 12. Weekly proportion of ED attendances for scarlet fever among the total coded attendances remained comparable to those observed the two previous years on the same period.The regional analysis also showed that 35% of attendances for scarlet fever during this period were observed in Paris area. But, number of attendances for scarlet fever in this region was comparable during this period to numbers observed the two previous years.Conclusions: The analysis of emergency syndromic data sources enables to confirm an increase of consultations for scarlet fever in SOS Médecins associations from weeks 9 to 14, mainly for children less than 15 years old.The large implementation of the SOS Médecins associations on the whole territory allowed us to provide a geographical location of the outbreak: mainly in the east part of Paris area. The temporal pattern of scarlet fever visits in this region may be in favor of a small cluster of cases.The availability of data collected routinely during a long period of time by the syndromic surveillance system enables to evaluate that the outbreak occurred earlier than the previous years, but the intensity of the outbreak was similar to those observed previously.Scarlet outbreak was not confirmed through the ED network, even if a limited increase was observed during the same period of time. The investigation of this outbreak in ED network revealed a miscoding practice in one ED structure, resulting locally in a larger number of attendances than in the other ED of Paris area.Finally, this case study led to improve data quality and highlighted the importance of the validation step of alarms by epidemiologists, even in an automatized system.

Medical Image Retrieval in Healthcare Social Networks

In this article, the authors present a multimodal research model to research medical images based on multimedia information that is extracted from a radiological collaborative social network. The opinions shared on a medical image in a medico-social network is a textual description which in most cases requires cleaning by using a medical thesaurus. In addition, they describe the textual description and medical image in a TF-IDF weight vector using a “bag-of-words” approach. The authors then use latent semantic analysis to establish relationships between textual terms and visual terms in shared opinions on the medical image. The model is evaluated against the ImageCLEFmedbaseline, which is the ground truth for the experiments. The authors have conducted numerous experiments with different descriptors and many combinations of modalities. The analysis of results shows that when the model is based on two methods it can increase the performance of a research system based on a single modality both visually or textually.

A comparative study of the origin, structure, and indexing language of the Persian and English keywords of articles indexed in the IranMedex database and their compliance with the Persian medical thesaurus and Medical Subject Headings.

Introduction:In the present era, thesauri as tools in indexing play an effective role in integrating retrieval preventing fragmentation as well as a multiplicity of terminologies and also in providing information content of documents.Goals:This study aimed to investigate the keywords of articles indexed in IranMedex in terms of origin, structure and indexing situation and their Compliance with the Persian Medical Thesaurus and Medical Subject Headings (MeSH).Materials and Methods:This study is an applied research, and a survey has been conducted. Statistical population includes 32,850 Persian articles which are indexed in the IranMedex during the years 1385–1391. 379 cases were selected as sample of the study. Data collection was done using a checklist. In analyzing the findings, the SPSS Software were used.Findings:Although there was no significant difference in terms of indexing origin between the proportion of different types of the Persian and English keywords of articles indexed in the IranMedex, the compliance rates of the Persian and English keywords with the Persian medical thesaurus and MeSH were different in different years. In the meantime, the structure of keywords is leaning more towards phrase structure, and a single word structure and the majority of keywords are selected from the titles and abstracts.Conclusion:The authors’ familiarity with the thesauri and controlled tools causes homogeneity in assigning keywords and also provides more precise, faster, and easier retrieval of the keywords. It's suggested that a mixture of natural and control languages to be used in this database in order to reach more comprehensive results.

Semantic medical image retrieval in a medical social network

Medical social networks have become an exchange of opinions between patients and health professionals. However, patients are anxious to quickly find a reliable analysis and a concise explanation of their medical images and express their queries through a textual description or a visual description or both sets. For this, we present in this paper a multimodal research model to research medical images based on multimedia information that is extracted from a radiological collaborative social network. Indeed, the opinions shared on a medical image in a medico-social network are a textual description which in most cases requires cleaning by using a medical thesaurus. In addition, we describe the textual description and medical image in a TF-IDF weight vector using an approach of “bag of words”. We use latent semantic analysis to establish relationships between textual terms and visual terms in shared opinions on the medical image. The multimodal modeling researches the medical information through multimodal queries. Our model is evaluated against the ImageCLEFMed’2015 baseline, which is the ground truth for our experiments. We have conducted numerous experiments with different descriptors and many combinations of modalities. The analysis of results shows that the model based on two methods can increase the performance of a research system based on a single modality, visual or textual.

Technologies for Complex Intelligent Clinical Data Analysis

The paper presents the system for intelligent analysis of clinical information. Authors describe methods implemented in the system for clinical information retrieval, intelligent diagnostics of chronic diseases, patient's features importance and for detection of hidden dependencies between features. Results of the experimental evaluation of these methods are also presented. Healthcare facilities generate a large flow of both structured and unstructured data which contain important information about patients. Test results are usually retained as structured data but some data is retained in the form of natural language texts (medical history, the results of physical examination, and the results of other examinations, such as ultrasound, ECG or X-ray studies). Many tasks arising in clinical practice can be automated applying methods for intelligent analysis of accumulated structured array and unstructured data that leads to improvement of the healthcare quality. the creation of the complex system for intelligent data analysis in the multi-disciplinary pediatric center. Authors propose methods for information extraction from clinical texts in Russian. The methods are carried out on the basis of deep linguistic analysis. They retrieve terms of diseases, symptoms, areas of the body and drugs. The methods can recognize additional attributes such as "negation" (indicates that the disease is absent), "no patient" (indicates that the disease refers to the patient's family member, but not to the patient), "severity of illness", disease course", "body region to which the disease refers". Authors use a set of hand-drawn templates and various techniques based on machine learning to retrieve information using a medical thesaurus. The extracted information is used to solve the problem of automatic diagnosis of chronic diseases. A machine learning method for classification of patients with similar nosology and the methodfor determining the most informative patients'features are also proposed. Authors have processed anonymized health records from the pediatric center to estimate the proposed methods. The results show the applicability of the information extracted from the texts for solving practical problems. The records ofpatients with allergic, glomerular and rheumatic diseases were used for experimental assessment of the method of automatic diagnostic. Authors have also determined the most appropriate machine learning methods for classification of patients for each group of diseases, as well as the most informative disease signs. It has been found that using additional information extracted from clinical texts, together with structured data helps to improve the quality of diagnosis of chronic diseases. Authors have also obtained pattern combinations of signs of diseases. The proposed methods have been implemented in the intelligent data processing system for a multidisciplinary pediatric center. The experimental results show the availability of the system to improve the quality of pediatric healthcare.

Methodolo- gical Aspects of Semantic Relationship Extraction for Automatic Thesaurus Generation

The paper is devoted to analysis of methods for automatic generation of a specialized thesaurus. The main algorithm of generation consists of three stages: selection and preprocessing of a text corpus, recognition of thesaurus terms, and extraction of relations among terms. Our work is focused on exploring methods for semantic relation extraction. We developed a test bench that allow to test well-known algorithms for extraction of synonyms and hypernyms. These algorithms are based on different relation extraction techniques: lexico-syntactic patterns, morpho-syntactic rules, measurement of term information quantity, general-purpose thesaurus WordNet, and Levenstein distance. For analysis of the result thesaurus we proposed a complex assessment that includes the following metrics: precision of extracted terms, precision and recall of hierarchical and synonym relations, and characteristics of the thesaurus graph (the number of extracted terms and semantic relationships of different types, the number of connected components, and the number of vertices in the largest component). The proposed set of metrics allows to evaluate the quality of the thesaurus as a whole, reveal some drawbacks of standard relation extraction methods, and create more efficient hybrid methods that can generate thesauri with better characteristics than thesauri generated by using separate methods. In order to illustrate this fact, one of such hybrid methods is considered in the paper. It combines the best standard algorithms for hypernym and synonym extraction and generates a specialized medical thesaurus. The hybrid method leaves the thesaurus quality on the same level and finds more relations between terms than well-known algorithms.

Explore the construction of thesaurus in the ancient literature of traditional Chinese medicine digital construction

Chinese medical thesaurus was constructed setting knowledge element as the core and knowledge classification as the basis. In making the full use of our previous traditional knowledge classification research on traditional knowledge protection of TCM, the ancient literature of TCM knowledge classification system was constructed. The construction of TCM ancient books thesaurus plays a vital role in ancient Chinese medicine resource description, intelligent retrieval and knowledge discovery. This article disscussed the necessity of constructing Chinese medical thesaurus and the problems need to pay attention to in the process of building from three points of the Chinese medical thesaurus construction idea, plan, and the relevant system construction. Key words: Ancient books of traditional Chinese medicine; Digital; Thesaurus; Build

A Proposed Framework to Enrich Norwegian EHR System with Health-trusted Information for Patients and Professionals.

In the era of social media, semantic web and big data, a huge amount of health-related information, knowledge and resources exist on the Web. Patients and healthcare professionals should spend enormous effort and time in order to find health-trusted information, while the appropriate technologies to interlink and retrieve this type of information already exist. In this paper we propose a framework to enrich DIPS, the most deployed Norwegian EHR System, with health-trusted information for patients and state-of-the-art resources for healthcare professionals. The framework based upon the new architecture of DIPS, namely DIPS Arena, and upon the interlinking with the semantic web, social media and open linked data cloud information, knowledge and resources through well-established medical thesauri like SNOMED CT and MeSH.

Multimodal medical information retrieval with unsupervised rank fusion

Modern medical information retrieval systems are paramount to manage the insurmountable quantities of clinical data. These systems empower health care experts in the diagnosis of patients and play an important role in the clinical decision process. However, the ever-growing heterogeneous information generated in medical environments poses several challenges for retrieval systems. We propose a medical information retrieval system with support for multimodal medical case-based retrieval. The system supports medical information discovery by providing multimodal search, through a novel data fusion algorithm, and term suggestions from a medical thesaurus. Our search system compared favorably to other systems in 2013 ImageCLEFMedical.

Explicitly integrating MeSH thesaurus help into health information retrieval systems: An empirical user study

When consumers search for health information, a major obstacle is their unfamiliarity with the medical terminology. Even though medical thesauri such as the Medical Subject Headings (MeSH) and related tools (e.g., the MeSH Browser) were created to help consumers find medical term definitions, the lack of direct and explicit integration of these help tools into a health retrieval system prevented them from effectively achieving their objectives. To explore this issue, we conducted an empirical study with two systems: One is a simple interface system supporting query-based searching; the other is an augmented system with two new components supporting MeSH term searching and MeSH tree browsing. A total of 45 subjects were recruited to participate in the study. The results indicated that the augmented system is more effective than the simple system in terms of improving user-perceived topic familiarity and question–answer performance, even though we did not find users spend more time on the augmented system. The two new MeSH help components played a critical role in participants’ health information retrieval and were found to allow them to develop new search strategies. The findings of the study enhanced our understanding of consumers’ search behaviors and shed light on the design of future health information retrieval systems.