An artificial intelligence algorithm for co-clustering to help in pharmacovigilance before and during the COVID-19 pandemic.

AIMS: Monitoring drug safety in real-world settings is the primary aim of pharmacovigilance. Frequent adverse drug reactions (ADRs) are usually identified during drug development. Rare ones are mostly characterized through post-marketing scrutiny, increasingly with the use of data mining and disproportionality approaches, which lead to new drug safety signals. Nonetheless, waves of excessive numbers of reports, often stirred up by social media, may overwhelm and distort this process, as observed recently with levothyroxine or COVID-19 vaccines. As human resources become rarer in the field of pharmacovigilance, we aimed to evaluate the performance of an unsupervised co-clustering method to help the monitoring of drug safety. METHODS: A dynamic latent block model (dLBM), based on a time-dependent co-clustering generative method, was used to summarize all regional ADR reports (n = 45 269) issued between 1 January 2012 and 28 February 2022. After analysis of their intra and extra interrelationships, all reports were grouped into different cluster types (time, drug, ADR). RESULTS: Our model clustered all reports in 10 time, 10 ADR and 9 drug collections. Based on such clustering, three prominent societal problems were detected, subsequent to public health concerns about drug safety, including a prominent media hype about the perceived safety of COVID-19 vaccines. The dLBM also highlighted some specific drug-ADR relationships, such as the association between antiplatelets, anticoagulants and bleeding. CONCLUSIONS: Co-clustering and dLBM appear as promising tools to explore large pharmacovigilance databases. They allow, 'unsupervisedly', the detection, exploration and strengthening of safety signals, facilitating the analysis of massive upsurges of reports.

Vasari Scoring System in Discerning between Different Degrees of Glioma and IDH Status Prediction: A Possible Machine Learning Application?

(1) The aim of our study is to evaluate the capacity of the Visually AcceSAble Rembrandt Images (VASARI) scoring system in discerning between the different degrees of glioma and Isocitrate Dehydrogenase (IDH) status predictions, with a possible application in machine learning. (2) A retrospective study was conducted on 126 patients with gliomas (M/F = 75/51; mean age: 55.30), from which we obtained their histological grade and molecular status. Each patient was analyzed with all 25 features of VASARI, blinded by two residents and three neuroradiologists. The interobserver agreement was assessed. A statistical analysis was conducted to evaluate the distribution of the observations using a box plot and a bar plot. We then performed univariate and multivariate logistic regressions and a Wald test. We also calculated the odds ratios and confidence intervals for each variable and the evaluation matrices with receiver operating characteristic (ROC) curves in order to identify cut-off values that are predictive of a diagnosis. Finally, we did the Pearson correlation test to see if the variables grade and IDH were correlated. (3) An excellent ICC estimate was obtained. For the grade and IDH status prediction, there were statistically significant results by evaluation of the degree of post-contrast impregnation (F4) and the percentage of impregnated area (F5), not impregnated area (F6), and necrotic (F7) tissue. These models showed good performances according to the area under the curve (AUC) values (>70%). (4) Specific MRI features can be used to predict the grade and IDH status of gliomas, with important prognostic implications. The standardization and improvement of these data (aim: AUC > 80%) can be used for programming machine learning software.