Causal Deep Learning for the Detection of Adverse Drug Reactions: Drug-Induced Acute Kidney Injury as a Case Study.

Causal Deep/Machine Learning (CDL/CML) is an emerging Artificial Intelligence (AI) paradigm. The combination of causal inference and AI could mine explainable causal relationships between data features, providing useful insights for various applications, e.g.

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.

Benchmarking of Machine Learning classifiers on plasma proteomic for COVID-19 severity prediction through interpretable artificial intelligence.

The SARS-CoV-2 pandemic highlighted the need for software tools that could facilitate patient triage regarding potential disease severity or even death. In this article, an ensemble of Machine Learning (ML) algorithms is evaluated in terms of predicting the severity of their condition using plasma proteomics and clinical data as input. An overview of AI-based technical developments to support COVID-19 patient management is presented outlining the landscape of relevant technical developments.

Exploiting Social Media for Active Pharmacovigilance: The PVClinical Social Media Workspace.

The value of social media data for Adverse Drug Reaction (ADR) monitoring is actively investigated. While social media provide a vast amount of data, these data are hard to analyse due to their unstructured nature and lack of credibility. Despite these challenges, social media have been identified as a potentially useful data source, potentially able to "strengthen" the evidence for new ADRs.