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Healthcare knowledge acquisition: an ontology-based approach using the extensible markup language (XML)

Cheah YN, Abidi SS

Stud Health Technol Inform, 2000;77:827-31.
PMID: 11187669

The healthcare enterprise requires a great deal of knowledge to maintain premium efficiency in the delivery of quality healthcare. We employ Knowledge Management based knowledge acquisition strategies to procure 'tacit' healthcare knowledge from experienced healthcare practitioners. Situational, problem-specific Scenarios are proposed as viable knowledge acquisition and representation constructs. We present a healthcare Tacit Knowledge Acquisition Info-structure (TKAI) that allows remote healthcare practitioners to record their tacit knowledge. TKAI employs (a) ontologies for standardisation of tacit knowledge and (b) XML to represent scenario instances for their transfer over the Internet to the server-side Scenario-Base and for the global sharing of acquired tacit healthcare knowledge.

Matched MeSH terms: Vocabulary, Controlled*
From a Categorical Structure to a Concept Model: The International Classification of Health Interventions (ICHI)

Rodrigues JM, Kim S, Aljunid S, Lee JJ, Ten Napel H, Trombert B

Stud Health Technol Inform, 2017;245:1334.
PMID: 29295415

The International Classification of Health Interventions (ICHI) alpha2 2016 Section 1 Interventions on Body Systems and Functions is based on ISO 1828 international standard named categorial Structure (CAST). This is not sufficient to represent the meaning of ICD9-CM Volume 3 labels. We propose to modify it by using the SNOMED CT concept model.

Matched MeSH terms: Vocabulary, Controlled
Fulltext Semantic representation of monogenean haptoral Bar image annotation

Abu A, Susan LL, Sidhu AS, Dhillon SK

BMC Bioinformatics, 2013;14:48.
PMID: 23398696 DOI: 10.1186/1471-2105-14-48

Digitised monogenean images are usually stored in file system directories in an unstructured manner. In this paper we propose a semantic representation of these images in the form of a Monogenean Haptoral Bar Image (MHBI) ontology, which are annotated with taxonomic classification, diagnostic hard part and image properties. The data we used are basically of the monogenean species found in fish, thus we built a simple Fish ontology to demonstrate how the host (fish) ontology can be linked to the MHBI ontology. This will enable linking of information from the monogenean ontology to the host species found in the fish ontology without changing the underlying schema for either of the ontologies.

Matched MeSH terms: Vocabulary, Controlled
miRNA for the assessment of lymph node metastasis in patients with oral squamous cell carcinoma: Systematic review and metanalysis

Jadhav KB, Nagraj SK, Arora S

J Oral Pathol Med, 2020 Nov 21.
PMID: 33220092 DOI: 10.1111/jop.13134

BACKGROUND: miRNA is one of the advanced epigenetic molecular markers correlating with lymph node metastasis in patients with Oral squamous cell carcinoma (OSCC). Numerous published papers are showing correlation of miRNA with metastasis. There is a need to analyze and validate such correlation.
METHOD: English language literature in major databases from the last 20 years was searched using controlled vocabulary and keywords. Strict inclusion and exclusion criteria were followed for selection of studies. The quality assessment was done as per the QUADAS tool 2 by three independent reviewers. The metanalysis was performed by using random effect model. Standardized mean difference (SMD) was considered as the effect measure. Statistical software used was STATA version 13.1.
RESULTS: With all inclusion and exclusion criteria, eight studies could qualify for metanalysis. The pooled estimate is found to be 0.13 (-0.35, 0.62), P = .585, which is statistically not significant. This indicates that there is a no significant difference in the fold change between metastasis and no metastasis groups. P-value of chi-square statistic for heterogeneity is

Matched MeSH terms: Vocabulary, Controlled
Fulltext Role of OpenEHR as an open source solution for the regional modelling of patient data in obstetrics

Pahl C, Zare M, Nilashi M, de Faria Borges MA, Weingaertner D, Detschew V, et al.

J Biomed Inform, 2015 Jun;55:174-87.
PMID: 25900270 DOI: 10.1016/j.jbi.2015.04.004

This work investigates, whether openEHR with its reference model, archetypes and templates is suitable for the digital representation of demographic as well as clinical data. Moreover, it elaborates openEHR as a tool for modelling Hospital Information Systems on a regional level based on a national logical infrastructure. OpenEHR is a dual model approach developed for the modelling of Hospital Information Systems enabling semantic interoperability. A holistic solution to this represents the use of dual model based Electronic Healthcare Record systems. Modelling data in the field of obstetrics is a challenge, since different regions demand locally specific information for the process of treatment. Smaller health units in developing countries like Brazil or Malaysia, which until recently handled automatable processes like the storage of sensitive patient data in paper form, start organizational reconstruction processes. This archetype proof-of-concept investigation has tried out some elements of the openEHR methodology in cooperation with a health unit in Colombo, Brazil. Two legal forms provided by the Brazilian Ministry of Health have been analyzed and classified into demographic and clinical data. LinkEHR-Ed editor was used to read, edit and create archetypes. Results show that 33 clinical and demographic concepts, which are necessary to cover data demanded by the Unified National Health System, were identified. Out of the concepts 61% were reused and 39% modified to cover domain requirements. The detailed process of reuse, modification and creation of archetypes is shown. We conclude that, although a major part of demographic and clinical patient data were already represented by existing archetypes, a significant part required major modifications. In this study openEHR proved to be a highly suitable tool in the modelling of complex health data. In combination with LinkEHR-Ed software it offers user-friendly and highly applicable tools, although the complexity built by the vast specifications requires expert networks to define generally excepted clinical models. Finally, this project has pointed out main benefits enclosing high coverage of obstetrics data on the Clinical Knowledge Manager, simple modelling, and wide network and support using openEHR. Moreover, barriers described are enclosing the allocation of clinical content to respective archetypes, as well as stagnant adaption of changes on the Clinical Knowledge Manager leading to redundant efforts in data contribution that need to be addressed in future works.

Matched MeSH terms: Vocabulary, Controlled*