The increased consumption of a variety of herbs/supplements has been raising serious health concerns. Owing to an inadequate understanding of herb/supplement–drug interactions, the simultaneous consumption of these products may result in deleterious effects and, in extreme cases, even fatal outcomes. This systematic review is aimed at understanding the knowledge and beliefs about the consumption of herbs/supplements and herb/drug–supplement interactions (HDIs). The study follows the PRISMA guidelines. Four online databases (Web of Science; PubMed; Cochrane; and EBSCOhost) were searched, and a total of 44 studies were included, encompassing 16,929 participants. Herb and supplement consumption is explained mostly by the reported benefits across multiple conditions and ease of use. Regarding HDIs, most people take both herbs/supplements and prescription drugs simultaneously. Only a small percentage of participants have knowledge about their interaction effects, and many reported adverse interactions or side effects. Nevertheless, the main reason for stopping the prescribed drug intake is the perceived lack of its effect, and not due to interactions. Therefore, it is important to increase the knowledge about supplement use so that further strategies can be elaborated to better detect or be alert for whenever a potentially dangerous reaction and/or interaction may occur. This paper raises awareness regarding the need for developing a decision support system and ends with some considerations about the development of a technological solution capable of detecting HDIs and, thereby, aiding in the improvement of pharmacy services.

The growing number of privacy breaches that access confidential and sensitive data has increased the demand for intelligent cybersecurity solutions. Authentication plays a crucial role in safeguarding sensitive data, and a promising authentication method is analyzing the user’s unique typing patterns to ensure access control. KeyRecs, a recent keystroke dynamics dataset containing temporal, demographic, and handedness features, has been proposed but has not yet been benchmarked. This work focuses on analyzing KeyRecs with the primary objective of assessing the feasibility of distinguishing users based on their typing patterns in a zero-effort attack scenario using Machine Learning (ML) models. The study includes an Exploratory Data Analysis, effectively identifying and removing outliers and visualizing distinctive writing patterns. In addition, an evaluation of the fixed-text subset of KeyRecs was established with three algorithms employed for binary and multi-class classification: K-Nearest Neighbours (KNN), Random Forest (RF), and Light Gradient Boosting Machine (LGBM). The results show that LGBM for binary classification achieved the best performance with 80% F1-score, and lowest False Rejection Rate (FRR) and Equal Error Rate (EER) mean values. In conclusion, the KeyRecs dataset offers potential to enhance security measures and access control in digital systems, paving the way for the use of ML in intelligent biometric authentication solutions.

Due to the rising prevalence of polypharmacy, pharmacists face more challenges in ensuring patient safety and optimizing medication management. This paper introduces PharmiTech, a Clinical Decision Support System that leverages Artificial Intelligence (AI) to tackle the growing need for efficient tools to assist pharmacists. The primary focus of the tool is to identify possible herb-drug interactions and instances of prescription drug abuse, combining an expert knowledge base with a supervised classification model and providing user-friendly alerts to pharmacists. To demonstrate the capabilities of the developed tool, this paper presents its functionalities through a case study involving simulated scenarios using de-identified information to maintain the confidentiality of real patients’ personal data. Tested in Portuguese pharmacies, PharmiTech enhances pharmaceutical care, safeguards patient data, and aids pharmacists in informed decision-making, making it a valuable resource for healthcare professionals.

Complementary and alternative medicine are commonly used concomitantly with conventional medications leading to adverse drug reactions and even fatality in some cases. Furthermore, the vast possibility of herb-drug interactions prevents health professionals from remembering or manually searching them in a database. Decision support systems are a powerful tool that can be used to assist clinicians in making diagnostic and therapeutic decisions in patient care. Therefore, an original and hybrid decision support system was designed to identify herb-drug interactions, applying artificial intelligence techniques to identify new possible interactions. Different machine learning models will be used to strengthen the typical rules engine used in these cases. Thus, using the proposed system, the pharmacy community, people's first line of contact within the Healthcare System, will be able to make better and more accurate therapeutic decisions and mitigate possible adverse events.

BACKGROUND Herbal medicines have been used for centuries and are still widely used as alternative or complementary therapies for several conditions. However, the use of these products has raised concerns about their safety, particularly about interactions with conventional drugs, known as herb-drug interactions. OBJECTIVE In this paper, we showcase the ForPharmacy Decision Support System, designed to identify herb-drug interactions. With this system, pharmacists could effectively utilize scientific information, including randomized controlled trials, non-randomized trials, retrospective studies, and case reports, and convert it into practical knowledge for individual patients. METHODS The knowledge-based of ForPharmacy Decision Support System was built by conducting a literature review performed in English, using 3 electronic databases: PubMed, Scopus, and Web of Science, including studies conducted with human beings with no restriction of age, gender or disease. The search focused on herb/plant-drug interactions and included both studies with interactions and with no found interactions. The results allowedconstructing a corresponding knowledge database that feeds the ForPharmacy expert system. Additionally, a user-friendly interface was designed for an easy and efficient access and utilization of the system in an intuitive manner. RESULTS The ForPharmacy Decision Support System assists pharmacists in quickly and efficiently identifying herb-drug interactions. Its knowledge base is composed by a total of 187 interactions involving various herbs. The most frequent herb associated with drug interaction was St. John's Wort, accounting for 10.7% of the interactions, followed by Ginkgo (4.5%), Ginseng (3.6%), and Milk Thistle (3.6%). With respect to drugs, bupropion exhibited the highest probability of interactions, with a percentage of 13.37%, followed by dextromethorphan (11.8%) and warfarin (11.2%). This system offers pharmacists a user-friendly interface, which allows them to identify interactions between a particular drug and herb, or obtain a list of all potential interactions for a specific drug. Additionally, the system provides comprehensive details on the common symptoms associated with each interaction, along with the potential danger and significance value. CONCLUSIONS Our research provides a valuable contribution to the field of herb-drug interactions, as it offers a new Decision Support System that can effectively identify potential interactions and associated symptoms. The system's user-friendly interface and distinct use cases make it a practical tool for healthcare professionals and patients. Future work should focus on expanding the knowledge base to facilitate the application of machine learning models. This would allow autonomous identification of new interactions, thereby enhancing the system's ability to identify novel interactions. Our findings underscore the significance of ongoing research in this area to advance patient safety and prevent adverse events.