Monitoring Adverse Drug Events in Web Forums: Evaluation of a Pipeline and Use Case Study.

BACKGROUND: To mitigate safety concerns, regulatory agencies must make informed decisions regarding drug usage and adverse drug events (ADEs). The primary pharmacovigilance data stem from spontaneous reports by health care professionals. However, underreporting poses a notable challenge within the current system. Explorations into alternative sources, including electronic patient records and social media, have been undertaken. Nevertheless, social media's potential remains largely untapped in real-world scenarios. OBJECTIVE: The challenge faced by regulatory agencies in using social media is primarily attributed to the absence of suitable tools to support decision makers. An effective tool should enable access to information via a graphical user interface, presenting data in a user-friendly manner rather than in their raw form. This interface should offer various visualization options, empowering users to choose representations that best convey the data and facilitate informed decision-making. Thus, this study aims to assess the potential of integrating social media into pharmacovigilance and enhancing decision-making with this novel data source. To achieve this, our objective was to develop and assess a pipeline that processes data from the extraction of web forum posts to the generation of indicators and alerts within a visual and interactive environment. The goal was to create a user-friendly tool that enables regulatory authorities to make better-informed decisions effectively. METHODS: To enhance pharmacovigilance efforts, we have devised a pipeline comprising 4 distinct modules, each independently editable, aimed at efficiently analyzing health-related French web forums. These modules were (1) web forums' posts extraction, (2) web forums' posts annotation, (3) statistics and signal detection algorithm, and (4) a graphical user interface (GUI). We showcase the efficacy of the GUI through an illustrative case study involving the introduction of the new formula of Levothyrox in France. This event led to a surge in reports to the French regulatory authority. RESULTS: Between January 1, 2017, and February 28, 2021, a total of 2,081,296 posts were extracted from 23 French web forums. These posts contained 437,192 normalized drug-ADE couples, annotated with the Anatomical Therapeutic Chemical (ATC) Classification and Medical Dictionary for Regulatory Activities (MedDRA). The analysis of the Levothyrox new formula revealed a notable pattern. In August 2017, there was a sharp increase in posts related to this medication on social media platforms, which coincided with a substantial uptick in reports submitted by patients to the national regulatory authority during the same period. CONCLUSIONS: We demonstrated that conducting quantitative analysis using the GUI is straightforward and requires no coding. The results aligned with prior research and also offered potential insights into drug-related matters. Our hypothesis received partial confirmation because the final users were not involved in the evaluation process. Further studies, concentrating on ergonomics and the impact on professionals within regulatory agencies, are imperative for future research endeavors. We emphasized the versatility of our approach and the seamless interoperability between different modules over the performance of individual modules. Specifically, the annotation module was integrated early in the development process and could undergo substantial enhancement by leveraging contemporary techniques rooted in the Transformers architecture. Our pipeline holds potential applications in health surveillance by regulatory agencies or pharmaceutical companies, aiding in the identification of safety concerns. Moreover, it could be used by research teams for retrospective analysis of events.

Encouraging Behavior Changes and Preventing Cardiovascular Diseases Using the Prevent Connect Mobile Health App: Conception and Evaluation of App Quality.

BACKGROUND: Cardiovascular diseases are a major cause of death worldwide. Mobile health apps could help in preventing cardiovascular diseases by improving modifiable risk factors such as eating habits, physical activity levels, and alcohol or tobacco consumption. OBJECTIVE: The aim of this study was to design a mobile health app, Prevent Connect, and to assess its quality for (1) assessing patient behavior for 4 cardiovascular risk factors (unhealthy eating, sedentary lifestyle, alcohol, and tobacco consumption) and (2) suggesting personalized recommendations and mobile health interventions for risky behaviors. METHODS: The knowledge base of the app is based on French national recommendations for healthy eating, physical activity, and limiting alcohol and tobacco consumption. It contains a list of patient behaviors and related personalized recommendations and digital health interventions. The interface was designed according to usability principles. Its quality was assessed by a panel of 52 users in a 5-step process: completion of the demographic form, visualization of a short presentation of the app, testing of the app, completion of the user version of the Mobile App Rating Scale (uMARS), and an open group discussion. RESULTS: This app assesses patient behaviors through specific questionnaires about 4 risk factors (unhealthy eating, sedentary lifestyle, alcohol, and tobacco consumption) and suggests personalized recommendations and digital health interventions for improving behavior. The app was deemed to be of good quality, with a mean uMARS quality score of 4 on a 5-point Likert scale. The functionality and information content of the app were particularly appreciated, with a mean uMARS score above 4. Almost all the study participants appreciated the navigation system and found the app easy to use. More than three-quarters of the study participants found the app content relevant, concise, and comprehensive. The aesthetics and the engagement of the app were also appreciated (uMARS score, 3.7). Overall, 80% (42/52) of the study participants declared that the app helped them to become aware of the importance of addressing health behavior, and 65% (34/52) said that the app helped motivate them to change lifestyle habits. CONCLUSIONS: The app assessed the risky behaviors of the patients and delivered personalized recommendations and digital health interventions for multiple risk factors. The quality of the app was considered to be good, but the impact of the app on behavior changes is yet to be demonstrated and will be assessed in further studies.

Evaluation Criteria of Noninvasive Telemonitoring for Patients With Heart Failure: Systematic Review.

BACKGROUND: Telemonitoring can improve heart failure (HF) management, but there is no standardized evaluation framework to comprehensively evaluate its impact. OBJECTIVE: Our objectives were to list the criteria used in published evaluations of noninvasive HF telemonitoring projects, describe how they are used in the evaluation studies, and organize them into a consistent scheme. METHODS: Articles published from January 1990 to August 2015 were obtained through MEDLINE, Web of Science, and EMBASE. Articles were eligible if they were original reports of a noninvasive HF telemonitoring evaluation study in the English language. Studies of implantable telemonitoring devices were excluded. Each selected article was screened to extract the description of the telemonitoring project and the evaluation process and criteria. A qualitative synthesis was performed. RESULTS: We identified and reviewed 128 articles leading to 52 evaluation criteria classified into 6 dimensions: clinical, economic, user perspective, educational, organizational, and technical. The clinical and economic impacts were evaluated in more than 70% of studies, whereas the educational, organizational, and technical impacts were studied in fewer than 15%. User perspective was the most frequently covered dimension in the development phase of telemonitoring projects, whereas clinical and economic impacts were the focus of later phases. CONCLUSIONS: Telemonitoring evaluation frameworks should cover all 6 dimensions appropriately distributed along the telemonitoring project lifecycle. Our next goal is to build such a comprehensive evaluation framework for telemonitoring and test it on an ongoing noninvasive HF telemonitoring project.

OntoADR a semantic resource describing adverse drug reactions to support searching, coding, and information retrieval.

INTRODUCTION: Efficient searching and coding in databases that use terminological resources requires that they support efficient data retrieval. The Medical Dictionary for Regulatory Activities (MedDRA) is a reference terminology for several countries and organizations to code adverse drug reactions (ADRs) for pharmacovigilance. Ontologies that are available in the medical domain provide several advantages such as reasoning to improve data retrieval. The field of pharmacovigilance does not yet benefit from a fully operational ontology to formally represent the MedDRA terms. Our objective was to build a semantic resource based on formal description logic to improve MedDRA term retrieval and aid the generation of on-demand custom groupings by appropriately and efficiently selecting terms: OntoADR. METHODS: The method consists of the following steps: (1) mapping between MedDRA terms and SNOMED-CT, (2) generation of semantic definitions using semi-automatic methods, (3) storage of the resource and (4) manual curation by pharmacovigilance experts. RESULTS: We built a semantic resource for ADRs enabling a new type of semantics-based term search. OntoADR adds new search capabilities relative to previous approaches, overcoming the usual limitations of computation using lightweight description logic, such as the intractability of unions or negation queries, bringing it closer to user needs. Our automated approach for defining MedDRA terms enabled the association of at least one defining relationship with 67% of preferred terms. The curation work performed on our sample showed an error level of 14% for this automated approach. We tested OntoADR in practice, which allowed us to build custom groupings for several medical topics of interest. DISCUSSION: The methods we describe in this article could be adapted and extended to other terminologies which do not benefit from a formal semantic representation, thus enabling better data retrieval performance. Our custom groupings of MedDRA terms were used while performing signal detection, which suggests that the graphical user interface we are currently implementing to process OntoADR could be usefully integrated into specialized pharmacovigilance software that rely on MedDRA.

A Multiagent System for Integrated Detection of Pharmacovigilance Signals.

Pharmacovigilance is the scientific discipline that copes with the continuous assessment of the safety profile of marketed drugs. This assessment relies on diverse data sources, which are routinely analysed to identify the so-called "signals", i.e. potential associations between drugs and adverse effects, that are unknown or incompletely documented. Various computational methods have been proposed to support domain experts in signal detection. However, recent comparative studies illustrated that current methods exhibit high false-positive rates, significantly variable performance across different datasets used for analysis and events of interest, but also complementarity in their outcomes. In this regard, in order to reinforce accurate and timely signal detection, we elaborated through an agent-based approach towards systematic, joint exploitation of multiple heterogeneous signal detection methods, data sources and other drug-related resources under a common, integrated framework. The approach relies on a multiagent system operating based on a collaborative agent interaction protocol, aiming to implement a comprehensive workflow that comprises of method selection and execution, as well as outcomes' aggregation, filtering, ranking and annotation. This paper presents the design of the proposed multiagent system, discusses implementation issues and demonstrates the applicability of the proposed solution in an example signal detection scenario. This work constitutes a step towards large-scale, integrated and knowledge-intensive computational signal detection.

[Automated grouping of terms associated to cardiac valve fibrosis in MedDRA]. / Groupement automatisé de termes liés aux valvulopathies médicamenteuses dans MedDRA.

AIM: To propose an alternative approach for building custom groupings of terms that complements the usual approach based on both hierarchical method (selection of reference groupings in medical dictionary for regulatory activities [MedDRA]) and/or textual method (string search), for case reports extraction from a pharmacovigilance database in response to a safety problem. Here we take cardiac valve fibrosis as an example. METHODS: The list of terms obtained by an automated approach, based on querying ontology of adverse drug reactions (OntoADR), a knowledge base defining MedDRA terms through relationships with systematized nomenclature of medicine-clinical terms (SNOMED CT) concepts, was compared with the reference list consisting of 53 preferred terms obtained by hierarchical and textual method. Two queries were performed on OntoADR by using a dedicated software: OntoADR query tools. Both queries excluded congenital diseases, and included a procedure or an auscultation method performed on cardiac valve structures. Query 1 also considered MedDRA terms related to fibrosis, narrowing or calcification of heart valves, and query 2 MedDRA terms described according to one of these four SNOMED CT terms: "Insufficiency", "Valvular sclerosis", "Heart valve calcification" or "Heart valve stenosis". RESULTS: The reference grouping consisted of 53 MedDRA preferred terms. Our automated method achieved recall of 79% and precision of 100% for query 1 privileging morphological abnormalities, and recall of 100% and precision of 96% for query 2 privileging functional abnormalities. CONCLUSION: An alternative approach to MedDRA reference groupings for building custom groupings is feasible for cardiac valve fibrosis. OntoADR is still in development. Its application to other adverse reactions would require significant work for a knowledge engineer to define every MedDRA term, but such definitions could then be queried as many times as necessary by pharmacovigilance professionals.

Adverse Drug Reaction Identification and Extraction in Social Media: A Scoping Review.

BACKGROUND: The underreporting of adverse drug reactions (ADRs) through traditional reporting channels is a limitation in the efficiency of the current pharmacovigilance system. Patients' experiences with drugs that they report on social media represent a new source of data that may have some value in postmarketing safety surveillance. OBJECTIVE: A scoping review was undertaken to explore the breadth of evidence about the use of social media as a new source of knowledge for pharmacovigilance. METHODS: Daubt et al's recommendations for scoping reviews were followed. The research questions were as follows: How can social media be used as a data source for postmarketing drug surveillance? What are the available methods for extracting data? What are the different ways to use these data? We queried PubMed, Embase, and Google Scholar to extract relevant articles that were published before June 2014 and with no lower date limit. Two pairs of reviewers independently screened the selected studies and proposed two themes of review: manual ADR identification (theme 1) and automated ADR extraction from social media (theme 2). Descriptive characteristics were collected from the publications to create a database for themes 1 and 2. RESULTS: Of the 1032 citations from PubMed and Embase, 11 were relevant to the research question. An additional 13 citations were added after further research on the Internet and in reference lists. Themes 1 and 2 explored 11 and 13 articles, respectively. Ways of approaching the use of social media as a pharmacovigilance data source were identified. CONCLUSIONS: This scoping review noted multiple methods for identifying target data, extracting them, and evaluating the quality of medical information from social media. It also showed some remaining gaps in the field. Studies related to the identification theme usually failed to accurately assess the completeness, quality, and reliability of the data that were analyzed from social media. Regarding extraction, no study proposed a generic approach to easily adding a new site or data source. Additional studies are required to precisely determine the role of social media in the pharmacovigilance system.

Formalizing MedDRA to support semantic reasoning on adverse drug reaction terms.

Although MedDRA has obvious advantages over previous terminologies for coding adverse drug reactions and discovering potential signals using data mining techniques, its terminological organization constrains users to search terms according to predefined categories. Adding formal definitions to MedDRA would allow retrieval of terms according to a case definition that may correspond to novel categories that are not currently available in the terminology. To achieve semantic reasoning with MedDRA, we have associated formal definitions to MedDRA terms in an OWL file named OntoADR that is the result of our first step for providing an "ontologized" version of MedDRA. MedDRA five-levels original hierarchy was converted into a subsumption tree and formal definitions of MedDRA terms were designed using several methods: mappings to SNOMED-CT, semi-automatic definition algorithms or a fully manual way. This article presents the main steps of OntoADR conception process, its structure and content, and discusses problems and limits raised by this attempt to "ontologize" MedDRA.

Rapid Review on Publicly Available Datasets for Health Misinformation Detection.

The proliferation of health misinformation in recent years has prompted the development of various methods for detecting and combatting this issue. This review aims to provide an overview of the implementation strategies and characteristics of publicly available datasets that can be used for health misinformation detection. Since 2020, a large number of such datasets have emerged, half of which are focused on COVID-19. Most of the datasets are based on fact-checkable websites, while only a few are annotated by experts. Furthermore, some datasets provide additional information such as social engagement and explanations, which can be utilized to study the spread of misinformation. Overall, these datasets offer a valuable resource for researchers working to combat the spread and consequences of health misinformation.

Transfer Learning for Early Prediction of Adverse Drug Reactions: Docetaxel and Alopecia in Breast Cancer as a Case Study.

Transfer Learning (TL) is an approach which has not yet been widely investigated in healthcare, mostly applied in image data. This study outlines a TL pipeline leveraging Individual Case Safety reports (ICSRs) and Electronic Health Records (EHR), applied for the early detection Adverse Drug Reactions (ADR), evaluated using of alopecia and docetaxel on breast cancer patients as use case.

Improving information retrieval using Medical Subject Headings Concepts: a test case on rare and chronic diseases.

BACKGROUND: As more scientific work is published, it is important to improve access to the biomedical literature. Since 2000, when Medical Subject Headings (MeSH) Concepts were introduced, the MeSH Thesaurus has been concept based. Nevertheless, information retrieval is still performed at the MeSH Descriptor or Supplementary Concept level. OBJECTIVE: The study assesses the benefit of using MeSH Concepts for indexing and information retrieval. METHODS: Three sets of queries were built for thirty-two rare diseases and twenty-two chronic diseases: (1) using PubMed Automatic Term Mapping (ATM), (2) using Catalog and Index of French-language Health Internet (CISMeF) ATM, and (3) extrapolating the MEDLINE citations that should be indexed with a MeSH Concept. RESULTS: Type 3 queries retrieve significantly fewer results than type 1 or type 2 queries (about 18,000 citations versus 200,000 for rare diseases; about 300,000 citations versus 2,000,000 for chronic diseases). CISMeF ATM also provides better precision than PubMed ATM for both disease categories. DISCUSSION: Using MeSH Concept indexing instead of ATM is theoretically possible to improve retrieval performance with the current indexing policy. However, using MeSH Concept information retrieval and indexing rules would be a fundamentally better approach. These modifications have already been implemented in the CISMeF search engine.

Automatic generation of MedDRA terms groupings using an ontology.

In the context of PROTECT European project, we have developed an ontology of adverse drug reactions (OntoADR) based on the original MedDRA hierarchy and a query-based method to achieve automatic MedDRA terms groupings for improving pharmacovigilance signal detection. Those groupings were evaluated against standard handmade MedDRA groupings corresponding to first priority pharmacovigilance safety topics. Our results demonstrate that this automatic method allows catching most of the terms present in the reference groupings, and suggest that it could offer an important saving of time for the achievement of pharmacovigilance groupings. This paper describes the theoretical context of this work, the evaluation methodology, and presents the principal results.

Clinical diagnosis support system based on case based fuzzy cognitive maps and semantic web.

Incorrect or improper diagnostic tests uses have important implications for health outcomes and costs. Clinical Decision Support Systems purports to optimize the use of diagnostic tests in clinical practice. The computerized medical reasoning should not only focus on existing medical knowledge but also on physician's previous experiences and new knowledge. Such medical knowledge is vague and defines uncertain relationships between facts and diagnosis, in this paper, Case Based Fuzzy Cognitive Maps (CBFCM) are proposed as an evolution of Fuzzy Cognitive Maps. They allow more complete representation of knowledge since case-based fuzzy rules are introduced to improve diagnosis decision. We have developed a framework for interacting with patient's data and formalizing knowledge from Guidelines in the domain of Urinary Tract Infection. The conducted study allowed us to test cognitive approaches for implementing Guidelines with Semantic Web tools. The advantage of this approach is to enable the sharing and reuse of knowledge from Guidelines, physicians experiences and simplify maintenance.

Decision support system based semantic web for personalized patient care.

Personalized medicine may be considered an extension of traditional approaches to understanding and treating diseases, but with greater precision. A profile of a patient's genetic variation can guide the selection of drugs or treatment protocols that minimize harmful side effects or ensure a more successful outcome. In this paper we describe a decision support system designed to assist physicians for personalized care, and methodology for integration in the clinical workflow. A reasoning method for interacting heterogeneous knowledge and data is a necessity in the context of personalized medicine. Development of clinical decision support based semantic web for personalized patient care is to achieve its potential and improve the quality, safety and efficiency of healthcare.

The Electronic Healthcare Record for Clinical Research (EHR4CR) information model and terminology.

A major barrier to repurposing routinely collected data for clinical research is the heterogeneity of healthcare information systems. Electronic Healthcare Record for Clinical Research (EHR4CR) is a European platform designed to improve the efficiency of conducting clinical trials. In this paper, we propose an initial architecture of the EHR4CR Semantic Interoperability Framework. We used a model-driven engineering approach to build a reference HL7-based multidimensional model bound to a set of reference clinical terminologies acting as a global as view model. We then conducted an evaluation of its expressiveness for patient eligibility. The EHR4CR information model consists in one fact table dedicated to clinical statement and 4 dimensions. The EHR4CR terminology integrates reference terminologies used in patient care (e.g LOINC, ICD-10, SNOMED CT, etc). We used the Object Constraint Language (OCL) to represent patterns of eligibility criteria as constraints on the EHR4CR model to be further transformed in SQL statements executed on different clinical data warehouses.

Data Definition Ontology for clinical data integration and querying.

This paper describes an approach to build a Data Definition Ontology (DDO) in the context of full domain ontology integration with datasets in order to share and query clinical heterogeneous data repositories. We have adapted an existing semantic web tool (D2RQ) to implement a process that automatically generates the DDO from a database information model, thanks to reverse engineering and schema mapping approaches. This study has been performed in the context of the DebugIT European project (Detecting and Eliminating Bacteria UsinG Information Technology) that aims to control and monitor the bacterial growth via a semantic interoperability platform (IP). The evaluation of the process is based, first, on the accuracy of the produced DDO for different samples of database storage and second, by checking the congruency between the DDO and the D2RQ database mapping file.

An Integrated Care Platform System (C3-Cloud) for Care Planning, Decision Support, and Empowerment of Patients With Multimorbidity: Protocol for a Technology Trial.

BACKGROUND: There is an increasing need to organize the care around the patient and not the disease, while considering the complex realities of multiple physical and psychosocial conditions, and polypharmacy. Integrated patient-centered care delivery platforms have been developed for both patients and clinicians. These platforms could provide a promising way to achieve a collaborative environment that improves the provision of integrated care for patients via enhanced information and communication technology solutions for semiautomated clinical decision support. OBJECTIVE: The Collaborative Care and Cure Cloud project (C3-Cloud) has developed 2 collaborative computer platforms for patients and members of the multidisciplinary team (MDT) and deployed these in 3 different European settings. The objective of this study is to pilot test the platforms and evaluate their impact on patients with 2 or more chronic conditions (diabetes mellitus type 2, heart failure, kidney failure, depression), their informal caregivers, health care professionals, and, to some extent, health care systems. METHODS: This paper describes the protocol for conducting an evaluation of user experience, acceptability, and usefulness of the platforms. For this, 2 "testing and evaluation" phases have been defined, involving multiple qualitative methods (focus groups and surveys) and advanced impact modeling (predictive modeling and cost-benefit analysis). Patients and health care professionals were identified and recruited from 3 partnering regions in Spain, Sweden, and the United Kingdom via electronic health record screening. RESULTS: The technology trial in this 4-year funded project (2016-2020) concluded in April 2020. The pilot technology trial for evaluation phases 3 and 4 was launched in November 2019 and carried out until April 2020. Data collection for these phases is completed with promising results on platform acceptance and socioeconomic impact. We believe that the phased, iterative approach taken is useful as it involves relevant stakeholders at crucial stages in the platform development and allows for a sound user acceptance assessment of the final product. CONCLUSIONS: Patients with multiple chronic conditions often experience shortcomings in the care they receive. It is hoped that personalized care plan platforms for patients and collaboration platforms for members of MDTs can help tackle the specific challenges of clinical guideline reconciliation for patients with multimorbidity and improve the management of polypharmacy. The initial evaluative phases have indicated promising results of platform usability. Results of phases 3 and 4 were methodologically useful, yet limited due to the COVID-19 pandemic. TRIAL REGISTRATION: ClinicalTrials.gov NCT03834207; https://clinicaltrials.gov/ct2/show/NCT03834207. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/21994.

Integrating clinical research with the Healthcare Enterprise: from the RE-USE project to the EHR4CR platform.

BACKGROUND: There are different approaches for repurposing clinical data collected in the Electronic Healthcare Record (EHR) for use in clinical research. Semantic integration of "siloed" applications across domain boundaries is the raison d'être of the standards-based profiles developed by the Integrating the Healthcare Enterprise (IHE) initiative - an initiative by healthcare professionals and industry promoting the coordinated use of established standards such as DICOM and HL7 to address specific clinical needs in support of optimal patient care. In particular, the combination of two IHE profiles - the integration profile "Retrieve Form for Data Capture" (RFD), and the IHE content profile "Clinical Research Document" (CRD) - offers a straightforward approach to repurposing EHR data by enabling the pre-population of the case report forms (eCRF) used for clinical research data capture by Clinical Data Management Systems (CDMS) with previously collected EHR data. OBJECTIVE: Implement an alternative solution of the RFD-CRD integration profile centered around two approaches: (i) Use of the EHR as the single-source data-entry and persistence point in order to ensure that all the clinical data for a given patient could be found in a single source irrespective of the data collection context, i.e. patient care or clinical research; and (ii) Maximize the automatic pre-population process through the use of a semantic interoperability services that identify duplicate or semantically-equivalent eCRF/EHR data elements as they were collected in the EHR context. METHODS: The RE-USE architecture and associated profiles are focused on defining a set of scalable, standards-based, IHE-compliant profiles that can enable single-source data collection/entry and cross-system data reuse through semantic integration. Specifically, data reuse is realized through the semantic mapping of data collection fields in electronic Case Report Forms (eCRFs) to data elements previously defined as part of patient care-centric templates in the EHR context. The approach was evaluated in the context of a multi-center clinical trial conducted in a large, multi-disciplinary hospital with an installed EHR. RESULTS: Data elements of seven eCRFs used in a multi-center clinical trial were mapped to data elements of patient care-centric templates in use in the EHR at the George Pompidou hospital. 13.4% of the data elements of the eCRFs were found to be represented in EHR templates and were therefore candidate for pre-population. During the execution phase of the clinical study, the semantic mapping architecture enabled data persisted in the EHR context as part of clinical care to be used to pre-populate eCRFS for use without secondary data entry. To ensure that the pre-populated data is viable for use in the clinical research context, all pre-populated eCRF data needs to be first approved by a trial investigator prior to being persisted in a research data store within a CDMS. CONCLUSION: Single-source data entry in the clinical care context for use in the clinical research context - a process enabled through the use of the EHR as single point of data entry, can - if demonstrated to be a viable strategy - not only significantly reduce data collection efforts while simultaneously increasing data collection accuracy secondary to elimination of transcription or double-entry errors between the two contexts but also ensure that all the clinical data for a given patient, irrespective of the data collection context, are available in the EHR for decision support and treatment planning. The RE-USE approach used mapping algorithms to identify semantic coherence between clinical care and clinical research data elements and pre-populate eCRFs. The RE-USE project utilized SNOMED International v.3.5 as its "pivot reference terminology" to support EHR-to-eCRF mapping, a decision that likely enhanced the "recall" of the mapping algorithms. The RE-USE results demonstrate the difficult challenges involved in semantic integration between the clinical care and clinical research contexts.

Grouping pharmacovigilance terms with semantic distance.

Pharmacovigilance is the activity related to the collection, analysis and prevention of adverse drug reactions (ADRs) induced by drugs or biologics. Besides other methods, statistical algorithms are used to detect previously unknown ADRs, and it was noted that groupings of ADR terms can further improve safety signal detection. Standardised MedDRA Queries are developed to assist retrieval and evaluation of MedDRA-coded ADR reports. Dependent on the context of their application, different SMQs show varying degrees of specificity and sensitivity; some appear to be over-inclusive, some might miss relevant terms. Moreover, several important safety topics are not yet fully covered by SMQs. The objective of this work is to propose an automatic method for the creation of groupings of terms. This method is based on the application of the semantic distance between MedDRA terms. Several experiments are performed, showing a promising precision and an acceptable recall.

Identifying Actionability as a Key Factor for the Adoption of 'Intelligent' Systems for Drug Safety: Lessons Learned from a User-Centred Design Approach.

INTRODUCTION: Information technology (IT) plays an important role in the healthcare landscape via the increasing digitization of medical data and the use of modern computational paradigms such as machine learning (ML) and knowledge graphs (KGs). These 'intelligent' technical paradigms provide a new digital 'toolkit' supporting drug safety and healthcare processes, including 'active pharmacovigilance'. While these technical paradigms are promising, intelligent systems (ISs) are not yet widely adopted by pharmacovigilance (PV) stakeholders, namely the pharma industry, academia/research community, drug safety monitoring organizations, regulatory authorities, and healthcare institutions. The limitations obscuring the integration of ISs into PV activities are multifaceted, involving technical, legal and medical hurdles, and thus require further elucidation. OBJECTIVE: We dissect the abovementioned limitations by describing the lessons learned during the design and implementation of the PVClinical platform, a web platform aiming to support the investigation of potential adverse drug reactions (ADRs), emphasizing the use of knowledge engineering (KE) as its main technical paradigm. RESULTS: To this end, we elaborate on the related 'business processes' (i.e. operational processes) and 'user goals' identified as part of the PVClinical platform design process based on Design Thinking principles. We also elaborate on key challenges restricting the adoption of such ISs and their integration in the clinical setting and beyond. CONCLUSIONS: We highlight the fact that beyond providing analytics and useful statistics to the end user, 'actionability' has emerged as the operational priority identified through the whole process. Furthermore, we focus on the needs for valid, reproducible, explainable and human-interpretable results, stressing the need to emphasize on usability.