Forma mentis networks quantify crucial differences in STEM perception between students and experts.

PLoS One. 2019;14(10):e0222870

Authors: Stella M, de Nigris S, Aloric A, Siew CSQ

Abstract
In order to investigate how high school students and researchers perceive science-related (STEM) subjects, we introduce forma mentis networks. This framework models how people conceptually structure their stance, mindset or forma mentis toward a given topic. In this study, we build forma mentis networks revolving around STEM and based on psycholinguistic data, namely free associations of STEM concepts (i.e., which words are elicited first and associated by students/researchers reading "science"?) and their valence ratings concepts (i.e., is "science" perceived as positive, negative or neutral by students/researchers?). We construct separate networks for (Ns = 159) Italian high school students and (Nr = 59) interdisciplinary professionals and researchers in order to investigate how these groups differ in their conceptual knowledge and emotional perception of STEM. Our analysis of forma mentis networks at various scales indicate that, like researchers, students perceived "science" as a strongly positive entity. However, differently from researchers, students identified STEM subjects like "physics" and "mathematics" as negative and associated them with other negative STEM-related concepts. We call this surrounding of negative associations a negative emotional aura. Cross-validation with external datasets indicated that the negative emotional auras of physics, maths and statistics in the students' forma mentis network related to science anxiety. Furthermore, considering the semantic associates of "mathematics" and "physics" revealed that negative auras may originate from a bleak, dry perception of the technical methodology and mnemonic tools taught in these subjects (e.g., calculus rules). Overall, our results underline the crucial importance of emphasizing nontechnical and applied aspects of STEM disciplines, beyond purely methodological teaching. The quantitative insights achieved through forma mentis networks highlight the necessity of establishing novel pedagogic and interdisciplinary links between science, its real-world complexity, and creativity in science learning in order to enhance the impact of STEM education, learning and outreach activities.

PMID: 31622351 [PubMed - in process]

THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images.

PLoS One. 2019;14(10):e0223792

Authors: Hebart MN, Dickter AH, Kidder A, Kwok WY, Corriveau A, Van Wicklin C, Baker CI

Abstract
In recent years, the use of a large number of object concepts and naturalistic object images has been growing strongly in cognitive neuroscience research. Classical databases of object concepts are based mostly on a manually curated set of concepts. Further, databases of naturalistic object images typically consist of single images of objects cropped from their background, or a large number of naturalistic images of varying quality, requiring elaborate manual image curation. Here we provide a set of 1,854 diverse object concepts sampled systematically from concrete picturable and nameable nouns in the American English language. Using these object concepts, we conducted a large-scale web image search to compile a database of 26,107 high-quality naturalistic images of those objects, with 12 or more object images per concept and all images cropped to square size. Using crowdsourcing, we provide higher-level category membership for the 27 most common categories and validate them by relating them to representations in a semantic embedding derived from large text corpora. Finally, by feeding images through a deep convolutional neural network, we demonstrate that they exhibit high selectivity for different object concepts, while at the same time preserving variability of different object images within each concept. Together, the THINGS database provides a rich resource of object concepts and object images and offers a tool for both systematic and large-scale naturalistic research in the fields of psychology, neuroscience, and computer science.

PMID: 31613926 [PubMed - in process]

Ten years of knowledge representation for health care (2009-2018): Topics, trends, and challenges.

Artif Intell Med. 2019 Sep;100:101713

Authors: Riaño D, Peleg M, Ten Teije A

Abstract
BACKGROUND: In the last ten years, the international workshop on knowledge representation for health care (KR4HC) has hosted outstanding contributions of the artificial intelligence in medicine community pertaining to the formalization and representation of medical knowledge for supporting clinical care. Contributions regarding modeling languages, technologies and methodologies to produce these models, their incorporation into medical decision support systems, and practical applications in concrete medical settings have been the main contributions and the basis to define the evolution of this field across Europe and worldwide.
OBJECTIVES: Carry out a review of the papers accepted in KR4HC in the 2009-2018 decade, analyze and characterize the topics and trends within this field, and identify challenges for the evolution of the area in the near future.
METHODS: We reviewed the title, the abstract, and the keywords of the 112 papers that were accepted to the workshop, identified the medical and technological topics involved in these works, provided a classification of these papers in medical and technological perspectives and obtained the timeline of these topics in order to determine interest growths and declines. The experience of the authors in the field and the evidences after the review were the basis to propose a list of challenges of knowledge representation in health care for the future.
RESULTS: The most generic knowledge representation methods are ontologies (31%), semantic web related formalisms (26%), decision tables and rules (19%), logic (14%), and probabilistic models (10%). From a medical informatics perspective, knowledge is mainly represented as computer interpretable clinical guidelines (43%), medical domain ontologies (26%), and electronic health care records (22%). Within the knowledge lifecycle, contributions are found in knowledge generation (38%), knowledge specification (24%), exception detection and management (12%), knowledge enactment (8%), temporal knowledge and reasoning (7%), and knowledge sharing and maintenance (7%). The clinical emphasis of knowledge is mainly related to clinical treatments (27%), diagnosis (13%), clinical quality indicators (13%), and guideline integration for multimorbid patients (12%). According to the level of development of the works presented, we distinguished four maturity levels: formal (22%), implementation (52%), testing (13%), and deployment (2%) levels. Some papers described technologies for specific clinical issues or diseases, mainly cancer (22%) and diseases of the circulatory system (20%). Chronicity and comorbidity were present in 10% and 8% of the papers, respectively.
CONCLUSIONS: KR4HC is a stable community, still active after ten years. A persistent focus has been knowledge representation, with an emphasis on semantic-web ontologies and on clinical-guideline based decision-support. Among others, two topics receive growing attention: integration of computer-interpretable guideline knowledge for the management of multimorbidity patients, and patient empowerment and patient-centric care.

PMID: 31607346 [PubMed - in process]

EAGLE-A Scalable Query Processing Engine for Linked Sensor Data.

Sensors (Basel). 2019 Oct 09;19(20):

Authors: Nguyen Mau Quoc H, Serrano M, Mau Nguyen H, G Breslin J, Le-Phuoc D

Abstract
Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio-temporal correlations. Most semantic approaches do not have spatio-temporal support. Some of them have attempted to provide full spatio-temporal support, but have poor performance for complex spatio-temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio-temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio-temporal computing in the linked sensor data context.

PMID: 31600957 [PubMed - in process]

SEAweb: the small RNA Expression Atlas web application.

Nucleic Acids Res. 2019 Oct 10;:

Authors: Rahman RU, Liebhoff AM, Bansal V, Fiosins M, Rajput A, Sattar A, Magruder DS, Madan S, Sun T, Gautam A, Heins S, Liwinski T, Bethune J, Trenkwalder C, Fluck J, Mollenhauer B, Bonn S

Abstract
We present the Small RNA Expression Atlas (SEAweb), a web application that allows for the interactive querying, visualization and analysis of known and novel small RNAs across 10 organisms. It contains sRNA and pathogen expression information for over 4200 published samples with standardized search terms and ontologies. In addition, SEAweb allows for the interactive visualization and re-analysis of 879 differential expression and 514 classification comparisons. SEAweb's user model enables sRNA researchers to compare and re-analyze user-specific and published datasets, highlighting common and distinct sRNA expression patterns. We provide evidence for SEAweb's fidelity by (i) generating a set of 591 tissue specific miRNAs across 29 tissues, (ii) finding known and novel bacterial and viral infections across diseases and (iii) determining a Parkinson's disease-specific blood biomarker signature using novel data. We believe that SEAweb's simple semantic search interface, the flexible interactive reports and the user model with rich analysis capabilities will enable researchers to better understand the potential function and diagnostic value of sRNAs or pathogens across tissues, diseases and organisms.

PMID: 31598718 [PubMed - as supplied by publisher]

Fine Subdivisions of the Semantic Network Supporting Social and Sensory-Motor Semantic Processing.

Cereb Cortex. 2018 08 01;28(8):2699-2710

Authors: Lin N, Wang X, Xu Y, Wang X, Hua H, Zhao Y, Li X

Abstract
Neuroimaging studies have consistently indicated that semantic processing involves a brain network consisting of multimodal cortical regions distributed in the frontal, parietal, and temporal lobes. However, little is known about how semantic information is organized and processed within the network. Some recent studies have indicated that sensory-motor semantic information modulates the activation of this network. Other studies have indicated that this network responds more to social semantic information than to other information. Using fMRI, we collectively investigated the brain activations evoked by social and sensory-motor semantic information by manipulating the sociality and imageability of verbs in a word comprehension task. We detected 2 subgroups of brain regions within the network showing sociality and imageability effects, respectively. The 2 subgroups of regions are distinct but overlap in bilateral angular gyri and adjacent middle temporal gyri. A follow-up analysis of resting-state functional connectivity showed that dissociation of the 2 subgroups of regions is partially associated with their intrinsic functional connectivity differences. Additionally, an interaction effect of sociality and imageability was observed in the left anterior temporal lobe. Our findings indicate that the multimodal cortical semantic network has fine subdivisions that process and integrate social and sensory-motor semantic information.

PMID: 28633369 [PubMed - indexed for MEDLINE]

A Pervasive Healthcare System for COPD Patients.

Diagnostics (Basel). 2019 Oct 01;9(4):

Authors: Ajami H, Mcheick H, Mustapha K

Abstract
Chronic obstructive pulmonary disease (COPD) is one of the most severe public health problems worldwide. Pervasive computing technology creates a new opportunity to redesign the traditional pattern of medical system. While many pervasive healthcare systems are currently found in the literature, there is little published research on the effectiveness of these paradigms in the medical context. This paper designs and validates a rule-based ontology framework for COPD patients. Unlike conventional systems, this work presents a new vision of telemedicine and remote care solutions that will promote individual self-management and autonomy for COPD patients through an advanced decision-making technique. Rules accuracy estimates were 89% for monitoring vital signs, and environmental factors, and 87% for nutrition facts, and physical activities.

PMID: 31581453 [PubMed]

Technical Note: Ontology-guided Radiomics Analysis Workflow (O-RAW).

Med Phys. 2019 Oct 03;:

Authors: Shi Z, Traverso A, van Soest J, Dekker A, Wee L

Abstract
PURPOSE: Radiomics is the process to automate tumour feature extraction from medical images. This has shown potential for quantifying the tumour phenotype and predicting treatment response. The three major challenges of radiomics research and clinical adoption are: (i) lack of standardized methodology for radiomics analyses, (ii) lack of a universal lexicon to denote features that are semantically equivalent, and (iii) lists of feature values alone do not sufficiently capture the details of feature extraction that might nonetheless strongly affect feature values (e.g. image normalization or interpolation parameters). These barriers hamper multi-centre validation studies applying subtly different imaging protocols, pre-processing steps and radiomics software. We propose an open-source Ontology-guided Radiomics Analysis Workflow (O-RAW) to address the above challenges in the following manner: (i) distributing a free and open-source software package for radiomics analysis, (ii) deploying a standard lexicon to uniquely describe features in common usage and (iii) provide methods to publish radiomic features as a semantically-interoperable data graph object complying to FAIR (Findable Accessible Interoperable Reusable) data principles.
METHODS: O-RAW was developed in Python, and has three major modules using open-source component libraries (PyRadiomics Extension and PyRadiomics). First, PyRadiomics Extension takes standard DICOM-RT (Radiotherapy) input objects (i.e. a DICOM series and an RTSTRUCT file) and parses them as arrays of voxel intensities and a binary mask corresponding to a volume of interest (VOI). Next, these arrays are passed into PyRadiomics, which performs the feature extraction procedure and returns a Python dictionary object. Lastly, PyRadiomics Extension parses this dictionary as a W3C-compliant Semantic Web "triple store" (i.e., list of subject-predicate-object statements) with relevant semantic meta-labels drawn from the Radiation Oncology Ontology and Radiomics Ontology. The output can be published on an SPARQL endpoint, and can be remotely examined via SPARQL queries or to a comma separated file for further analysis.
RESULTS: We showed that O-RAW executed efficiently on three datasets with differing modalities, MMD (CT), CROSS (PET) and THUNDER (MR). The test was performed on an HP laptop running Windows 7 operating system and 8GB RAM on which we noted execution time including DICOM images and associated RTSTRUCT matching, binary mask conversion of a single VOI, batch-processing of feature extraction (105 basic features in PyRadiomics), and the conversion to an RDF object. The results were (RIDER) 407.3, (MMD) 123.5, (CROSS) 513.2 and (THUNDER) 128.9 seconds for a single VOI. In addition, we demonstrated a use case, taking images from a public repository and publishing the radiomics results as FAIR data in this study on www.radiomics.org. Finally, we provided a practical instance to show how a user could query radiomic features and track the calculation details based on the RDF graph object created by O-RAW via a simple SPARQL query.
CONCLUSIONS: We implemented O-RAW for FAIR radiomics analysis, and successfully published radiomic features from DICOM-RT objects as semantic web triples. Its practicability and flexibility can greatly increase the development of radiomics research and ease transfer to clinical practice.

PMID: 31580484 [PubMed - as supplied by publisher]

Beyond opinion classification: Extracting facts, opinions and experiences from health forums.

PLoS One. 2019;14(1):e0209961

Authors: Carrillo-de-Albornoz J, Aker A, Kurtic E, Plaza L

Abstract
INTRODUCTION: Surveys indicate that patients, particularly those suffering from chronic conditions, strongly benefit from the information found in social networks and online forums. One challenge in accessing online health information is to differentiate between factual and more subjective information. In this work, we evaluate the feasibility of exploiting lexical, syntactic, semantic, network-based and emotional properties of texts to automatically classify patient-generated contents into three types: "experiences", "facts" and "opinions", using machine learning algorithms. In this context, our goal is to develop automatic methods that will make online health information more easily accessible and useful for patients, professionals and researchers.
MATERIAL AND METHODS: We work with a set of 3000 posts to online health forums in breast cancer, morbus crohn and different allergies. Each sentence in a post is manually labeled as "experience", "fact" or "opinion". Using this data, we train a support vector machine algorithm to perform classification. The results are evaluated in a 10-fold cross validation procedure.
RESULTS: Overall, we find that it is possible to predict the type of information contained in a forum post with a very high accuracy (over 80 percent) using simple text representations such as word embeddings and bags of words. We also analyze more complex features such as those based on the network properties, the polarity of words and the verbal tense of the sentences and show that, when combined with the previous ones, they can boost the results.

PMID: 30625206 [PubMed - indexed for MEDLINE]

Clinical Concept Value Sets and Interoperability in Health Data Analytics.

AMIA Annu Symp Proc. 2018;2018:480-489

Authors: Gold S, Batch A, McClure R, Jiang G, Kharrazi H, Saripalle R, Huser V, Weng C, Roderer N, Szarfman A, Elmqvist N, Gotz D

Abstract
This paper focuses on value sets as an essential component in the health analytics ecosystem. We discuss shared repositories of reusable value sets and offer recommendations for their further development and adoption. In order to motivate these contributions, we explain how value sets fit into specific analytic tasks and the health analytics landscape more broadly; their growing importance and ubiquity with the advent of Common Data Models, Distributed Research Networks, and the availability of higher order, reusable analytic resources like electronic phenotypes and electronic clinical quality measures; the formidable barriers to value set reuse; and our introduction of a concept-agnostic orientation to vocabulary collections. The costs of ad hoc value set management and the benefits of value set reuse are described or implied throughout. Our standards, infrastructure, and design recommendations are not systematic or comprehensive but invite further work to support value set reuse for health analytics. The views represented in the paper do not necessarily represent the views of the institutions or of all the co-authors.

PMID: 30815088 [PubMed - indexed for MEDLINE]

Enabling Web-scale data integration in biomedicine through Linked Open Data.

NPJ Digit Med. 2019;2:90

Authors: Kamdar MR, Fernández JD, Polleres A, Tudorache T, Musen MA

Abstract
The biomedical data landscape is fragmented with several isolated, heterogeneous data and knowledge sources, which use varying formats, syntaxes, schemas, and entity notations, existing on the Web. Biomedical researchers face severe logistical and technical challenges to query, integrate, analyze, and visualize data from multiple diverse sources in the context of available biomedical knowledge. Semantic Web technologies and Linked Data principles may aid toward Web-scale semantic processing and data integration in biomedicine. The biomedical research community has been one of the earliest adopters of these technologies and principles to publish data and knowledge on the Web as linked graphs and ontologies, hence creating the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we provide our perspective on some opportunities proffered by the use of LSLOD to integrate biomedical data and knowledge in three domains: (1) pharmacology, (2) cancer research, and (3) infectious diseases. We will discuss some of the major challenges that hinder the wide-spread use and consumption of LSLOD by the biomedical research community. Finally, we provide a few technical solutions and insights that can address these challenges. Eventually, LSLOD can enable the development of scalable, intelligent infrastructures that support artificial intelligence methods for augmenting human intelligence to achieve better clinical outcomes for patients, to enhance the quality of biomedical research, and to improve our understanding of living systems.

PMID: 31531395 [PubMed]

Model annotation and discovery with the Physiome Model Repository.

BMC Bioinformatics. 2019 Sep 06;20(1):457

Authors: Sarwar DM, Kalbasi R, Gennari JH, Carlson BE, Neal ML, Bono B, Atalag K, Hunter PJ, Nickerson DP

Abstract
BACKGROUND: Mathematics and Phy sics-based simulation models have the potential to help interpret and encapsulate biological phenomena in a computable and reproducible form. Similarly, comprehensive descriptions of such models help to ensure that such models are accessible, discoverable, and reusable. To this end, researchers have developed tools and standards to encode mathematical models of biological systems enabling reproducibility and reuse, tools and guidelines to facilitate semantic description of mathematical models, and repositories in which to archive, share, and discover models. Scientists can leverage these resources to investigate specific questions and hypotheses in a more efficient manner.
RESULTS: We have comprehensively annotated a cohort of models with biological semantics. These annotated models are freely available in the Physiome Model Repository (PMR). To demonstrate the benefits of this approach, we have developed a web-based tool which enables users to discover models relevant to their work, with a particular focus on epithelial transport. Based on a semantic query, this tool will help users discover relevant models, suggesting similar or alternative models that the user may wish to explore or use.
CONCLUSION: The semantic annotation and the web tool we have developed is a new contribution enabling scientists to discover relevant models in the PMR as candidates for reuse in their own scientific endeavours. This approach demonstrates how semantic web technologies and methodologies can contribute to biomedical and clinical research. The source code and links to the web tool are available at https://github.com/dewancse/model-discovery-tool.

PMID: 31492098 [PubMed - in process]

A Neural Network-Inspired Approach for Improved and True Movie Recommendations.

Comput Intell Neurosci. 2019;2019:4589060

Authors: Ibrahim M, Bajwa IS, Ul-Amin R, Kasi B

Abstract
In the last decade, sentiment analysis, opinion mining, and subjectivity of microblogs in social media have attracted a great deal of attention of researchers. Movie recommendation systems are the tools, which provide valuable services to the users. The data available online are growing gradually because the online activities of users or viewers are increasing day by day. Because of this, big data, analytics, and computational issues have raised. Therefore, we have to improve recommendations services upon the traditional one to make the recommendation system significant and efficient. This article presents the solution for these issues by producing the significant and efficient recommendation services using multivariates (ratings, votes, Twitter likes, and reviews) of movies from multiple external resources which are fetched by the web bot and managed by the Apache Hadoop framework in a distributed manner. Reviews are analyzed by a deep semantic analyzer based on the recurrent neural network (RNN/LSTM attention) with user movie attention (UMA) to produce the emotion. The proposed recommender evaluates multivariates and produces a more significant movie recommendation list according to the taste of the user on a mobile app in an efficient way.

PMID: 31467517 [PubMed - in process]

Linking Health Records with Knowledge Sources Using OWL and RDF.

Stud Health Technol Inform. 2019;257:53-58

Authors: Chelsom J, Dogar N

Abstract
This paper describes a method by which the Web Ontology Language (OWL) can be used to specify a highly structured health record, following internationally recognised standards such as ISO 13606 and HL7 CDA. The structured record is coded using schemes such as SNOMED, ICD or LOINC, with the coding applied statically, on the basis of the predefined structure, or dynamically, on the basis of data values entered in the health record. The highly structured, coded record can then be linked with external knowledge sources which are themselves coded using the Resource Description Framework. These methods have been used to implement dynamic decision support in the open source cityEHR health records system. The effectiveness of the decision support depends on the scope and quality of the clinical coding and the sophistication of the algorithm used to match the structured record with knowledge sources.

PMID: 30741172 [PubMed - indexed for MEDLINE]

The SNIK Graph: Visualization of a Medical Informatics Ontology.

Stud Health Technol Inform. 2019 Aug 21;264:1941-1942

Authors: Jahn F, Höffner K, Schneider B, Lörke A, Pause T, Ammenwerth E, Winter A

Abstract
SNIK, a medical informatics ontology, combines knowledge from different literature sources dealing with the management of hospital information systems (HIS). Concepts and relations were extracted from literature, modeled as an ontology and visualized as a graph on a website. We demonstrate the potential of the graph visualization for tuitional scenarios. SNIK complements teaching and learning with conventional literature by concentrating knowledge that is scattered over different pieces of text around one node of a graph.

PMID: 31438418 [PubMed - in process]

Open and Linkable Knowledge About Management of Health Information Systems.

Stud Health Technol Inform. 2019 Aug 21;264:1678-1679

Authors: Höffner K, Jahn F, Lörke A, Pause T, Schneider B, Ammenwerth E, Winter A

Abstract
Given a care delivery organization, its health information system can be defined as the part of the organization that processes and stores data, information, and knowledge. There is an enormous number of frameworks, textbooks and articles that describe the scope of health information system management from the perspective of medical informatics. Transforming this knowledge to Linked Open Data results in a structured data representation that is accessible for both humans and machines, the Semantic Network of Information Management in Hospitals (SNIK). We present interfaces that are useful for researchers, practitioners and students, depending on their objectives and their Semantic Web skills.

PMID: 31438289 [PubMed - in process]

TBench: A Collaborative Work Platform for Multilingual Terminology Editing and Development.

Stud Health Technol Inform. 2019 Aug 21;264:1449-1450

Authors: Deng P, Ji Y, Shen L, Li J, Ren H, Qian Q, Sun H

Abstract
Terminology facilitates consistent use and semantic integration of heterogeneous, multimodal data within and across domains. This paper presents TBench (Termilology Workbench) for multilingual terminology editing and development within a distributed environment. TBench is a web-service Java tool consisting of two main functionalities that are knowledge construction (i.e.extended model based on ISO25964, batch reusing and constructing multilingual concept hierarchy and relationships) and collaborative control in order to achieve custom extensions, reuse, multilingual alignment, integration and refactoring.

PMID: 31438175 [PubMed - in process]

Linked Open Data in the Biomedical Information Area: A Keywords Analysis.

Stud Health Technol Inform. 2019 Aug 21;264:1429-1430

Authors: Bonacina S

Abstract
The objective of this paper was to determine the extent of the usage of "Linked Open Data" within biomedical literature. Applying PRISMA statement for literature reviews, forty-six papers were included in the analysis and keywords identified. Keywords have been classified according to MeSH categories, when possible. Twenty-three keywords had a frequency > one, 146 keywords had a frequency equal to one. Two MeSH categories were recurring. Future work includes applying association rules learning to keywords.

PMID: 31438165 [PubMed - in process]

Using an Artificial Intelligence-Based Argument Theory to Generate Automated Patient Education Dialogues for Families of Children with Juvenile Idiopathic Arthritis.

Stud Health Technol Inform. 2019 Aug 21;264:1337-1341

Authors: Rose-Davis B, Van Woensel W, Stringer E, Abidi S, Abidi SSR

Abstract
Juvenile Idiopathic Arthritis (JIA) is the most common chronic rheumatic disease of childhood, with outcomes including pain, prolonged dependence on medications, and disability. Parents of children with JIA report being overwhelmed by the volume of information in the patient education materials that are available to them. This paper addresses this educational gap by applying an artificial intelligence method, based on an extended model of argument, to design and implement a dialogue system that allows users get the educational material they need, when they need it. In the developed system, the studied model of argument was leveraged as part of the system's dialogue manager. A qualitative evaluation of the system, using cognitive walkthroughs and semi-structured interviews with JIA domain experts, suggests that these methods show great promise for providing quality information to families of children with JIA when they need it.

PMID: 31438143 [PubMed - in process]

Implementation of Clinical Decision Support Services to Detect Potential Drug-Drug Interaction Using Clinical Quality Language.

Stud Health Technol Inform. 2019 Aug 21;264:724-728

Authors: Nguyen BP, Reese T, Decker S, Malone D, Boyce RD, Beyan O

Abstract
Potential drug-drug interactions (PDDI) rules are currently represented without any common standard making them difficult to update, maintain, and exchange. The PDDI minimum information model developed by the Semantic Web in the Healthcare and Life Sciences Community Group describes PDDI knowledge in an actionable format. In this paper, we report implementation and evaluation of CDS Services which represent PDDI knowledge with Clinical Quality Language (CQL). The suggested solution is based on emerging standards including CDS Hooks, FHIR, and CQL. Two use cases are selected, implemented with CQL rules and tested at the Connectathon held at the 32nd Annual Plenary & Working Group Meeting of HL7.

PMID: 31438019 [PubMed - in process]