ABSTRACT
Estamos asistiendo a una verdadera revolución tecnológi-ca en el campo de la salud. Los procesos basados en la aplicación de la inteligencia artificial (IA) y el aprendizaje automático (AA) están llegando progresivamente a todas las áreas disciplinares, y su aplicación en el campo de las enfermedades infecciosas es ya vertiginoso, acelerado por la pandemia de COVID-19.Hoy disponemos de herramientas que no solamente pue-den asistir o llevar adelante el proceso de toma de deci-siones basadas en guías o algoritmos, sino que también pueden modificar su desempeño a partir de los procesos previamente realizados. Desde la optimización en la identificación de microorganis-mos resistentes, la selección de candidatos a participar en ensayos clínicos, la búsqueda de nuevos agentes terapéu-ticos antimicrobianos, el desarrollo de nuevas vacunas, la predicción de futuras epidemias y pandemias, y el segui-miento clínico de pacientes con enfermedades infecciosas hasta la asignación de recursos en el curso de manejo de un brote son actividades que hoy ya pueden valerse de la inteligencia artificial para obtener un mejor resultado. El desarrollo de la IA tiene un potencial de aplicación expo-nencial y sin dudas será uno de los determinantes principa-les que moldearán la actividad médica del futuro cercano.Sin embargo, la maduración de esta tecnología, necesaria para su inserción definitiva en las actividades cotidianas del cuidado de la salud, requiere la definición de paráme-tros de referencia, sistemas de validación y lineamientos regulatorios que todavía no existen o son aún solo inci-pientes
We are in the midst of a true technological revolution in healthcare. Processes based upon artificial intelligence and machine learning are progressively touching all disciplinary areas, and its implementation in the field of infectious diseases is astonishing, accelerated by the COVID-19 pandemic. Today we have tools that can not only assist or carry on decision-making processes based upon guidelines or algorithms, but also modify its performance from the previously completed tasks. From optimization of the identification of resistant pathogens, selection of candidates for participating in clinical trials, the search of new antimicrobial therapeutic agents, the development of new vaccines, the prediction of future epidemics and pandemics, the clinical follow up of patients suffering infectious diseases up to the resource allocation in the management of an outbreak, are all current activities that can apply artificial intelligence in order to improve their final outcomes.This development has an exponential possibility of application, and is undoubtedly one of the main determinants that will shape medical activity in the future.Notwithstanding the maturation of this technology that is required for its definitive insertion in day-to-day healthcare activities, should be accompanied by definition of reference parameters, validation systems and regulatory guidelines that do not exist yet or are still in its initial stages
Subject(s)
Humans , Male , Female , Artificial Intelligence/trends , Communicable Diseases , Validation Studies as Topic , Machine Learning/trendsABSTRACT
A crescente digitalização e aplicação de inteligência artificial (IA) em problemas complexos do mundo real, tem potencial de melhorar os serviços de saúde, inclusive da atuação dos farmacêuticos no processo do cuidado. O objetivo deste estudo foi identificar na literatura científica, estudos que testam algoritmos de aprendizado de máquina (Machine Learning ML) aplicados as atividades de farmacêuticos clínicos no cuidado ao paciente. Trata-se de uma revisão integrativa, realizada nas bases de dados, Pubmed, Portal BVS, Cochrane Library e Embase. Artigos originais, relacionados ao objetivo proposto, disponíveis e publicados antes de 31 de dezembro de 2021, foram incluídos, sem limitações de idioma. Foram encontrados 831 artigos, sendo 5 incluídos relacionados as atividades inseridas nos serviços de revisão da farmacoterapia (3) e monitorização terapêutica (2). Foram utilizadas técnicas supervisionadas (3) e não supervisionadas (2) de ML, com variedade de algoritmos testados, sendo todos os estudos publicados recentemente (2019-2021). Conclui-se que a aplicação da IA na farmácia clínica, ainda é discreta, sinalizando os desafios da era digital.
The growing application of artificial intelligence (AI) in complex real-world problems has shown an enormous potential to improve health services, including the role of pharmacists in the care process. Thus, the objective of this study was to identify, in the scientific literature, studies that addressed the use of machine learning (ML) algorithms applied to the activities of clinical pharmacists in patient care. This is an integrative review, conducted in the databases Pubmed, VHL Regional Portal, Cochrane Library and Embase. Original articles, related to the proposed topic, which were available and published before December 31, 2021, were included, without language limitations. There were 831 articles retrieved 5 of which were related to activities included in the pharmacotherapy review services (3) and therapeutic monitoring (2). Supervised (3) and unsupervised (2) ML techniques were used, with a variety of algorithms tested, with all studies published recently (20192021). It is concluded that the application of AI in clinical pharmacy is still discreet, signaling the challenges of the digital age.
Subject(s)
Pharmaceutical Services/organization & administration , Artificial Intelligence/trends , Machine Learning/trendsABSTRACT
Abstract Infertility is becoming a growing issue in almost all countries. Assisted Reproductive Technologies (ART) are recent development in treating infertility that give hope to the infertile couples. However, the pregnancy rates achieved with the aid of ART is considerably low, as success in ART is not only based on the treatment but also on many other controllable and uncontrollable biological, social, and environmental features. High expenditures and painful process of ART cycles are the two major barriers for opting for ART. Moreover, ART treatments are not covered by any health insurance schemes. Computational prediction models could be used to improve the success rate by predicting the treatment outcome, before the start of an ART cycle. This may suggest the couples and the doctors to decide on the next course of action i.e. either to opt for ART or opt for correcting determinants or quit the ART. With the intension to improve the success rate of ART by providing decision support system to the physicians as well to the patients before entering into the treatment this research work proposes a dynamic model for ART outcome prediction using Machine Learning (ML) techniques. The proposed dynamic model is partially implemented with the help of an ensemble of heterogeneous incremental classifier and its performance is compared with state-of-art classifiers such as Naïve Bayes (NB), Random Forest (RF), K-star etc.,using ART dataset. Performance of the model is evaluated with various metrics such as accuracy, Precision Recall Curve (PRC), Receiver Operating Characteristic (ROC), F-Measure etc., However, ROC cure area is taken as the chief metric. Evaluation results shows that the model achieves the performance with the ROC area value of 94.1 %.