Using machine‐learning methods to support health‐care professionals in making admission decisions

Luo, Li; Li, Jialing; Shen, Wenwu

doi:10.1002/hpm.2769

Cited by 19 publications

(13 citation statements)

References 32 publications

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“…To exclude any statistical bias, the learning‐curve approach was used to access the data dependence of machine‐learning modeling. The learning‐curve method was adopted to demonstrate the relationship between RMSE and training sample size, which was an extremely useful tool for diagnosing machine‐learning model performance …”

Section: Methodsmentioning

confidence: 99%

A Machine Learning–Based Design Rule for Improved Open‐Circuit Voltage in Ternary Organic Solar Cells

Lee

2019

Advanced Intelligent Systems

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Organic solar cells (OSCs) based on ternary blends are among the most promising photovoltaic technologies. To further improve the power conversion efficiency (PCE), the materials selection criteria must be focused on achieving high open-circuit voltage (V oc ) through the alignment of the energy levels of the ternary blends. Hence, machine-learning approaches are in high demand for extracting the complex correlation between V oc and the energy levels of the ternary blends, which are crucial to facilitate device design. Herein, the datadriven strategies are used to generate a model based on the available experimental data, and the V oc is then predicted using available machine-learning methods (the Random Forest regression and the Support Vector regression). In addition, the Random Forest regression is developed to find the appropriate energy-level alignment of ternary OSCs and to reveal the relationship between V oc and electronic features. Finally, an analysis based on the ranking of variables in terms of importance by the Random Forest model is performed to identify the key feature governing the V oc and the performance of ternary OSCs. From the perspective of device design, the machine-learning approach provides sufficient insights to improve the V oc and advances the comprehensive understanding of ternary OSCs.

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Section: Methodsmentioning

confidence: 99%

A Machine Learning–Based Design Rule for Improved Open‐Circuit Voltage in Ternary Organic Solar Cells

Lee

2019

Advanced Intelligent Systems

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“…AI is widely used in the medicine domain [26] . Despite the ethical concern regarding the application of AI with patients’ data in the current pandemic scenario, these methods should be used to support medical staff [27] , [28] , [29] . The stress factors that affect medical professionals during this pandemic scenario concerning the increase of patients in the hospitals are significantly affect their work and performance [30] .…”

Section: Introductionmentioning

confidence: 99%

Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network

Marques

Agarwal

Díez

2020

Applied Soft Computing

262

151

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COVID-19 infection was reported in December 2019 at Wuhan, China. This virus critically affects several countries such as the USA, Brazil, India and Italy. Numerous research units are working at their higher level of effort to develop novel methods to prevent and control this pandemic scenario. The main objective of this paper is to propose a medical decision support system using the implementation of a convolutional neural network (CNN). This CNN has been developed using EfficientNet architecture. To the best of the authors’ knowledge, there is no similar study that proposes an automated method for COVID-19 diagnosis using EfficientNet. Therefore, the main contribution is to present the results of a CNN developed using EfficientNet and 10-fold stratified cross-validation. This paper presents two main experiments. First, the binary classification results using images from COVID-19 patients and normal patients are shown. Second, the multi-class results using images from COVID-19, pneumonia and normal patients are discussed. The results show average accuracy values for binary and multi-class of 99.62% and 96.70%, respectively. On the one hand, the proposed CNN model using EfficientNet presents an average recall value of 99.63% and 96.69% concerning binary and multi-class, respectively. On the other hand, 99.64% is the average precision value reported by binary classification, and 97.54% is presented in multi-class. Finally, the average F1-score for multi-class is 97.11%, and 99.62% is presented for binary classification. In conclusion, the proposed architecture can provide an automated medical diagnostics system to support healthcare specialists for enhanced decision making during this pandemic scenario.

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“…Here, newer machine learning techniques-we will refer to them as modern machine learning techniques in this workincluding artificial neural nets (ANN), especially deep learning (DL), and ensemble models such as tree boosting have often shown higher performance than traditional machine learning techniques such as linear or logistic regression, e.g. [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome

et al. 2020

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State-of-the-art machine learning (ML) artificial intelligence methods are increasingly leveraged in clinical predictive modeling to provide clinical decision support systems to physicians. Modern ML approaches such as artificial neural networks (ANNs) and tree boosting often perform better than more traditional methods like logistic regression. On the other hand, these modern methods yield a limited understanding of the resulting predictions. However, in the medical domain, understanding of applied models is essential, in particular, when informing clinical decision support. Thus, in recent years, interpretability methods for modern ML methods have emerged to potentially allow explainable predictions paired with high performance. To our knowledge, we present in this work the first explainability comparison of two modern ML methods, tree boosting and multilayer perceptrons (MLPs), to traditional logistic regression methods using a stroke outcome prediction paradigm. Here, we used clinical features to predict a dichotomized 90 days post-stroke modified Rankin Scale (mRS) score. For interpretability, we evaluated clinical features' importance with regard to predictions using deep Taylor decomposition for MLP, Shapley values for tree boosting and model coefficients for logistic regression. With regard to performance as measured by Area under the Curve (AUC) values on the test dataset, all models performed comparably: Logistic regression AUCs were 0.83, 0.83, 0.81 for three different regularization schemes; tree boosting AUC was 0.81; MLP AUC was 0.83. Importantly, the interpretability analysis demonstrated consistent results across models by rating age and stroke severity consecutively amongst the most important predictive features. For less important features, some differences were observed between the methods. Our analysis suggests that modern machine learning methods can provide explainability which is compatible with domain knowledge interpretation and traditional method rankings. Future work should focus on replication of these findings in other datasets and further testing of different explainability methods.

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Using machine‐learning methods to support health‐care professionals in making admission decisions

Cited by 19 publications

References 32 publications

A Machine Learning–Based Design Rule for Improved Open‐Circuit Voltage in Ternary Organic Solar Cells

A Machine Learning–Based Design Rule for Improved Open‐Circuit Voltage in Ternary Organic Solar Cells

Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network

Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome

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