Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Contribuciones de las técnicas machine learning a la cardiología. Predicción de reestenosis tras implante de stent coronario

[ES]Antecedentes: Existen pocos temas de actualidad equiparables a la posibilidad de la tecnología actual para desarrollar las mismas capacidades que el ser humano, incluso en medicina. Esta capacidad de simular los procesos de inteligencia humana por parte de máquinas o sistemas informáticos es lo que conocemos hoy en día como inteligencia artificial. Uno de los campos de la inteligencia artificial con mayor aplicación a día de hoy en medicina es el de la predicción, recomendación o diagnóstico, donde se aplican las técnicas machine learning. Asimismo, existe un creciente interés en las técnicas de medicina de precisión, donde las técnicas machine learning pueden ofrecer atención médica individualizada a cada paciente. El intervencionismo coronario percutáneo (ICP) con stent se ha convertido en una práctica habitual en la revascularización de los vasos coronarios con enfermedad aterosclerótica obstructiva significativa. El ICP es asimismo patrón oro de tratamiento en pacientes con infarto agudo de miocardio; reduciendo las tasas de muerte e isquemia recurrente en comparación con el tratamiento médico. El éxito a largo plazo del procedimiento está limitado por la reestenosis del stent, un proceso patológico que provoca un estrechamiento arterial recurrente en el sitio de la ICP. Identificar qué pacientes harán reestenosis es un desafío clínico importante; ya que puede manifestarse como un nuevo infarto agudo de miocardio o forzar una nueva resvascularización del vaso afectado, y que en casos de reestenosis recurrente representa un reto terapéutico. Objetivos: Después de realizar una revisión de las técnicas de inteligencia artificial aplicadas a la medicina y con mayor profundidad, de las técnicas machine learning aplicadas a la cardiología, el objetivo principal de esta tesis doctoral ha sido desarrollar un modelo machine learning para predecir la aparición de reestenosis en pacientes con infarto agudo de miocardio sometidos a ICP con implante de un stent. Asimismo, han sido objetivos secundarios comparar el modelo desarrollado con machine learning con los scores clásicos de riesgo de reestenosis utilizados hasta la fecha; y desarrollar un software que permita trasladar esta contribución a la práctica clínica diaria de forma sencilla. Para desarrollar un modelo fácilmente aplicable, realizamos nuestras predicciones sin variables adicionales a las obtenidas en la práctica rutinaria. Material: El conjunto de datos, obtenido del ensayo GRACIA-3, consistió en 263 pacientes con características demográficas, clínicas y angiográficas; 23 de ellos presentaron reestenosis a los 12 meses después de la implantación del stent. Todos los desarrollos llevados a cabo se han hecho en Python y se ha utilizado computación en la nube, en concreto AWS (Amazon Web Services). Metodología: Se ha utilizado una metodología para trabajar con conjuntos de datos pequeños y no balanceados, siendo importante el esquema de validación cruzada anidada utilizado, así como la utilización de las curvas PR (precision-recall, exhaustividad-sensibilidad), además de las curvas ROC, para la interpretación de los modelos. Se han entrenado los algoritmos más habituales en la literatura para elegir el que mejor comportamiento ha presentado. Resultados: El modelo con mejores resultados ha sido el desarrollado con un clasificador extremely randomized trees; que superó significativamente (0,77; área bajo la curva ROC a los tres scores clínicos clásicos; PRESTO-1 (0,58), PRESTO-2 (0,58) y TLR (0,62). Las curvas exhaustividad sensibilidad ofrecieron una imagen más precisa del rendimiento del modelo extremely randomized trees que muestra un algoritmo eficiente (0,96) para no reestenosis, con alta exhaustividad y alta sensibilidad. Para un umbral considerado óptimo, de 1,000 pacientes sometidos a implante de stent, nuestro modelo machine learning predeciría correctamente 181 (18%) más casos en comparación con el mejor score de riesgo clásico (TLR). Las variables más importantes clasificadas según su contribución a las predicciones fueron diabetes, enfermedad coronaria en 2 ó más vasos, flujo TIMI post-ICP, plaquetas anormales, trombo post-ICP y colesterol anormal. Finalmente, se ha desarrollado una calculadora para trasladar el modelo a la práctica clínica. La calculadora permite estimar el riesgo individual de cada paciente y situarlo en una zona de riesgo, facilitando la toma de decisión al médico en cuanto al seguimiento adecuado para el mismo. Conclusiones: Aplicado inmediatamente después de la implantación del stent, un modelo machine learning diferencia mejor a aquellos pacientes que presentarán o no reestenosis respecto a los discriminadores clásicos actuales

Applications of Artificial Intelligence in Battling Against Covid-19: A Literature Review

© 2020 Elsevier Ltd. All rights reserved.Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.Peer reviewe

Clinical Studies, Big Data, and Artificial Intelligence in Nephrology and Transplantation

In recent years, artificial intelligence has increasingly been playing an essential role in diverse areas in medicine, assisting clinicians in patient management. In nephrology and transplantation, artificial intelligence can be utilized to enhance clinical care, such as through hemodialysis prescriptions and the follow-up of kidney transplant patients. Furthermore, there are rapidly expanding applications and validations of comprehensive, computerized medical records and related databases, including national registries, health insurance, and drug prescriptions. For this Special Issue, we made a call to action to stimulate researchers and clinicians to submit their invaluable works and present, here, a collection of articles covering original clinical research (single- or multi-center), database studies from registries, meta-analyses, and artificial intelligence research in nephrology including acute kidney injury, electrolytes and acid–base, chronic kidney disease, glomerular disease, dialysis, and transplantation that will provide additional knowledge and skills in the field of nephrology and transplantation toward improving patient outcomes

Machine Learning Modelling of Critical Care Patients in the Intensive Care Units

The ICU is a fast-paced data-rich environment which treats the most critically ill patients. On average, over 15 % of patients admitted to the ICU amount in mortality. Therefore, machine learning (ML) is paramount to aiding the optimisation and inference of insight in critical care. In addition, the early and accurate evaluation of the severity at the time of admission is significant for physicians. Such evaluations make patient management more effective as they are more likely to predict whose physical conditions may worsen. Moreover, ML techniques could potentially enhance patients' experience in the clinical setting by providing medical alerts and insight into future events occurring during hospitalisation. The need for interpretable models is crucial in the ICU and clinical setting, as it is vital to explain a decision that leads to any course of action related to an individual patient. This thesis primarily focuses on mortality, length of stay forecasting, and AF classification in critical care. We cover multiple outcomes and modelling methods whilst using multiple cohorts throughout the research. However, the analysis conducted throughout the thesis aims to create interpretable models for each modelling objective. In Chapter 3, we investigate three publicly available critical care databases containing multiple modalities of data and a wide range of parameters. We describe the processes and contemplations which must be considered to create actionable data for analysis in the ICU. Furthermore, we compared the three data sources using traditional statistical and ML methods and compared predictive performance. Based on 24 hours of sequential data, we achieved AUC performances of 79.5% for ICU mortality prediction and a prediction error of approximately 1.3 hours for ICU LOS. In Chapter 4, we investigate a sepsis cohort and conduct three sub-studies. Firstly, we investigated sepsis subtypes and compared biomarkers using traditional modelling methods. Next, we compare our approach to commonly and routinely used scoring systems in the ICU, such as APACHE IV and SOFA. Our tailored approach achieved superior performance with pulmonary and abdominal sepsis (AUC 0.74 and 0.71respectivly), displaying distinct individualities amongst the different sepsis groups. Next, we further expand our analysis by comparing ML methods and inference approaches to our baseline model and ICU acuity scores. We further investigate extending analysis to other outcomes of interest (In-hospital/ICU mortality, In-hospital/ICU LOS) to gain a more holistic view of the sepsis derivatives. This research shows that nonlinear models such as RF and GBM commonly outperformICU scoring, methods such as APACHE IV and SOFA and linear methods such as logistic/linear regression. Lastly, we extend our analysis in a multi-task learning framework for model optimisation and improved predictive performance. Our results showed superior performance with pulmonary, abdominal and renal/UTI sepsis (AUC 0.76, 0.77 and 0.73, respectively). Lastly, Chapter 5 investigates the classification of atrial fibrillation (AF) in long-lead ECG waveforms in sepsis patients. We developed a deep neural network to classify AF ECGs from Non-AF ECG cases in conjunction with refining a method to gain insight from the neural network model. We achieved a predictive performance of 0.99 and 0.89 regarding the test and external validation data. The inference from the model was achieved through the use of saliency maps, dimensionality reduction methods and clustering, utilising the automatic features learned by the developed model. We developed visualisations to help support the inference behind the classification of each ECG prediction. Overall, the research displays a wide range of novelties and unique approaches to solving various outcomes of interest in the ICU. In addition, this research demonstrates the implication of ML applicability in the ICU environment by providing insight and inference to diverse tasks regardless of the level of complexity. With further development, the frameworks and approaches outlined in this thesis have the potential to be used in clinical practice as decision-support tools in the ICU, allowing the automated alert and detection of patient classification, amongst others. The results generated in this thesis resulted in journal publications and medical understanding gained from insight available in the developed ML frameworks

Risk assessment for progression of Diabetic Nephropathy based on patient history analysis

A nefropatia diabética (ND) é uma das complicações mais comuns em doentes com diabetes. Trata-se de uma doença crónica que afeta progressivamente os rins, podendo resultar numa insuficiência renal. A digitalização permitiu aos hospitais armazenar as informações dos doentes em registos de saúde eletrónicos (RSE). A aplicação de algoritmos de Machine Learning (ML) a estes dados pode permitir a previsão do risco na evolução destes doentes, conduzindo a uma melhor gestão da doença. O principal objetivo deste trabalho é criar um modelo preditivo que tire partido do historial do doente presente nos RSE. Foi aplicado neste trabalho o maior conjunto de dados de doentes portugueses com DN, seguidos durante 22 anos pela Associação Protetora dos Diabéticos de Portugal (APDP). Foi desenvolvida uma abordagem longitudinal na fase de pré-processamento de dados, permitindo que estes fossem servidos como entrada para dezasseis algoritmos de ML distintos. Após a avaliação e análise dos respetivos resultados, o Light Gradient Boosting Machine foi identificado como o melhor modelo, apresentando boas capacidades de previsão. Esta conclusão foi apoiada não só pela avaliação de várias métricas de classificação em dados de treino, teste e validação, mas também pela avaliação do seu desempenho por cada estádio da doença. Para além disso, os modelos foram analisados utilizando gráficos de feature ranking e através de análise estatística. Como complemento, são ainda apresentados a interpretabilidade dos resultados através do método SHAP, assim como a distribuição do modelo utilizando o Gradio e os servidores da Hugging Face. Através da integração de técnicas ML, de um método de interpretação e de uma aplicação Web que fornece acesso ao modelo, este estudo oferece uma abordagem potencialmente eficaz para antecipar a evolução da ND, permitindo que os profissionais de saúde tomem decisões informadas para a prestação de cuidados personalizados e gestão da doença