Supervised Machine Learning-Based Decision Support for Signal Validation Classification

Imran, Muhammad; Bhatti, Aasia; King, David M.; Lerch, Magnus; Dietrich, Juergen; Doron, Guy; Manlik, Katrin

doi:10.1007/s40264-022-01159-2

Cited by 9 publications

(5 citation statements)

References 24 publications

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“…For instance, ‘reported causality’ (i.e., the availability of information on single-case causality assessments in structured fields) was one of the least frequent features. However, this feature was described as helpful in a decision-support system for signal validation when SDRs were confounded by indication [ 19 ]. To facilitate a critical interpretation of signals, judgments on features of ADR reports may be made clearer in clinical assessments (e.g., [ 20 , 21 ]).…”

Section: Discussionmentioning

confidence: 99%

Signals of Adverse Drug Reactions Communicated by Pharmacovigilance Stakeholders: A Scoping Review of the Global Literature

et al. 2022

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Introduction and Objective Signals of adverse drug reactions (ADRs) can be supported by reports of ADRs and by interventional and non-interventional studies. The evidence base and features of ADR reports that are used to support signals remain to be comprehensively described. To this end, we have undertaken a scoping review. Methods We searched the following databases: PubMed, EMBASE, PsycINFO, Web of Science, and Google Scholar, without language or time restrictions. We also hand searched the bibliographies of relevant studies. We included studies of any design if the results were described as signals . We assessed the levels of evidence using the Oxford Centre for Evidence-Based Medicine (OCEBM) criteria and coded features of reports of ADRs using the Bradford Hill guidelines. Results Overall, 1974 publications reported 2421 studies of signals; 1683/2421 were clinical assessments of anecdotal reports of ADRs, but only 225 (13%) of these included explicit judgments on which features of the ADR reports were supportive of a signal. These 225 studies yielded 228 signals; these were supported by features, which were: ‘experimental evidence’ (i.e., positive dechallenge or rechallenge, 154 instances [68%]), ‘temporality’ (i.e., time to onset, 130 [57%]), ‘exclusion of competing causes’ (49 [21%]), and others (40 [17%]). Positive dechallenge/rechallenge often co-occurred with temporality (77/228). OCEBM 4 (i.e., case series and case-control studies) was the most frequent level of evidence (2078 studies). Between 2013 and 2019, there was a three-fold increase in clinical assessments of reports of ADRs compared with a less than two-fold increase in studies supported by higher levels of evidence (i.e., OCEBM 1–3). We identified an increased rate between 2013 and 2019 in disproportionality analyses (about 15 studies per year), mostly from academia. Conclusions Most signals were supported by temporality and dechallenge/rechallenge, but clear reporting of judgments on causality remains infrequent. The number of studies supported only by anecdotal reports of ADRs increased from year to year. The impact of a growing number of signals of disproportionate reporting communicated without an accompanying clinical assessment should be evaluated.

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Section: Discussionmentioning

confidence: 99%

Signals of Adverse Drug Reactions Communicated by Pharmacovigilance Stakeholders: A Scoping Review of the Global Literature

et al. 2022

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“…It was found that recent papers can be classified into three types of databases, namely clinical [13], [18], [27], [33], registry [10], [12], [20], [24], [25], [29], [34], and knowledge [14], [15], [16], [17], [19], [21], [22], [23], [26], [28], [30], [31], [32], [35]. Additionally, based on review papers of existing XAI models [48], [49], the XAI algorithms used were divided into the following four categories: surrogate The main advantage of an explanatory technique such as SHAP is that it has solid roots in game theory, which ensures that the explanation of a prediction instance is fairly distributed across the features.…”

Section: ) Xai Methodsmentioning

confidence: 99%

Explainable Artificial Intelligence for Patient Safety: A Review of Application in Pharmacovigilance

et al. 2023

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Explainable AI (XAI) is a methodology that complements the black box of artificial intelligence, and its necessity has recently been highlighted in various fields. The purpose of this research is to identify studies in the field of pharmacovigilance using XAI. Though there have been many previous attempts to select papers, with a total of 781 papers being confirmed, only 25 of them manually met the selection criteria. This study presents an intuitive review of the potential of XAI technologies in the field of pharmacovigilance. In the included studies, clinical data, registry data, and knowledge data were used to investigate drug treatment, side effects, and interaction studies based on tree models, neural network models, and graph models. Finally, key challenges for several research issues for the use of XAI in pharmacovigilance were identified. Although artificial intelligence (AI) is actively used in drug surveillance and patient safety, gathering adverse drug reaction information, extracting drug-drug interactions, and predicting effects, XAI is not normally utilized. Therefore, the potential challenges involved in its use alongside future prospects should be continuously discussed.

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“…Moreover, the emergence of additional data sources such as biochemical databases, electronic health records (EHRs), insurance claims or other "Real-World Data" (RWD) and social media (Hussain, 2021; Knowledge Base workgroup of the Observational Health Data Sciences and Informatics (OHDSI) collaborative, 2017) have led to relevant research initiatives (Natsiavas et al, 2019b;Ball et al, 2022). To this end, Machine Learning (ML) algorithms are also under investigation (Lee et al, 2022;Imran et al, 2022), including the use of Natural Language Processing (NLP) which is elaborated to identify ADR mentions in EHRs/clinical notes or other free text/unstructured data. Other Knowledge Engineering approaches (e.g., the use of Semantic Web technologies, ontologies and "reasoning" upon Knowledge Graphs etc.)…”

Section: Introductionmentioning

confidence: 99%

An open-source platform integrating emerging data sources to support multi-modal active pharmacovigilance

Dimitriadis

Dimitsaki

Chytas

et al. 2023

Front. Drug Saf. Regul.

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As new data sources have emerged, the data space which Pharmacovigilance (PV) processes can use has significantly expanded. However, still, the currently available tools do not widely exploit data sources beyond Spontaneous Report Systems built to collect Individual Case Safety Reports (ICSRs). This article presents an open-source platform enabling the integration of heterogeneous data sources to support the analysis of drug safety related information. Furthermore, the results of a comparative study as part of the project’s pilot phase are also presented. Data sources were integrated in the form of four “workspaces”: (a) Individual Case Safety Reports—obtained from OpenFDA, (b) Real-World Data (RWD) —using the OMOP-CDM data model, (c) social media data—collected via Twitter, and (d) scientific literature—retrieved from PubMed. Data intensive analytics are built for each workspace (e.g., disproportionality analysis metrics are used for OpenFDA data, descriptive statistics for OMOP-CDM data and twitter data streams etc.). Upon these workspaces, the end-user sets up “investigation scenarios” defined by Drug-Event Combinations (DEC). Specialized features like detailed reporting which could be used to support reports for regulatory purposes and also “quick views” are provided to facilitate use where detailed statistics might not be needed and a qualitative overview of the available information might be enough (e.g., clinical environment). The platform’s technical features are presented as Supplementary Material via a walkthrough of an example “investigation scenario”. The presented platform is evaluated via a comparative study against the EVDAS system, conducted by PV professionals. Results from the comparative study, show that there is indeed a need for relevant technical tools and the ability to draw recent data from heterogeneous data sources is appreciated. However, a reluctance by end-users is also outlined as they feel technical improvements and systematic training are required before the potential adoption of the presented software. As a whole, it is concluded that integrating such a platform in real-world setting is far from trivial, requiring significant effort on training and usability aspects.

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Supervised Machine Learning-Based Decision Support for Signal Validation Classification

Cited by 9 publications

References 24 publications

Signals of Adverse Drug Reactions Communicated by Pharmacovigilance Stakeholders: A Scoping Review of the Global Literature

Signals of Adverse Drug Reactions Communicated by Pharmacovigilance Stakeholders: A Scoping Review of the Global Literature

Explainable Artificial Intelligence for Patient Safety: A Review of Application in Pharmacovigilance

An open-source platform integrating emerging data sources to support multi-modal active pharmacovigilance

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