Predicting post-stroke pneumonia using deep neural network approaches

Ge, Yanqiu; Wang, Qinghua; Wang, Li; Wu, Honghu; Chen, Peng; Wang, Jiajing; Xu, Yuan; Xiong, Gang; Zhang, Yaoyun; Yi, Yingping

doi:10.1016/j.ijmedinf.2019.103986

Cited by 72 publications

(39 citation statements)

References 26 publications

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“…The first main reason was that the OCSP subtype was based on these neurological deficits. The second one was that the newly developed machine learning methods (such as deep learning) could process medical data better [27,28], even the features collected in 1990's were not very mature. In the study the dataset was firstly analyzed by Shapiro-Wilk algorithm and Pearson Correlation.…”

Section: Discussionmentioning

confidence: 99%

Automated Ischemic Stroke Subtyping Based on Machine Learning Approach

Fang

Liu

2020

IEEE Access

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Ischemic stroke subtyping was not only highly valuable for effective intervention and treatment, but also important to the prognosis of ischemic stroke. The manual adjudication of disease classification was time-consuming, error-prone, and limits scaling to large datasets. In this study, an integrated machine learning approach was used to classify the subtype of ischemic stroke on The International Stroke Trial (IST) dataset. We considered the common problems of feature selection and prediction in medical datasets. Firstly, the importances of features were ranked by the Shapiro-Wilk algorithm and Pearson correlations between features were analyzed. Then, we used Recursive Feature Elimination with Cross-Validation (RFECV), which incorporated linear SVC, Random-Forest-Classifier, Extra-Trees-Classifier, AdaBoost-Classifier, and Multinomial-Naïve-Bayes-Classifier as estimator respectively, to select robust features important to ischemic stroke subtyping. Furthermore, the importances of selected features were determined by Extra-Trees-Classifier. Finally, the selected features were used by Extra-Trees-Classifier and a simple deep learning model to classify the ischemic stroke subtype on IST dataset. It was suggested that the described method could classify ischemic stroke subtype accurately. And the result showed that the machine learning approaches outperformed human professionals.

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Section: Discussionmentioning

confidence: 99%

Automated Ischemic Stroke Subtyping Based on Machine Learning Approach

Fang

Liu

2020

IEEE Access

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“…É perceptível na Figura 1 que os trabalhos com foco no diagóstico preventivo de pneumonia utilizaram apenas tarefas de classificação. Conforme comentado pelos autores, a identificação da patologia no início do tratamento é um fator essencial para um bom prognóstico e por isso a tarefa de classificação se destaca entre as demais [DeLisle et al 2013] [Ge et al 2019]. Para trabalhos focados nas categorias de Mortalidade e Complicações e Tempo de Internação e Readmissão, existe uma divisão dos resultados entre Tarefas de Classificação, em que os autores focam apenas na sinalização desses riscos à equipe médica [Wu et al 2014] [Shimizu et al 2019] [Lai et al 2018] e Regressão, para estimar a probabilidade de tais riscos ocorrerem [Caruana et al 2015] [Villiers et al 2018].…”

Section: Quais Tarefas De MD Foram Identificadas?unclassified

“…Considerando que a análise da curva ROC é baseada na Sensitividade e Especificidade, representando a relação bidimensional entre os casos verdadeiros positivos e falso positivos , justifica-se sua utilização pela maioria dos modelos considerando que erros na previsão desses casos causam maior impacto os pacientes, representando doentes, corretamente classificados como doentes [Caruana et al 2015] [Naydenova et al 2015] [Lai et al 2018] [Ge et al 2019]. Acurácia geral e Score Kappa obtiveram a menor representatividade nos trabalhos, resultado justificável visto que ambas as medidas focam na performance genérica dos modelos, enquanto os modelos utilizados na área médica se concentram em errar o menos possível na classe de interesse.…”

Section: Quais As Métricas Mais Utilizadas Para Avaliação Dos Modelos?unclassified

Machine Learning to Assist in Pneumonia Decision Making: A Systematic Review of the Literature

Silva¹,

Novo²,

Souza³

et al. 2020

Anais Do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2020)

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Clinical decision support systems is a research area in which Machine Learning (ML) techniques can be applied. Nevertheless, specifically in assisting pneumonia decision making, the use of ML has not been so expressive. To help matters, this work aims to contribute to the evolution of the intersection of such areas by presenting a Systematic Review of the Literature. It provides results which may help to identify, interpret and evaluate how ML techniques have been applied and some research enhancements yet to be done.

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“…Learning from a set of data (training data), machine learning algorithms apply a predictive model to unseen data (test data) [12]. There has been a plethora of applications of machine learning in healthcare, such as predicting diseases, health events and drug response, survival prediction, clustering of patients based on risk classification, analyzing genetics data and medical imaging [13][14][15][16][17]. In addition, a few studies have utilized machine learning for predicting cancer survival from hospital records and registries [18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Machine learning to predict 5-year survival among pediatric Acute Myeloid Leukemia patients and development of OSPAM-C online survival prediction tool

Das

Mishra

Mishra³

et al. 2020

Preprint

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Background: Acute myeloid leukemia (AML) accounts for a fifth of childhood leukemia. Although survival rates for AML have greatly improved over the past few decades, they vary depending on demographic and AML type factors. Objectives: To predict the five-year survival among pediatric AML patients using machine learning algorithms and deploy the best performing algorithm as an online survival prediction tool. Materials and methods: Pediatric patients (0 to 14 years) with a microscopically confirmed AML were extracted from the Surveillance Epidemiology and End Results (SEER) database (2000-2016) and split into training and test datasets (80/20 ratio). Four machine learning algorithms (logistic regression, support vector machine, gradient boosting and K nearest neighbor) and a deep neural network algorithm were trained on features to predict five-year survival. Performances of the algorithms were compared and the best performing algorithm was deployed as an online prediction tool. Results: A total of 2,140 patients met our inclusion criteria. The gradient boosting algorithm was the best performer in terms of discrimination and predictive ability. It was deployed as the online survival prediction tool named OSPAM-C (https://ashis-das.shinyapps.io/ospam/). Conclusions: Our study provides a framework for the development and deployment of an online survival prediction tool for pediatric patients with AML. While external validation is needed, our survival prediction tool presents an opportunity to inform clinical decision-making for AML patients.

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Predicting post-stroke pneumonia using deep neural network approaches

Cited by 72 publications

References 26 publications

Automated Ischemic Stroke Subtyping Based on Machine Learning Approach

Automated Ischemic Stroke Subtyping Based on Machine Learning Approach

Machine Learning to Assist in Pneumonia Decision Making: A Systematic Review of the Literature

Machine learning to predict 5-year survival among pediatric Acute Myeloid Leukemia patients and development of OSPAM-C online survival prediction tool

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