Opisthorchis viverrini (O. viverrini) infection is the primary cause of cholangiocarcinoma (CCA) and a major public health challenge along the Mekong River in Thailand, Vietnam, Laos PDR, Cambodia, China and Myanmar. This systematic review appraised the risk factors for O. viverrini infection. Literature searches were conducted using Medical Subject Headings (MeSH) and keywords, without date or language restriction, in PubMed, EMBASE, Global Health, and Thai Journals Online. References from relevant papers also were reviewed to expand the scope of the search. The inclusion criteria were human subjects. The primary outcome was O. viverrini infection. The exclusion criteria were in vitro, animal, genetic research, and systematic reviews. All included studies were summarized and reported as follows: study design, age, sample size, setting, data collection and fecal examination methods, adjusted odds ratio and 95% confidence interval, significant risk factors, and other findings. The search results show that across all databases 1,098 records were identified. Twenty-four articles were included in the systematic review, consisting of cross-sectional studies (79.2%), cohort studies (12.5%), and case-control studies (8.3%). The majority of study settings were in Thailand (75%). The People's Democratic Republic of Laos (Lao PDR) accounted for the second greatest number of studies (20.8%), and 4.2% of the studies originated in Vietnam. Key findings included demographic, environmental, geographic, health behavior, treatment with praziquantel, and a history of O. viverrini infection that was significantly associated with O. viverrini infection. Health professionals should investigate the potential risk factors for the disease and should seek and develop innovative methods for prevention and control of O. viverrini infection in these countries.

Introduction:Telemedicine is an effective means of delivering health care in Southeast Asian (SEA) countries. This systematic review explored the use of telemedicine systems for delivering health care services in SEA countries during the past 10 years. Methods:Literature searches were conducted in PubMed, Embase, Medline, Global Health, and CENTRAL (Cochrane Central Register of Controlled Trials). The inclusion criteria were as follows: (1) studies published between 2010 and 2021; (2) study settings located in SEA countries; (3) articles published in English; and (4) availability of a full-text version of the article. Information was extracted and evaluated for each study based on quality and risk of bias. Results:Thirty-seven of 6,554 records were eligible for inclusion. Studies included based on percentage were descriptive (29.73%), cost-effective (8.11%), randomized controlled trials (5.41%), and mixed methods (2.7%). Teleophthalmology and teleconsultation were the major reasons for using telemedicine, representing 21.62% of all studies. A hub-and-spoke and store-and-forward models were used. Free messenger applications supported communication modalities in or out of the systems. Discussion:The COVID-19 (coronavirus disease 2019) pandemic increased research studies on telemedicine, with most studies occurring in Singaporean hospitals (49%). Descriptive studies predominated, followed by retrospective and cross-sectional studies. Conclusions:Our findings demonstrated that telemedicine was a powerful tool. It is feasible, safe, effective, and less expensive than traditional methods. However, robust research is needed to fully investigate telemedicine systems in SEA countries.

SNOMED CT is a large concept-based terminology designed according to epistemic, semantic and pragmatic principles relevant to clinicians. Its goal is structured clinical reporting in electronic healthcare records (EHRs). The Basic Formal Ontology (BFO) is an ontology designed on the basis of types claimed to exist in reality based on a domain-independent ontological theory. Its goal is faithful representation of reality within that theory. The Ontology for General Medical Science (OGMS) extends the BFO by providing definitions for types relevant within the clinical domain. Combining SNOMED CT with the ontological rigor of BFO and OGMS might improve clinical reporting by, f.i., preventing data entry mistakes and inconsistencies, and make EHRs more comparable. To that end, we are developing a logical framework capable of exploiting what SNOMED CT offers terminologically and realism-based ontologies such as the BFO and the OGMS ontologically by means of bridging axioms compatible with the BFO, and expressed in the same CLIF-dialect as used in its axiomatization in first order logic. In this paper, we report on our attempts to detect in the combinations of binary relations that are used in the definition of SNOMED CT’s definitions of disorder concepts patterns which might at least partially automate the construction of such axioms. Our findings suggest that this partial automation is indeed possible, but to a smaller extent than we had hoped for. We compare our approach with a recent proposal that seeks to bring SNOMED CT and BFO closer together by reinterpreting SNOMED CT disorders as clinical occurrents. The proposal has its merit in providing a realist underpinning for that part of SNOMED CT’s concept model in terms of the BFO, but is not discriminatory enough for an automatic translation into OGMS. Key problem is the lack of face validity of SNOMED CT disorder terms as compared to the formal definitions they are given and this in absence of textual definitions.

In a proof of concept study, we assessed the feasibility of designing a first-order logic (FOL) framework capable of translating SNOMED CT's terminological view on patient data as referencing concepts, into the realism-based view of the Basic Formal Ontology and the Ontology for General Medical Science according to which patient data represent instances of types. Because within the subject domain of this study, SNOMED CT's terminological coverage was excellent, and its EL++ axioms can be automatically translated into FOL as well as the antecedent part of bridging axioms between SNOMED CT and realism-based ontologies, we conclude that this is an area of R&D that deserves further attention and that may lead to new ways of federating terminologies with ontologies.

BACKGROUND Cholangiocarcinoma (CCA) is a critical public health problem in Thailand. The prevalence is much higher than other areas in the world. Data about CCA are stored in different data sources and standards between research-based data collection and electronic health records (EHR). OBJECTIVE This study aims to integrates and analyzes cholangiocarcinoma (CCA) data from various sources to investigate risk factors and develop prediction models using the Cholangiocarcinoma Ontology (CCAO). METHODS Datasets from Thailand were annotated with CCAO and analyzed using ontology-based term enrichment methods. We apply ontology term enrichment analysis, similar to that used with the Gene Ontology, for identifying significant risk factors for suspected CCA and patients with CCA. Our program provides a list of significant terms associated with CCA and a visualization of ontology hierarchy with significant terms highlighted. The outputs of the term enrichment analyses have been used as the inputs to machine learning classification tasks. RESULTS The results confirmed that indicators for CCA include dilated bile ducts, periductal fibrosis, and hepatic mass, based on ultrasound findings from several years prior. Our analysis also revealed demographic and lifestyle risk factors such as male gender, having no education, alcohol consumption, smoking, being a farmer, and having diabetes. We seeded a random forest classifier with the term enrichment results and predicted CCA patients with average 0.92 precision-recall curve score (0.023 standard deviation) with age, dilated bile ducts, periductal fibrosis, suspected CCA, and hepatic mass as the top five important features. CONCLUSIONS These findings can be used to focus and monitor populations at risk for CCA. Expanding CCAO with molecular data related to CCA using ontology-driven term enrichment analysis and machine learning will help us to discover new hypotheses to decrease the morbidity and mortality of CCA in Thailand.