Electronic Health Records (EHR) have brought valuable improvements to hospital practices by integrating patient information. In fact, the understanding of this data can prevent mistakes that may put patients' lives at risk. Nonetheless, to the best of our knowledge, there are no previous studies addressing the automatic detection of outlier prescriptions, regarding dosage and frequency. In this paper, we propose an unsupervised method, called Density-Distance-Centrality (DDC), to detect potential outlier prescriptions. A dataset with 563 thousand prescribed medications was used to assess our proposed approach against different state-of-the-art techniques for outlier detection. In the experiments, our approach achieves better results in the task of overdose and underdose detection in medical prescriptions, compared to other methods applied to this problem. Additionally, most of the false positive instances detected by our algorithm were potential prescriptions errors.

Resumo Objetivo Comparar o desempenho de modelos de aprendizado de máquina com o Medication Fall Risk Score (MFRS) na previsão de risco de queda relacionado a medicamentos prescritos. Métodos Trata-se de um estudo caso-controle retrospectivo de pacientes adultos e idosos de um hospital terciário de Porto Alegre, RS, Brasil. Medicamentos prescritos e classes de medicamentos foram investigados. Os dados foram exportados para o software RStudio para análise estatística. As variáveis foram analisadas por meio dos algoritmos de Regressão Logística, Naive [...] Resumo Objetivo Comparar o desempenho de modelos de aprendizado de máquina com o Medication Fall Risk Score (MFRS) na previsão de risco de queda relacionado a medicamentos prescritos. Métodos Trata-se de um estudo caso-controle retrospectivo de pacientes adultos e idosos de um hospital terciário de Porto Alegre, RS, Brasil. Medicamentos prescritos e classes de medicamentos foram investigados. Os dados foram exportados para o software RStudio para análise estatística. As variáveis foram analisadas por meio dos algoritmos de Regressão Logística, Naive

Background: Drug-induced liver injury (DILI) is an important but underdiagnosed adverse drug reaction in hospitalized patients. The complexity of diagnosis, under-reporting, and a lack of standardized assessment tools contribute to underestimation of its prevalence. Objectives: To estimate the prevalence of DILI in a tertiary care hospital and to discuss the role of clinical pharmacists in using the Roussel Uclaf Causality Assessment Method (RUCAM) to detect, assess, and manage DILI. Methods: This cross-sectional study included patients who were admitted between January and June 2022 to a tertiary care hospital in Porto Alegre, Brazil. All hospitalized patients with alanine aminotransferase (ALT) levels at least 3 times the upper limit of normal at any point during the hospital stay were screened. Suspected cases of DILI were evaluated by trained pharmacists using the RUCAM. Data were collected retrospectively from electronic medical records and analyzed using descriptive and inferential statistics. Results: Of 56 014 patients admitted to the hospital during the study period, 1274 had elevated ALT, of whom 38 were classified by the RUCAM as having DILI. Among these, 33 (87%) experienced development of DILI during the hospital stay. Most of the patients (n = 29, 76%) were in general hospital wards, and this factor showed a significant association with occurrence of DILI. Anti-infectives were the most commonly implicated drugs (69%), specifically the combination of rifampicin, isoniazid, pyrazinamide, and ethambutol and various β-lactams. Simvastatin was also commonly implicated. None of the DILI cases were reported to the institutional pharmacovigilance system. Conclusions: DILI remains under-recognized and under-reported in hospital settings. This study reinforces the potential role of clinical pharmacists in active surveillance using RUCAM and highlights the need for institutional strategies prioritizing the monitoring of liver function in high-risk patients, particularly on general wards. Keywords: drug-induced liver injury, hospital pharmacy, patient safety, pharmacovigilance RÉSUMÉ Contexte : La lésion hépatique d’origine médicamenteuse (LHOM) est une réaction indésirable importante, mais souvent sous-diagnostiquée chez les patients hospitalisés. La complexité du diagnostic, la sous déclaration des cas et le manque d’outils d’évaluation standardisés contribuent à la sous-estimation de sa prévalence. Objectifs : Estimer la prévalence de la LHOM dans un hôpital de soins tertiaires et discuter du rôle des pharmaciens cliniciens dans la détection, l’évaluation et la prise en charge de la LHOM à l’aide de la méthode d’évaluation de la causalité Roussel Uclaf (échelle RUCAM). Méthodologie : Cette étude transversale comprenait des patients admis de janvier à juin 2022 dans un établissement de soins tertiaires à Porto Alegre, au Brésil. Tous les patients hospitalisés dont le taux d’alanine aminotransférase (ALT) était supérieur ou égal à trois fois la limite supérieure de la normale à un moment quelconque de l’hospitalisation ont été sélectionnés. Les cas suspects de LHOM ont été évalués à l’aide de l’échelle RUCAM par des pharmaciens formés. Les données ont été recueillies rétrospectivement à partir des dossiers médicaux électroniques et analysées à l’aide de statistiques descriptives et inférentielles. Résultats : Sur les 56 014 patients admis à l’hôpital pendant la période de l’étude, les taux d’ALT de 1274 patients étaient élevés, dont 38 qui ont été classés comme ayant une LHOM selon l’échelle RUCAM. Parmi ceux-ci, 33 patients (87 %) ont développé une LHOM au cours de leur séjour. La majorité des patients (n = 29, 76 %) se trouvaient dans des unités d’hospitalisation générale. Ce facteur a démontré une association significative avec la survenue de la LHOM. Les médicaments anti-infectieux étaient les plus fréquemment associés à cette dernière (69 %), en particulier la combinaison rifampicine, isoniazide, pyrazinamide et éthambutol, ainsi que divers β-lactamines. La simvastatine y était, elle aussi, fréquemment associée. Aucun des cas de LHOM n’a été signalé au système institutionnel de pharmacovigilance. Conclusions : La LHOM demeure sous-reconnue et sous-déclarée en milieu hospitalier. Cette étude renforce le rôle potentiel des pharmaciens cliniciens dans la surveillance active à l’aide de l’échelle RUCAM et souligne la nécessité de stratégies institutionnelles visant à prioriser le suivi de la fonction hépatique chez les patients à haut risque, en particulier dans les unités d’hospitalisation générales. Mots-clés : lésion hépatique d’origine médicamenteuse, pharmacie d’hôpital, sécurité du patient, pharmacovigilance

Everyone who uses any digital platform in the daily routine has already been surprised by some sudden ad or product advertisement about which some information has been sought on the Internet. Coincidence? Of course not! This is just one example of how artificial intelligence is inserted into our daily lives. It is in the platforms for music streaming, movies, shopping for any product, in traffic applications, in the stock market. Each "like", each share, each post shows a pattern of consumer preference, a characteristic that can be used to direct advertisements in order to advertise or market a product to a specific target. This is already happening, it is not part of the future. Artificial intelligence is already part of our present. But how do these platforms manage to "guess" our preferences or tastes and hit exactly what we were looking for? In reality nothing is guessed, it is learned. Through computer modeling, these systems learn from the examples that we ourselves give them. We feed these systems on a daily basis. Just like children, who learn many things by example (languages, for instance) before they even go to school, these systems are also capable of learning. A child learns that a dog is different from a cat when it sees examples of several dogs and several cats. So a child can learn the differences between both animals. Algorithms learn the same way, through examples. This is what we call "machine learning," a sub-area of artificial intelligence (AI). It is an advance for society, but it must be applied with ethics and transparency (see the Netflix documentary Coded Bias). Moving away from the market sphere and thinking about health care, machine learning has also been widely employed, because these systems have the ability to learn using endless amount of patient and hospital data (Big Data). In this sense, AI-based systems have been developed aiming at improving patient care, from the organization of triage systems at clinics and hospitals, patient scheduling, organization of test result delivery, preventing errors in drug prescriptions, as well as predicting and assisting in disease diagnosis. The artificial intelligence literature in the medical field is already vast. In dentistry, research has focused on the use of convolutional neural networks (CNN) in dental radiology. Tools are produced for researchers and system developers that aim at assisting clinicians in imaging diagnosis, for example, of dental caries, periapical lesions, bone resorption, among other important outcomes. Some companies, in Brazil and worldwide, have already seen a potential market in the application of these neural networks, and are providing software to assist in the analysis of radiographic images. Far from being able to replace health professionals, this technology should be used to improve the work of dentists and bring more security in diagnosis. Trying to replace a health professional with artificial intelligence, especially in dentistry, is impossible and not productive at all (see Eric Topol's book Deep Medicine). Information technology as an ally will bring many benefits to dentistry, not only in radiology. The analysis of digital cohorts (electronic patient records) with machine learning algorithms can bring new insights to Science. Such algorithms are able to cross-reference thousands of predictive attributes with various endpoints to define which information is most relevant for qualitative analyses. It is the new advanced statistics. For this reason, it is especially important to emphasize the need to build a large-scale public dental dataset to make the clinical application of AI possible. The challenge now is to improve the quality of the datasets to build really accurate machine learning algorithms. Finally, it would be very useful for dentists if these developed machine learning systems become applications that could be widely available and spread to the dental community. The spectrum of AI is huge! Try doing a search today on some topic and wait for the algorithm to work! It will offer you all the information, based on the search example you yourself have offered! This is AI in our lives, no future, but a present! Keywords Artificial intelligence; Health care.