Predictors of mechanical ventilation and mortality in critically ill patients with COVID-19 pneumonia / Predictores de ventilación mecánica y mortalidad en pacientes críticos con neumonía por COVID-19

Objective To determine if potential predictors for invasive mechanical ventilation (IMV) are also determinants for mortality in COVID-19-associated acute respiratory distress syndrome (C-ARDS). Design Single center highly detailed longitudinal observational study. Setting Tertiary hospital ICU: two first COVID-19 pandemic waves, Madrid, Spain. Patients or participants : 280 patients with C-ARDS, not requiring IMV on admission. Interventions None. Main variables of interest : Target: endotracheal intubation and IMV, mortality. Predictors: demographics, hourly evolution of oxygenation, clinical data, and laboratory results. Results The time between symptom onset and ICU admission, the APACHE II score, the ROX index, and procalcitonin levels in blood were potential predictors related to both IMV and mortality. The ROX index was the most significant predictor associated with IMV, while APACHE II, LDH, and DaysSympICU were the most with mortality. Conclusions According to the results of the analysis, there are significant predictors linked with IMV and mortality in C-ARDS patients, including the time between symptom onset and ICU admission, the severity of the COVID-19 waves, and several clinical and laboratory measures. These findings may help clinicians to better identify patients at risk for IMV and mortality and improve their management. (AU)

Objetivo Determinar si las variables clínicas independientes que condicionan el inicio de ventilación mecánica invasiva (VMI) son los mismos que condicionan la mortalidad en el síndrome de distrés respiratorio agudo asociado con COVID-19 (C-SDRA). Diseño Estudio observacional longitudinal en un solo centro. Ámbito UCI, hospital terciario: primeras dos olas de COVID-19 en Madrid, España. Pacientes o participantes 280 pacientes con C-SDRA que no requieren VMI al ingreso en UCI. Intervenciones Ninguna. Principales variables de interés Objetivo: VMI y Mortalidad. Predictores: demográficos, variables clínicas, resultados de laboratorio y evolución de la oxigenación. Resultados El tiempo entre el inicio de los síntomas y el ingreso en la UCI, la puntuación APACHE II, el índice ROX y los niveles de procalcitonina en sangre eran posibles predictores relacionados tanto con la IMV como con la mortalidad. El índice ROX fue el predictor más significativo asociada con la IMV, mientras que APACHE II, LDH y DaysSympICU fueron los más influyentes en la mortalidad. Conclusiones Según los resultados obtenidos se identifican predictores significativos vinculados con la VMI y mortalidad en pacientes con C-ARDS, incluido el tiempo entre el inicio de los síntomas y el ingreso en la UCI, la gravedad de las olas de COVID-19 y varias medidas clínicas y de laboratorio. Estos hallazgos pueden ayudar a los médicos a identificar mejor a los pacientes en riesgo de IMV y mortalidad y mejorar su manejo. (AU)

Revisión sistemática de la aplicación de algoritmos de «machine learning» en la esclerosis múltiple / A systematic review of the application of machine-learning algorithms in multiple sclerosis

Introducción: La aplicación de la inteligencia artificial y en particular de algoritmos de aprendizaje automático o «machine learning» (ML) constituye un desafío y al mismo tiempo una gran oportunidad en diversas disciplinas científicas, técnicas y clínicas. Las aplicaciones específicas en el estudio de la esclerosis múltiple (EM) no han sido una excepción mostrando un creciente interés en los últimos años. Objetivo: Realizar una revisión sistemática de la aplicación de algoritmos de ML en la EM. Material y métodos: Empleando el motor de búsqueda de libre acceso PubMed que accede a la base de datos MEDLINE, se seleccionaron aquellos estudios que incluyeran simultáneamente los dos siguientes conceptos de búsqueda: «machine learning» y «multiple sclerosis». Se rechazaron aquellos estudios que fueran revisiones, estuvieran en otro idioma que no fuera el castellano o el inglés, y aquellos trabajos que tuvieran un carácter técnico y no fueran aplicados para la EM. Se seleccionaron como válidos 76 artículos y fueron rechazados 38. Conclusiones: Tras la revisión de los estudios seleccionados, se pudo observar que la aplicación del ML en la EM se concentró en cuatro categorías: 1) clasificación de subtipos de pacientes dentro de la enfermedad; 2) diagnóstico del paciente frente a controles sanos u otras enfermedades; 3) predicción de la evolución o de la respuesta a intervenciones terapéuticas y por último 4) otros enfoques. Los resultados hallados hasta la fecha muestran que los diferentes algoritmos de ML pueden ser un gran apoyo para el profesional sanitario tanto en la clínica como en la investigación de la EM.(AU)

Introduction: The applications of artificial intelligence, and in particular automatic learning or “machine learning” (ML), constitute both a challenge and a great opportunity in numerous scientific, technical, and clinical disciplines. Specific applications in the study of multiple sclerosis (MS) have been no exception, and constitute an area of increasing interest in recent years. Objective: We present a systematic review of the application of ML algorithms in MS. Materials and methods: We used the PubMed search engine, which allows free access to the MEDLINE medical database, to identify studies including the keywords “machine learning” and “multiple sclerosis.” We excluded review articles, studies written in languages other than English or Spanish, and studies that were mainly technical and did not specifically apply to MS. The final selection included 76 articles, and 38 were rejected. Conclusions: After the review process, we established 4 main applications of ML in MS: 1) classifying MS subtypes; 2) distinguishing patients with MS from healthy controls and individuals with other diseases; 3) predicting progression and response to therapeutic interventions; and 4) other applications. Results found to date have shown that ML algorithms may offer great support for health professionals both in clinical settings and in research into MS.(AU)

Aprendizaje automático para la toma de decisiones en cardiología: una revisión narrativa para ayudar a recorrer el nuevo panorama / Machine learning for decision-making in cardiology: a narrative review to aid navigating the new landscape

El aprendizaje automático (machine learning) en cardiología es cada vez más frecuente en la literatura médica, pero los modelos de aprendizaje automático aún no han producido un cambio generalizado de la práctica clínica. En parte esto se debe a que el lenguaje utilizado para describir el aprendizaje automático procede de la informática y resulta menos familiar a los lectores de revistas clínicas. En esta revisión narrativa se proporcionan, en primer lugar, algunas orientaciones sobre cómo leer las revistas de aprendizaje automático y, a continuación, orientaciones adicionales para quienes se plantean iniciar un estudio utilizando el aprendizaje automático. Por último, se ilustra el estado actual de la técnica con breves resúmenes de 5 artículos que van desde un modelo de aprendizaje automático muy sencillo hasta otros muy sofisticados.(AU)

Machine learning in cardiology is becoming more commonplace in the medical literature; however, machine learning models have yet to result in a widespread change in practice. This is partly due to the language used to describe machine, which is derived from computer science and may be unfamiliar to readers of clinical journals. In this narrative review, we provide some guidance on how to read machine learning journals and additional guidance for investigators considering instigating a study using machine learning. Finally, we illustrate the current state of the art with brief summaries of 5 articles describing models that range from the very simple to the highly sophisticated.(AU)

Inteligencia artificial en enfermedades infecciosas / Artificial intelligence in infectious diseases

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

Aprendizado de máquina nos serviços farmacêuticos: uma revisão integrativa / Machine learning in pharmaceutical services: an integrative review

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.

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms.

In the farming industry, the Internet of Things (IoT) is crucial for boosting utility. Innovative agriculture practices and medical informatics have the potential to increase crop yield while using the same amount of input. Individuals can benefit from the Internet of Things in various ways. The intelligent farms require the creation of an IoT-based infrastructure based on sensors, actuators, embedded systems, and a network connection. The agriculture sector will gain new advantages from machine learning and IoT data analytics in terms of improving crop output quantity and quality to fulfill rising food demand. This paper described an intelligent medical informatics farming system with predictive data analytics on sensing parameters, utilizing a supervised machine learning approach in an intelligent agricultural system. The four essential components of the proposed approach are the cloud layer, fog layer, edge layer, and sensor layer. The primary goal is to enhance production and provide organic farming by adjusting farming conditions as per plant needs that are considered in experimentation. The use of machine learning on acquired sensor data from a prototype embedded model is investigated for regulating the actuators in the system. Then, an analytics and decision-making system was built at the fog layer, employing two supervised machine learning approaches including classification and regression algorithms using a support vector machine (SVM) and artificial neural network (ANN) for effective computation over the cloud layer. The experimental results are evaluated and analyzed in MATLAB software, and it is found that the classification accuracy using SVM is much better as compared to ANN and other state of art methods.

An automatic method to quantify trichomes in Arabidopsis thaliana.

Trichomes are unicellular or multicellular hair-like appendages developed on the aerial plant epidermis of most plant species that act as a protective barrier against natural hazards. For this reason, evaluating the density of trichomes is a valuable approach for elucidating plant defence responses to a continuous challenging environment. However, previous methods for trichome counting, although reliable, require the use of specialised equipment, software or previous manipulation steps of the plant tissue, which poses a complicated hurdle for many laboratories. Here, we propose a new fast, accessible and user-friendly method to quantify trichomes that overcomes all these drawbacks and makes trichome quantification a reachable option for the scientific community. Particularly, this new method is based on the use of machine learning as a reliable tool for quantifying trichomes, following an Ilastik-Fiji tandem approach directly performed on 2D images. Our method shows high reliability and efficacy on trichome quantification in Arabidopsis thaliana by comparing manual and automated results in Arabidopsis accessions with diverse trichome densities. Due to the plasticity that machine learning provides, this method also showed adaptability to other plant species, demonstrating the ability of the method to spread its scope to a greater scientific community.

Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review.

Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a combination of climatic, environmental, and human mobility factors. Arboviruses prediction models can be a support tool for decision-making by public health agents. In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dynamics. To carry out this review, we searched reputable scientific bases such as IEE Xplore, PubMed, Science Direct, Springer Link, and Scopus. We search for studies published between the years 2015 and 2020, using a search string. A total of 429 articles were returned, however, after filtering by exclusion and inclusion criteria, 139 were included. Through this systematic review, it was possible to identify the challenges present in the construction of arboviruses prediction models, as well as the existing gap in the construction of spatiotemporal models.

Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions.

One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. Traditional medical procedures (angiography) for the diagnosis of heart disease have higher cost as well as serious health concerns. Therefore, researchers have developed various automated diagnostic systems based on machine learning (ML) and data mining techniques. ML-based automated diagnostic systems provide an affordable, efficient, and reliable solutions for heart disease detection. Various ML, data mining methods, and data modalities have been utilized in the past. Many previous review papers have presented systematic reviews based on one type of data modality. This study, therefore, targets systematic review of automated diagnosis for heart disease prediction based on different types of modalities, i.e., clinical feature-based data modality, images, and ECG. Moreover, this paper critically evaluates the previous methods and presents the limitations in these methods. Finally, the article provides some future research directions in the domain of automated heart disease detection based on machine learning and multiple of data modalities.

Machine Learning and Artificial Intelligence in Pharmaceutical Research and Development: a Review.

Over the past decade, artificial intelligence (AI) and machine learning (ML) have become the breakthrough technology most anticipated to have a transformative effect on pharmaceutical research and development (R&D). This is partially driven by revolutionary advances in computational technology and the parallel dissipation of previous constraints to the collection/processing of large volumes of data. Meanwhile, the cost of bringing new drugs to market and to patients has become prohibitively expensive. Recognizing these headwinds, AI/ML techniques are appealing to the pharmaceutical industry due to their automated nature, predictive capabilities, and the consequent expected increase in efficiency. ML approaches have been used in drug discovery over the past 15-20 years with increasing sophistication. The most recent aspect of drug development where positive disruption from AI/ML is starting to occur, is in clinical trial design, conduct, and analysis. The COVID-19 pandemic may further accelerate utilization of AI/ML in clinical trials due to an increased reliance on digital technology in clinical trial conduct. As we move towards a world where there is a growing integration of AI/ML into R&D, it is critical to get past the related buzz-words and noise. It is equally important to recognize that the scientific method is not obsolete when making inferences about data. Doing so will help in separating hope from hype and lead to informed decision-making on the optimal use of AI/ML in drug development. This manuscript aims to demystify key concepts, present use-cases and finally offer insights and a balanced view on the optimal use of AI/ML methods in R&D.