1,893 research outputs found
Using data-driven rules to predict mortality in severe community acquired pneumonia
Prediction of patient-centered outcomes in hospitals is useful for performance benchmarking, resource allocation, and guidance regarding active treatment and withdrawal of care. Yet, their use by clinicians is limited by the complexity of available tools and amount of data required. We propose to use Disjunctive Normal Forms as a novel approach to predict hospital and 90-day mortality from instance-based patient data, comprising demographic, genetic, and physiologic information in a large cohort of patients admitted with severe community acquired pneumonia. We develop two algorithms to efficiently learn Disjunctive Normal Forms, which yield easy-to-interpret rules that explicitly map data to the outcome of interest. Disjunctive Normal Forms achieve higher prediction performance quality compared to a set of state-of-the-art machine learning models, and unveils insights unavailable with standard methods. Disjunctive Normal Forms constitute an intuitive set of prediction rules that could be easily implemented to predict outcomes and guide criteria-based clinical decision making and clinical trial execution, and thus of greater practical usefulness than currently available prediction tools. The Java implementation of the tool JavaDNF will be publicly available. © 2014 Wu et al
The outperformance of the semantic learning machine, against commonly used algorithms, for binary and multi-class medical image classification: combined with the usage of feature extraction by several convolutional neural networks
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsExtensive recent research has shown the importance of innovation in medical healthcare,
with a focus on Pneumonia. It is vital and lifesaving to predict Pneumonia cases as fast
as possible and preferably in advance of the symptoms. An online database source
managed to gather Pneumonia-specific image data, with not just the presence of the
infection, but also the nature of it, divided in bacterial- and viral infection. The first
achievement is extracting valuable information from the X-Ray image datasets. Using
several ImageNet pre-trained CNNs, knowledge can be gained from images and
transferred to numeric arrays.
This, both binary and multi-class classification data, requires a sophisticated prediction
algorithm that recognizes X-Ray image patterns. Multiple, recently performed
experiments show promising results about the innovative Semantic Learning Machine
(SLM) that is essentially a geometric semantic hill climber for feedforward Neural
Networks. This SLM is based on a derivation of the Geometric Semantic Genetic
Programming (GSGP) mutation operator for real-value semantics.
To prove the outperformance of the binary and multi-class SLM in general, a selection of
commonly used algorithms is necessary in this research. A comprehensive
hyperparameter optimization is performed for commonly used algorithms for those kinds
of real-life problems, such as: Random Forest, Support Vector Machine, KNearestNeighbors
and Neural Networks.
The results of the SLM are promising for the Pneumonia application but could be used
for all types of predictions based on images in combination with the CNN feature
extractions.Uma extensa pesquisa recente mostrou a importância da inovação na assistência médica,
com foco na pneumonia. É vital e salva-vidas prever os casos de pneumonia o mais rápido
possível e, de preferência, antes dos sintomas. Uma fonte on-line conseguiu coletar dados
de imagem específicos da pneumonia, identificando não apenas a presença da infecção,
mas também seu tipo, bacteriana ou viral. A primeira conquista é extrair informações
valiosas dos conjuntos de dados de imagem de raios-X. Usando várias CNNs pré-treinadas
da ImageNet, é possível obter conhecimento das imagens e transferi-las para matrizes
numéricas.
Esses dados de classificação binários e multi-classe requerem um sofisticado algoritmo de
predição que reconhece os padrões de imagem de raios-X. Vários experimentos realizados
recentemente mostram resultados promissores sobre a inovadora Semantic Learning
Machine (SLM), que é essencialmente um hill climber semântico geométrico para
feedforward neural network. Esse SLM é baseado em uma derivação do operador de
mutação da Geometric Semantic Genetic Programming (GSGP) para valor-reais
semânticos.
Para provar o desempenho superior do SLM binário e multi-classe em geral, é necessária
uma seleção de algoritmos mais comuns na pesquisa. Uma otimização abrangente dos
hiperparâmetros é realizada para algoritmos comumente utilizados para esses tipos de
problemas na vida real, como Random Forest, Support Vector Machine,K-Nearest
Neighbors and Neural Networks.
Os resultados do SLM são promissores para o aplicativo pneumonia, mas podem ser usados
para todos os tipos de previsões baseadas em imagens em combinação com as extrações de
recursos da CNN
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
The Use of Neural Networks in the Cloud Computing Environment
In this research, we will address the relationship between cloud computing using neural networks, which in turn is associated with online services on the basis of cloud computing, such as the infrastructure of the system and access to Internet networks and the use of the communication network in the show cloud computing, but cloud computing for various types of services and applications which used to use the internet using neural networks. Here we will address the resource scheduling strategy, a technology key in cloud computing, which is a service that can be used to send Functions and tasks available resources such as Software and storage systems, as well as the aim is to Enlargement the utilization of The classification of the resources available and assembled together to To reach the top productivity in solving computational problems through neural networks. Introduction we are talking in this search for cloud computing, which is a glomus group of connected computers together each other representing the cloud from a variety or complex networks and cloud computing is going one direction between Most of the systems in the network with the help of some of the online networks . he is a basis of a new model of my account because it is the next-generation technologies and is built on the high speed of the computer with the way it works on the storage and analysis of data and services offered through distributed and Technology computing working on the pooling of resources and Cloud computing is also considered a type of distributed computing
A comparative analysis of chronic obstructive pulmonary disease using machine learning, and deep learning
Chronic obstructive pulmonary disease (COPD) is a general clinical issue in numerous countries considered the fifth reason for inability and the third reason for mortality on a global scale within 2021. From recent reviews, a deep convolutional neural network (CNN) is used in the primary analysis of the deadly COPD, which uses the computed tomography (CT) images procured from the deep learning tools. Detection and analysis of COPD using several image processing techniques, deep learning models, and machine learning models are notable contributions to this review. This research aims to cover the detailed findings on pulmonary diseases or lung diseases, their causes, and symptoms, which will help treat infections with high performance and a swift response. The articles selected have more than 80% accuracy and are tabulated and analyzed for sensitivity, specificity, and area under the curve (AUC) using different methodologies. This research focuses on the various tools and techniques used in COPD analysis and eventually provides an overview of COPD with coronavirus disease 2019 (COVID-19) symptoms.
Exploring the potential of artificial intelligence and machine learning to combat COVID-19 and existing opportunities for LMIC: A scoping review
Background: In the face of the current time-sensitive COVID-19 pandemic, the limited capacity of healthcare systems resulted in an emerging need to develop newer methods to control the spread of the pandemic. Artificial Intelligence (AI), and Machine Learning (ML) have a vast potential to exponentially optimize health care research. The use of AI-driven tools in LMIC can help in eradicating health inequalities and decrease the burden on health systems.Methods: The literature search for this Scoping review was conducted through the PubMed database using keywords: COVID-19, Artificial Intelligence (AI), Machine Learning (ML), and Low Middle-Income Countries (LMIC). Forty-three articles were identified and screened for eligibility and 13 were included in the final review. All the items of this Scoping review are reported using guidelines for PRISMA extension for scoping reviews (PRISMA-ScR).Results: Results were synthesized and reported under 4 themes. (a) The need of AI during this pandemic: AI can assist to increase the speed and accuracy of identification of cases and through data mining to deal with the health crisis efficiently, (b) Utility of AI in COVID-19 screening, contact tracing, and diagnosis: Efficacy for virus detection can a be increased by deploying the smart city data network using terminal tracking system along-with prediction of future outbreaks, (c) Use of AI in COVID-19 patient monitoring and drug development: A Deep learning system provides valuable information regarding protein structures associated with COVID-19 which could be utilized for vaccine formulation, and (d) AI beyond COVID-19 and opportunities for Low-Middle Income Countries (LMIC): There is a lack of financial, material, and human resources in LMIC, AI can minimize the workload on human labor and help in analyzing vast medical data, potentiating predictive and preventive healthcare.Conclusion: AI-based tools can be a game-changer for diagnosis, treatment, and management of COVID-19 patients with the potential to reshape the future of healthcare in LMIC
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