Analysis of the suitability of existing medical ontologies for building a scalable semantic interoperability solution supporting multi-site collaboration in oncology

Semantic interoperability is essential to facilitate efficient collaboration in heterogeneous multi-site healthcare environments. The deployment of a semantic interoperability solution has the potential to enable a wide range of informatics supported applications in clinical care and research both within as ingle healthcare organization and in a network of organizations. At the same time, building and deploying a semantic interoperability solution may require significant effort to carryout data transformation and to harmonize the semantics of the information in the different systems. Our approach to semantic interoperability leverages existing healthcare standards and ontologies, focusing first on specific clinical domains and key applications, and gradually expanding the solution when needed. An important objective of this work is to create a semantic link between clinical research and care environments to enable applications such as streamlining the execution of multi-centric clinical trials, including the identification of eligible patients for the trials. This paper presents an analysis of the suitability of several widely-used medical ontologies in the clinical domain: SNOMED-CT, LOINC, MedDRA, to capture the semantics of the clinical trial eligibility criteria, of the clinical trial data (e.g., Clinical Report Forms), and of the corresponding patient record data that would enable the automatic identification of eligible patients. Next to the coverage provided by the ontologies we evaluate and compare the sizes of the sets of relevant concepts and their relative frequency to estimate the cost of data transformation, of building the necessary semantic mappings, and of extending the solution to new domains. This analysis shows that our approach is both feasible and scalable

Closed-World Semantics for Query Answering in Temporal Description Logics

Ontology-mediated query answering is a popular paradigm for enriching answers to user queries with background knowledge. For querying the absence of information, however, there exist only few ontology-based approaches. Moreover, these proposals conflate the closed-domain and closed-world assumption, and therefore are not suited to deal with the anonymous objects that are common in ontological reasoning. Many real-world applications, like processing electronic health records (EHRs), also contain a temporal dimension, and require efficient reasoning algorithms. Moreover, since medical data is not recorded on a regular basis, reasoners must deal with sparse data with potentially large temporal gaps. Our contribution consists of three main parts: Firstly, we introduce a new closed-world semantics for answering conjunctive queries with negation over ontologies formulated in the description logic ELH⊥, which is based on the minimal universal model. We propose a rewriting strategy for dealing with negated query atoms, which shows that query answering is possible in polynomial time in data complexity. Secondly, we introduce a new temporal variant of ELH⊥ that features a convexity operator. We extend this minimal-world semantics for answering metric temporal conjunctive queries with negation over the logic and obtain similar rewritability and complexity results. Thirdly, apart from the theoretical results, we evaluate minimal-world semantics in practice by selecting patients, based their EHRs, that match given criteria

Oncolytic HSV-1 G207 immunovirotherapy for pediatric high-grade gliomas

BACKGROUND: Outcomes in children and adolescents with recurrent or progressive high-grade glioma are poor, with a historical median overall survival of 5.6 months. Pediatric high-grade gliomas are largely immunologically silent or cold, with few tumor-infiltrating lymphocytes. Preclinically, pediatric brain tumors are highly sensitive to oncolytic virotherapy with genetically engineered herpes simplex virus type 1 (HSV-1) G207, which lacks genes essential for replication in normal brain tissue. METHODS: We conducted a phase 1 trial of G207, which used a 3+3 design with four dose cohorts of children and adolescents with biopsy-confirmed recurrent or progressive supratentorial brain tumors. Patients underwent stereotactic placement of up to four intratumoral catheters. The following day, they received G207 (10 RESULTS: Twelve patients 7 to 18 years of age with high-grade glioma received G207. No dose-limiting toxic effects or serious adverse events were attributed to G207 by the investigators. Twenty grade 1 adverse events were possibly related to G207. No virus shedding was detected. Radiographic, neuropathological, or clinical responses were seen in 11 patients. The median overall survival was 12.2 months (95% confidence interval, 8.0 to 16.4); as of June 5, 2020, a total of 4 of 11 patients were still alive 18 months after G207 treatment. G207 markedly increased the number of tumor-infiltrating lymphocytes. CONCLUSIONS: Intratumoral G207 alone and with radiation had an acceptable adverse-event profile with evidence of responses in patients with recurrent or progressive pediatric high-grade glioma. G207 converted immunologically cold tumors to hot. (Supported by the Food and Drug Administration and others; ClinicalTrials.gov number, NCT02457845.)

Pemanfaatan Sparql Inferencing Notation (SPIN)Dalam Prototipe Pencarian Data Restoran Berbasis Semantik

INTISARI Dewasa ini, semakin banyak informasi yang beredar di internet yang menyebabkan semakin sulitnya pengguna dalam mencari informasi yang diinginkan dikarenakan banyak mesin pencari yang belum menggunakan konsep semantik dalam memahami kalimat pencarian. Aplikasi yang dikembangkan dalam penelitian ini berupa prototipe untuk pencarian semantik pada data restoran dengan memanfaatkan SPARQL Inferencing Notation (SPIN). Kalimat pencarian harus mengikuti suatu aturan yaitu harus diawali dengan kata perintah (tampil, cari, sebut) dan diikuti dengan sinonim dari class yang dicari (restoran, makanan, kategori, lokasi). Setiap kalimat pencarian yang diisikan pengguna diterjemahkan dengan melakukan proses tokenization, stemming, penghilangan stopword (proses filtering), dan dilanjutkan dengan representasi kalimat menggunakan keywords yang ada di ontologi bantuan (words.owl) sehingga terbentuk SPARQL yang dapat dijalankan untuk melakukan query data yang terletak di ontologi restaurants.owl. Hasil pengujian menunjukkan bahwa aplikasi pencarian yang dibuat mampu menangani berbagai variasi pola pertanyaan dengan nilai rasio Recall dan Precision adalah 1:1, ini berarti aplikasi ini memiliki efektifitas dan efisiensi yang tinggi dalam hasil pencariannya. Kata Kunci : ontologi, owl, restoran, pencarian semantik, SPARQL, SPI

Timely and reliable evaluation of the effects of interventions: a framework for adaptive meta-analysis (FAME)

Most systematic reviews are retrospective and use aggregate data AD) from publications, meaning they can be unreliable, lag behind therapeutic developments and fail to influence ongoing or new trials. Commonly, the potential influence of unpublished or ongoing trials is overlooked when interpreting results, or determining the value of updating the meta-analysis or need to collect individual participant data (IPD). Therefore, we developed a Framework for Adaptive Metaanalysis (FAME) to determine prospectively the earliest opportunity for reliable AD meta-analysis. We illustrate FAME using two systematic reviews in men with metastatic (M1) and non-metastatic (M0)hormone-sensitive prostate cancer (HSPC)

The Development and Validation of a System for the Knowledge-Based Tutoring of Special Education Rules and Regulations

Research indicates that school officials fail to identify a relatively high proportion of school-aged children with behavioral or emotional handicaps. As a result, these children may not be receiving the special education services to which they are entitled. Multidisciplinary team members may be failing to identify these children because they lack understanding of special education rules and regulations. The purpose of this project was to combine the technologies of expert systems and mastery-based instruction to develop an inservice and preservice training program capable of producing mastery-level performance of the skills required to identify children with behavioral or emotional handicaps. Borg and Gall\u27s ( 983) research and development cycle provided the model for developing, testing, and revising the program. Prototype evaluations and large-scale field tests revealed that the program met its performance and user satisfaction objectives when administered under conditions of independent administration. However, a failure on the use and part of remote remote administrators to comply with prescribed program administration procedures allowed an unacceptable number of subjects to end training without completing all computer exercises. Attention to administration procedures contributed to the success of the project in meeting its performance and user satisfaction objectives in the final operational field test. The positive findings of the project have implications on two levels. First, the findings are important for the positive effect they may have on the lives of children. Decision-making errors on the part of multidisciplinary team members can be costly to children with behavioral or emotional handicaps, as well as to other children. The evidence obtained in this project suggests that multidisciplinary team members can be trained to accurately identify children with behavioral or emotional handicaps. On another, and perhaps more important, level, the findings have implications for the design of effective inservice and preservice training programs. The application of innovative technologies to inservice and preservice training problems does not necessarily result in the development of products capable of producing mastery-level decision-making performance. The positive results achieved in the present project suggest that those seeking to apply innovative technologies to inservice and preservice training problems take into account basic instructional design principles

Implementing electronic scales to support standardized phenotypic data collection - the case of the Scale for the Assessment and Rating of Ataxia (SARA)

The main objective of this doctoral thesis was to facilitate the integration of the semantics required to automatically interpret collections of standardized clinical data. In order to address the objective, we combined the best performances from clinical archetypes, guidelines and ontologies for developing an electronic prototype for the Scale of the Assessment and Rating of Ataxia (SARA), broadly used in neurology. A scaled-down version of the Human Phenotype Ontology was automatically extracted and used as backbone to normalize the content of the SARA through clinical archetypes. The knowledge required to exploit reasoning on the SARA data was modeled as separate information-processing units interconnected via the defined archetypes. Based on this approach, we implemented a prototype named SARA Management System, to be used for both the assessment of cerebellar syndrome and the production of a clinical synopsis. For validation purposes, we used recorded SARA data from 28 anonymous subjects affected by SCA36. Our results reveal a substantial degree of agreement between the results achieved by the prototype and human experts, confirming that the combination of archetypes, ontologies and guidelines is a good solution to automate the extraction of relevant phenotypic knowledge from plain scores of rating scales

Automatic Generation of Personalized Recommendations in eCoaching

Denne avhandlingen omhandler eCoaching for personlig livsstilsstøtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er å designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede løsningen er fokusert på forbedring av fysisk aktivitet. Prototypen bruker bærbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for å utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen på teknologisk verifisering snarere enn klinisk evaluering.publishedVersio

Intégration de ressources en recherche translationnelle : une approche unificatrice en support des systèmes de santé "apprenants"

Learning health systems (LHS) are gradually emerging and propose a complimentary approach to translational research challenges by implementing close coupling of health care delivery, research and knowledge translation. To support coherent knowledge sharing, the system needs to rely on an integrated and efficient data integration platform. The framework and its theoretical foundations presented here aim at addressing this challenge. Data integration approaches are analysed in light of the requirements derived from LHS activities and data mediation emerges as the one most adapted for a LHS. The semantics of clinical data found in biomedical sources can only be fully derived by taking into account, not only information from the structural models (field X of table Y), but also terminological information (e.g. International Classification of Disease 10th revision) used to encode facts. The unified framework proposed here takes this into account. The platform has been implemented and tested in context of the TRANSFoRm endeavour, a European project funded by the European commission. It aims at developing a LHS including clinical activities in primary care. The mediation model developed for the TRANSFoRm project, the Clinical Data Integration Model, is presented and discussed. Results from TRANSFoRm use-cases are presented. They illustrate how a unified data sharing platform can support and enhance prospective research activities in context of a LHS. In the end, the unified mediation framework presented here allows sufficient expressiveness for the TRANSFoRm needs. It is flexible, modular and the CDIM mediation model supports the requirements of a primary care LHS.Les systèmes de santé "apprenants" (SSA) présentent une approche complémentaire et émergente aux problèmes de la recherche translationnelle en couplant de près les soins de santé, la recherche et le transfert de connaissances. Afin de permettre un flot d’informations cohérent et optimisé, le système doit se doter d’une plateforme intégrée de partage de données. Le travail présenté ici vise à proposer une approche de partage de données unifiée pour les SSA. Les grandes approches d’intégration de données sont analysées en fonction du SSA. La sémantique des informations cliniques disponibles dans les sources biomédicales est la résultante des connaissances des modèles structurelles des sources mais aussi des connaissances des modèles terminologiques utilisés pour coder l’information. Les mécanismes de la plateforme unifiée qui prennent en compte cette interdépendance sont décrits. La plateforme a été implémentée et testée dans le cadre du projet TRANSFoRm, un projet européen qui vise à développer un SSA. L’instanciation du modèle de médiation pour le projet TRANSFoRm, le Clinical Data Integration Model est analysée. Sont aussi présentés ici les résultats d’un des cas d’utilisation de TRANSFoRm pour supporter la recherche afin de donner un aperçu concret de l’impact de la plateforme sur le fonctionnement du SSA. Au final, la plateforme unifiée d’intégration proposée ici permet un niveau d’expressivité suffisant pour les besoins de TRANSFoRm. Le système est flexible et modulaire et le modèle de médiation CDIM couvre les besoins exprimés pour le support des activités d’un SSA comme TRANSFoRm