A health terminological system for inherited retinal diseases: Content coverage evaluation and a proposed classification.

PURPOSE: To present a classification of inherited retinal diseases (IRDs) and evaluate its content coverage in comparison with common standard terminology systems. METHODS: In this comparative cross-sectional study, a panel of subject matter experts annotated a list of IRDs based on a comprehensive review of the literature. Then, they leveraged clinical terminologies from various reference sets including Unified Medical Language System (UMLS), Online Mendelian Inheritance in Man (OMIM), International Classification of Diseases (ICD-11), Systematized Nomenclature of Medicine (SNOMED-CT) and Orphanet Rare Disease Ontology (ORDO). RESULTS: Initially, we generated a hierarchical classification of 62 IRD diagnosis concepts in six categories. Subsequently, the classification was extended to 164 IRD diagnoses after adding concepts from various standard terminologies. Finally, 158 concepts were selected to be classified into six categories and genetic subtypes of 412 cases were added to the related concepts. UMLS has the greatest content coverage of 90.51% followed respectively by SNOMED-CT (83.54%), ORDO (81.01%), OMIM (60.76%), and ICD-11 (60.13%). There were 53 IRD concepts (33.54%) that were covered by all five investigated systems. However, 2.53% of the IRD concepts in our classification were not covered by any of the standard terminologies. CONCLUSIONS: This comprehensive classification system was established to organize IRD diseases based on phenotypic and genotypic specifications. It could potentially be used for IRD clinical documentation purposes and could also be considered a preliminary step forward to developing a more robust standard ontology for IRDs or updating available standard terminologies. In comparison, the greatest content coverage of our proposed classification was related to the UMLS Metathesaurus.

Discovering Rules from a National Exam Repository: A Use Case for Data Analysis from Iranian Medical Schools Entry Exam.

Many methods have been studied to analyze and interpret patterns and relationships that are embedded in the database to discover new knowledge in educational systems. Association rule mining is a type of data mining that identifies relationships among elements of the dataset. However, because these methods often generate various rules including non-significant ones, it is important to identify the most useful rules. Therefore, evaluating and ranking rules has become a topic of interest in the decision-making process in order to represent the level of usefulness of rules. We incorporated Apriori and Eclat algorithms on an educational dataset of a national medical exam in Iran. The aim of this study is to identify the usefulness of the extracted rules. This method can reliably discover new knowledge by interpreting the prioritized rules. The results show that those who have Scored in the highest category, i.e. [407,493], are accepted and who have scored in the lowest category, i.e. [150,236), are not accepted in the exam regardless of others features. Although, the rules that implication Accept=0 occurs, find out with high confidence, due to a large number of samples in this case. The ranking rules show this method is effective in the identification of insignificant rules that have no effect on decision making.

The First Inherited Retinal Disease Registry in Iran: Research Protocol and Results of a Pilot Study.

BACKGROUND: To describe the protocol for developing a national inherited retinal disease (IRD) registry in Iran and present its initial report. METHODS: This community-based participatory research was approved by the Ministry of Health and Medical Education of Iran in 2016. To provide the minimum data set (MDS), several focus group meetings were held. The final MDS was handed over to an engineering team to develop a web-based software. In the pilot phase, the software was set up in two referral centers in Iran. Final IRD diagnosis was made based on clinical manifestations and genetic findings. Ultimately, patient registration was done based on all clinical and non-clinical manifestations. RESULTS: Initially, a total of 151 data elements were approved with Delphi technique. The registry software went live at www. IRDReg.org based on DHIS2 open source license agreement since February 2016. So far, a total of 1001 patients have been registered with a mean age of 32.41±15.60 years (range, 3 months to 74 years). The majority of the registered patients had retinitis pigmentosa (42%, 95% CI: 38.9% to 45%). Genetic testing was done for approximately 20% of the registered individuals. CONCLUSION: Our study shows successful web-based software design and data collection as a proof of concept for the first IRD registry in Iran. Multicenter integration of the IRD registry in medical centers throughout the country is well underway as planned. These data will assist researchers to rapidly access information about the distribution and genetic patterns of this disease.

Concept based auto-assignment of healthcare questions to domain experts in online Q&A communities.

BACKGROUND: Healthcare consumers are increasingly turning to the online health Q&A communities to seek answers for their questions because current general search engines are unable to digest complex health-related questions. Q&A communities are platforms where users ask unstructured questions from different healthcare topics. OBJECTIVES: This study aimed to provide a concept-based approach to automatically assign health questions to the appropriate domain experts. METHODS: We developed three processes for (1) expert profiling, (2) question analysis and (3) similarity calculation and assignment. Semantic weight of concepts combined with TF-IDF weighting comprised vectors of concepts as expert profiles. Subsequently, the similarity between submitted questions and expert profiles was calculated to find a relevant expert. RESULTS: We randomly selected 345 questions posted by consumers for 38 experts in 13 health topics from NetWellness as input data. Our results showed the precision and recall of our proposed method for the studied topics were between 63 %-92 % and 61 %-100 %, respectively. The calculated F-measure in selected topics was between 62 % (Addiction and Substance Abuse) and 94 % (Eye and Vision Care) with a combined F-measure of 80 %. CONCLUSIONS: Concept-based methods using unified medical language system and natural language processing techniques could automatically assign actual health questions in different topics to the relevant domain experts with good performance metrics.

Similarity of medical concepts in question and answering of health communities.

The ability to automatically categorize submitted questions based on topics and suggest similar question and answer to the users reduces the number of redundant questions. Our objective was to compare intra-topic and inter-topic similarity between question and answers by using concept-based similarity computing analysis. We gathered existing question and answers from several popular online health communities. Then, Unified Medical Language System concepts related to selected questions and experts in different topics were extracted and weighted by term frequency -inverse document frequency values. Finally, the similarity between weighted vectors of Unified Medical Language System concepts was computed. Our result showed a considerable gap between intra-topic and inter-topic similarities in such a way that the average of intra-topic similarity (0.095, 0.192, and 0.110, respectively) was higher than the average of inter-topic similarity (0.012, 0.025, and 0.018, respectively) for questions of the top 3 popular online communities including NetWellness, WebMD, and Yahoo Answers. Similarity scores between the content of questions answered by experts in the same and different topics were calculated as 0.51 and 0.11, respectively. Concept-based similarity computing methods can be used in developing intelligent question and answering retrieval systems that contain auto recommendation functionality for similar questions and experts.

Implementing an interface terminology for structured clinical documentation.

Clinically oriented interface terminologies support interactions between humans and computer programs that accept structured entry of healthcare information. This manuscript describes efforts over the past decade to introduce an interface terminology called CHISL (Categorical Health Information Structured Lexicon) into clinical practice as part of a computer-based documentation application at Vanderbilt University Medical Center. Vanderbilt supports a spectrum of electronic documentation modalities, ranging from transcribed dictation, to a partial template of free-form notes, to strict, structured data capture. Vanderbilt encourages clinicians to use what they perceive as the most appropriate form of clinical note entry for each given clinical situation. In this setting, CHISL occupies an important niche in clinical documentation. This manuscript reports challenges developers faced in deploying CHISL, and discusses observations about its usage, but does not review other relevant work in the field.

Identification of pre-coordinated term candidates in a Cardiology Outpatient Service.

Structured data entry systems have been used to facilitate detailed categorical entries which may be subsequently used for computer-assisted decision support. While these highly organized entry systems may encourage providers to document clinical findings more precisely, the detailed nature of these entries may prove more time consuming than traditional data collection systems. We retrospectively examine results entered in our structured entry system in this study for pre-coordination opportunities as a potential enhancement to the system.

The prevalence and inaccessibility of Internet references in the biomedical literature at the time of publication.

OBJECTIVES: To determine the prevalence and inaccessibility of Internet references in the bibliography of biomedical publications when first released in PubMed. METHODS: During a one-month observational study period (Feb 21 to Mar 21, 2006) the Internet citations from a 20% random sample of all forthcoming publications released in PubMed during the previous day were identified. Attempts to access the referenced Internet citations were completed within one day and inaccessible Internet citations were recorded. RESULTS: The study included 4,699 publications from 844 different journals. Among the 141,845 references there were 840 (0.6%) Internet citations. One or more Internet references were cited in 403 (8.6%) articles. From the 840 Internet references, 11.9% were already inaccessible within two days after an article's release to the public. CONCLUSION: The prevalence of Internet citations in journals included in PubMed is small (<1%); however, the inaccessibility rate at the time of publication is considered substantial. Authors, editors, and publishers need to take responsibility for providing accurate and accessible Internet references.

Prevalence and inaccessibility of URLs in the biomedical literature.

The World Wide Web is a dynamic environment that does not guarantee permanent access or content stability. We determined the prevalence of URLs in forthcoming, biomedical papers when they are first released in MEDLINE(R) and prospectively evaluated the rate of inaccessible URLs during a 19-day period. Among 96,153 references from 2,614 forthcoming papers (739 journals) the prevalence of URLs was 0.59%. The rate of inaccessible URLs was 12.4% when first available to the public community.

Factors affecting the sustainability of information technology applications in health care.

Biomedical informatics is a relatively new field; sustainability of information technology applications has not been studied in detail. We examined what factors contribute to sustainability in other fields (ecology, construction materials, business, primary health care, and environment and development). We describe some aspects of sustainability that can be applied to biomedical informatics: effectiveness, efficiency, financial viability, reproducibility, and portability.