AI/LEARN: An interactive videodisk system for teaching medical concepts and reasoning

1987 ◽  
Vol 11 (6) ◽  
pp. 421-429 ◽  
Author(s):  
Joyce A. Mitchell ◽  
Alex S. C. Lee ◽  
Terry TenBrink ◽  
James H. Cutts ◽  
David P. Clark ◽  
...  
Keyword(s):  
Interactive Videodisk ◽  
Medical Concepts

Quantitative and qualitative evaluation of interactive videodisk instructional modules in preclinical neuropathology education

1993 ◽  
Vol 5 (1) ◽  
pp. 3-9 ◽  
Author(s):  
Kathryn L. Lovell ◽  
Perrin E. Parkhurst ◽  
Sarah A. Sprafka ◽  
Mark W. Hodgins ◽  
Patricia Bean
Keyword(s):  
Qualitative Evaluation ◽  
Interactive Videodisk

Case-based conference in integrative medicine: A pilot project to enhance learning and exchange of different medical concepts

2009 ◽  
Vol 1 (4) ◽  
pp. 197
Author(s):  
D. Tauschel ◽  
C. Scheffer ◽  
M. Bovelet ◽  
M. Bräuer ◽  
M. Escher ◽  
...  
Keyword(s):  
Integrative Medicine ◽  
Pilot Project ◽  
Medical Concepts ◽  
Case Based

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 Smart Hospital concept and its implementation capabilities based on the incentive extension

2018 ◽  
Vol 44 ◽  
pp. 00040
Author(s):  
Oksana Ilyashenko ◽  
Igor Ilin ◽  
Dmitry Kurapeev
Keyword(s):  
Reference Model ◽  
Health Organizations ◽  
Digital Space ◽  
Smart Hospital ◽  
Innovative Management ◽  
Preparatory Stage ◽  
The Innovative Management ◽  
Health Organization ◽  
Medical Concepts ◽  
Specific Health

Currently, the health care as one of the priority development areas for the state requires special attention in the transition to the innovative management models based on the use of advanced medical concepts and digital technologies. Therefore, the transition to the SmartHospital model and the formation of an appropriate architectural solution become actual for health organizations which strategic task is the transition to the digital space and strengthening their positions in the medical services market. Prior to starting the development of the Smart Hospital architectural model as applied to the specific health organization, it is advisable to use a reference model that will take into account the basic business and IT requirements of the organization, limits and used technologies. The creation of the reference SmartHospital architectural solution is preceded by a preparatory stage that allows revealing the architecture and limit requirements. The article proposes the incentive extension on the basis of which the requirements and limits are formulated for the reference model of the SmartHospital architectural solution.

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.

scholarly journals ULisboa: Recognition and Normalization of Medical Concepts

2015 ◽  
Author(s):  
André Leal ◽  
Bruno Martins ◽  
Francisco Couto
Keyword(s):  
Medical Concepts

scholarly journals Can we trace medical concepts in the Philosophy of Plato?

2021 ◽  
Vol 3 (3) ◽  
pp. 14
Author(s):  
Christos Tsagkaris ◽  
Marko Dorosh ◽  
Dimitrios V. ◽  
Anna Loudovikou ◽  
Andreas S.
Keyword(s):  
Medical Concepts

scholarly journals Mesothelioma: one disease, many definitions. Comparative linguistic analysis in a “health literacy” perspective

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Antonio Maconi ◽  
Mariateresa Dacquino ◽  
Federica Viazzi ◽  
Emanuela Bovo ◽  
Federica Grosso ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Health Literacy ◽  
English Language ◽  
Syntactic Structure ◽  
Basic Knowledge ◽  
Patient Portal ◽  
Medical Field ◽  
Definition Of ◽  
The Uk ◽  
Medical Concepts

Objectives: The aim of this paper is to demonstrate how, while remaining within a specific field such as medicine, it is possible to use different languages depending on the target audience (doctors, professionals from other fields or patients) in order to improve its degree of health literacy. In particular, the aim is to show how even the definition of a disease, which should in principle be unambiguous, can in fact be linguistically adapted to the reader's basic knowledge. Methodology: Five definitions of mesothelioma are examined, analysed lexically, syntactically and graphically. Specifically, this comparison is made on three main levels, which in turn have different nuances: popular, including definitions from Wikipedia and the UK Mesothelioma patient portal; intermediate, corresponding to the Collins English language dictionary; and specialist, with definitions from the MeSH thesaurus and the Orphanet database. Results: At the end of the comparative analysis, it is possible to state that in linguistic and Health Literacy terms there is no single definition for this rare disease but as many definitions as there are targets. In particular, they vary in syntactic structure, graphic form and vocabulary, as they have to use technicalities typical of the medical field but have different nuances of complexity. Conclusion: A comparison of the definitions shows that the degree of readability does not always correspond to that of comprehensibility. The analysis demonstrates that it is difficult to explain complex medical concepts to practitioners and patients in a simple, clear and usable way and that this requires specific techniques of Health Literacy, related to both the linguistic and graphic aspects. The comparison of definitions is therefore a methodological premise for the creation of brochures dedicated to mesothelioma and the revision of the "Mai soli" site for mesothelioma patients.

scholarly journals Health Effects Associated With Electronic Cigarette Use: Automated Mining of Online Forums (Preprint)

2019 ◽  
Author(s):  
My Hua ◽  
Shouq Sadah ◽  
Vagelis Hristidis ◽  
Prue Talbot
Keyword(s):  
Health Effects ◽  
Electronic Cigarettes ◽  
Health Effect ◽  
Online Information ◽  
Online Forums ◽  
Unified Medical Language System ◽  
Organ Systems ◽  
Negative Sentiment ◽  
Specific Symptoms ◽  
Medical Concepts

BACKGROUND Our previous infodemiological study was performed by manually mining health-effect data associated with electronic cigarettes (ECs) from online forums. Manual mining is time consuming and limits the number of posts that can be retrieved. OBJECTIVE Our goal in this study was to automatically extract and analyze a large number (>41,000) of online forum posts related to the health effects associated with EC use between 2008 and 2015. METHODS Data were annotated with medical concepts from the Unified Medical Language System using a modified version of the MetaMap tool. Of over 1.4 million posts, 41,216 were used to analyze symptoms (undiagnosed conditions) and disorders (physician-diagnosed terminology) associated with EC use. For each post, sentiment (positive, negative, and neutral) was also assigned. RESULTS Symptom and disorder data were categorized into 12 organ systems or anatomical regions. Most posts on symptoms and disorders contained negative sentiment, and affected systems were similar across all years. Health effects were reported most often in the neurological, mouth and throat, and respiratory systems. The most frequently reported symptoms and disorders were headache (n=939), coughing (n=852), malaise (n=468), asthma (n=916), dehydration (n=803), and pharyngitis (n=565). In addition, users often reported linked symptoms (eg, coughing and headache). CONCLUSIONS Online forums are a valuable repository of data that can be used to identify positive and negative health effects associated with EC use. By automating extraction of online information, we obtained more data than in our prior study, identified new symptoms and disorders associated with EC use, determined which systems are most frequently adversely affected, identified specific symptoms and disorders most commonly reported, and tracked health effects over 7 years.

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