Empowering Discovery,
Enhancing Knowledge

Ticagrelor, acting as a reversible platelet aggregation inhibitor of P2Y12 receptors (P2Y12R), is regarded as one of the first-line antiplatelet drugs for acute cardiovascular diseases. Though the probability of ticagrelor resistance is much lower than that of clopidogrel, there have been recent reports of ticagrelor resistance. In this review, we summarized the clinical application of ticagrelor and then presented the criteria and current status of ticagrelor resistance. We further discussed the potential mechanisms for ticagrelor resistance in terms of drug absorption, metabolism, and receptor action. In conclusion, the incidences of ticagrelor resistance fluctuated between 0 and 20%, and possible mechanisms mainly arose from its absorption and receptor action. Specifically, a variety of factors, such as the drug form of ticagrelor, gut microecology, and the expression and function of P-glycoprotein (P-gp) and P2Y12R, have been shown to be associated with ticagrelor resistance. The exact mechanisms of ticagrelor resistance warrant further exploration, which may contribute to the diagnosis and treatment of ticagrelor resistance.

We report on the First Workshop on Human-in-the-loop Data Curation (HIL-DC), which was co-located with the ACM International Conference on Information and Knowledge Management (CIKM) 2022. Data curation, which may include annotation, cleaning, transformation, integration, etc., is a critical step to provide adequate assurances on the quality of analytics and machine learning results. Current approaches include manual, automated, and hybrid human-machine methods to data curation. However, this topic remains relatively unstudied, so our main aim for organizing this workshop was to bring together a group of people from both industry and academia with an interest in the topic, in order to arrive at a shared roadmap for the future. Through a program that included two keynotes, seven peer-reviewed papers, and six lightening talks, we have made initial steps towards a common understanding and shared …

Handling failures in computer vision systems that rely on deep learning models remains a challenge. While an increasing number of methods for bug identification and correction are proposed, little is known about how practitioners actually search for failures in these models. We perform an empirical study to understand the goals and needs of practitioners, the workflows and artifacts they use, and the challenges and limitations in their process. We interview 18 practitioners by probing them with a carefully crafted failure handling scenario. We observe that there is a great diversity of failure handling workflows in which cooperations are often necessary, that practitioners overlook certain types of failures and bugs, and that they generally do not rely on potentially relevant approaches and tools originally stemming from research. These insights allow to draw a list of research opportunities, such as creating a library of best practices and more representative formalisations of practitioners’ goals, developing interfaces to exploit failure handling artifacts, as well as providing specialized training.

In many practical applications, machine learning models are embedded into a pipeline involving a human actor that decides whether to trust the machine prediction or take a default route (eg, classify the example herself). Selective classifiers have the option to abstain from making a prediction on an example they do not feel confident about. Recently, the notion of the value of a machine learning model has been introduced as a way to jointly consider the benefit of a correct prediction, the cost of an error, and that of abstaining. In this paper, we study how active learning of selective classifiers is affected by the focus on value. We show that the performance of the state-of-the-art active learning strategies drops significantly when we evaluate them based on value rather than accuracy. Finally, we propose a novel value-aware active learning strategy that outperforms the state-of-the-art ones when the cost of incorrect predictions substantially outweighs that of abstaining.