scholarly journals Supervised Machine Learning for Risk Stratification of Influenza-Like Illness: A Model to Prioritize Emerging Infectious Disease Disaster Responses

2021 ◽  
Author(s):  
Han-Yi Robert Chiu ◽  
Chun-Kai Hwang ◽  
Shey-Ying Chen ◽  
Fuh-Yuan Shih ◽  
Hsieh-Cheng Han ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hospital Admissions ◽  
Emerging Infectious Diseases ◽  
Learning Technologies ◽  
Supervised Machine Learning ◽  
Severe Illness ◽  
Mass Casualties ◽  
Global Public Health ◽  
Neural Network Models ◽  
Influenza Like Illness

Abstract Emerging infectious diseases (EIDs), including the latest COVID-19 pandemic, have emerged and raised global public health crises in recent decades. Without existing protective immunity, an EID may spread rapidly and cause mass casualties in a very short time. Therefore, it is imperative to identify cases with risk of disease progression for the best allocation of medical resources in case medical facilities are overwhelmed with a flood of patients. This study aimed to exploit machine learning technologies to cope with this challenge. The study was based on 83,227 hospital admissions with influenza-like illness and we analysed the risk effects of 19 comorbidities along with age and gender for severe illness or mortality risk. The experimental results revealed that the conventional decision tree (DT) models built with only 6 features, including age, gender, and four comorbidities, delivered the same level of prediction accuracy as the state-of-the-art deep neural network models built with 18 features. Accordingly, we further studied how to exploit the DT models with different sensitivity levels to determine patient triage and optimize medical resource allocation in different stages of an EID disaster to aid the frontline clinicians and policy-makers. In conclusion, our study demonstrated an approach to exploit machine learning technologies to cope with the challenges during the outbreak of an EID.

scholarly journals Machine learning for emerging infectious disease field responses

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Han-Yi Robert Chiu ◽  
Chun-Kai Hwang ◽  
Shey-Ying Chen ◽  
Fuh-Yuan Shih ◽  
Hsieh-Cheng Han ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Hospital Admissions ◽  
Decision Rules ◽  
Emerging Infectious Diseases ◽  
Vaccination Strategy ◽  
Learning Technologies ◽  
Severe Illness ◽  
Mass Casualties ◽  
Global Public Health

AbstractEmerging infectious diseases (EIDs), including the latest COVID-19 pandemic, have emerged and raised global public health crises in recent decades. Without existing protective immunity, an EID may spread rapidly and cause mass casualties in a very short time. Therefore, it is imperative to identify cases with risk of disease progression for the optimized allocation of medical resources in case medical facilities are overwhelmed with a flood of patients. This study has aimed to cope with this challenge from the aspect of preventive medicine by exploiting machine learning technologies. The study has been based on 83,227 hospital admissions with influenza-like illness and we analysed the risk effects of 19 comorbidities along with age and gender for severe illness or mortality risk. The experimental results revealed that the decision rules derived from the machine learning based prediction models can provide valuable guidelines for the healthcare policy makers to develop an effective vaccination strategy. Furthermore, in case the healthcare facilities are overwhelmed by patients with EID, which frequently occurred in the recent COVID-19 pandemic, the frontline physicians can incorporate the proposed prediction models to triage patients suffering minor symptoms without laboratory tests, which may become scarce during an EID disaster. In conclusion, our study has demonstrated an effective approach to exploit machine learning technologies to cope with the challenges faced during the outbreak of an EID.

scholarly journals Comparison of Implicit vs. Explicit Regime Identification in Machine Learning Methods for Solar Irradiance Prediction

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 689 ◽  
Author(s):  
Tyler McCandless ◽  
Susan Dettling ◽  
Sue Ellen Haupt
Keyword(s):  
Machine Learning ◽  
Solar Power ◽  
Network Models ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Validation Dataset ◽  
Prediction Errors ◽  
Learning Approaches ◽  
Power Prediction ◽  
Neural Network Models

This work compares the solar power forecasting performance of tree-based methods that include implicit regime-based models to explicit regime separation methods that utilize both unsupervised and supervised machine learning techniques. Previous studies have shown an improvement utilizing a regime-based machine learning approach in a climate with diverse cloud conditions. This study compares the machine learning approaches for solar power prediction at the Shagaya Renewable Energy Park in Kuwait, which is in an arid desert climate characterized by abundant sunshine. The regime-dependent artificial neural network models undergo a comprehensive parameter and hyperparameter tuning analysis to minimize the prediction errors on a test dataset. The final results that compare the different methods are computed on an independent validation dataset. The results show that the tree-based methods, the regression model tree approach, performs better than the explicit regime-dependent approach. These results appear to be a function of the predominantly sunny conditions that limit the ability of an unsupervised technique to separate regimes for which the relationship between the predictors and the predictand would differ for the supervised learning technique.

scholarly journals Modern Ubasute: Public Discourse and Sentiment about Older Adults and COVID19 Using Machine Learning

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 952-953
Author(s):  
Xiaoling Xiang ◽  
Xuan Lu ◽  
Alex Halavanau ◽  
Jia Xue ◽  
Yihang Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Public Discourse ◽  
Latent Dirichlet Allocation ◽  
Supervised Machine Learning ◽  
Severe Illness ◽  
Causality Test ◽  
Perceptions Of Risk ◽  
Qualitative Thematic Analysis ◽  
Emotion Lexicon

Abstract This study examined public discourse and sentiment on social media regarding older adults in COVID-19. Twitter data (N=82,893) related to both older adults and COVID-19 and dated from January 23rd to May 20th, 2020, were analyzed. Classification of tweets involved supervised machine learning. Latent Dirichlet Allocation was used to identify dominant themes in public discourse using, accompanied by a qualitative thematic analysis. Sentiment analysis was conducted based on the NRC Emotion Lexicon. The most common category in the coded tweets was “personal opinions” (66.2%), followed by “informative” (24.7%), “jokes/ridicule” (4.8%), and “personal experiences” (4.3%). More than one in ten (11.5%) tweets implied that the life of older adults is less valuable or downplayed the pandemic because it mostly harms older adults. A small proportion (4.6%) explicitly supported the idea of just isolating older adults. Almost three-quarters (72.9%) within “jokes/ridicule” targeted older adults, half of which were “death jokes.” The daily average of ageist content was 18%, with the highest of 52.8% on March 11th, 2020. We extracted 14 themes, such as perceptions of lockdown and risk. A bivariate Granger causality test suggested that informative tweets regarding at-risk populations increased the prevalence of tweets that downplayed the pandemic. The COVID-19 pandemic has exposed and intensified ageism in our society. Information about COVID-19 on Twitter influenced public perceptions of risk and acceptable ways of controlling the pandemic. Public education on the risk of severe illness is needed to correct misperceptions.

scholarly journals A Neural Network Approach to Identify Glioblastoma Progression Phenotype from Multimodal MRI

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2006
Author(s):  
Jiun-Lin Yan ◽  
Cheng-Hong Toh ◽  
Li Ko ◽  
Kuo-Chen Wei ◽  
Pin-Yuan Chen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Tumor Progression ◽  
Diffusion Tensor ◽  
Supervised Machine Learning ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Clinical Prognosis ◽  
Multimodal Mri ◽  
Progression Patterns

The phenotypes of glioblastoma (GBM) progression after treatment are heterogeneous in both imaging and clinical prognosis. This study aims to apply radiomics and neural network analysis to preoperative multimodal MRI data to characterize tumor progression phenotypes. We retrospectively reviewed 41 patients with newly diagnosed cerebral GBM from 2009–2016 who comprised the machine learning training group, and prospectively included 18 patients from 2017–2018 for data validation. Preoperative MRI examinations included structural MRI, diffusion tensor imaging, and perfusion MRI. Tumor progression patterns were categorized as diffuse or localized. A supervised machine learning model and neural network-based models (VGG16 and ResNet50) were used to establish the prediction model of the pattern of progression. The diffuse progression pattern showed a significantly worse prognosis regarding overall survival (p = 0.032). A total of 153 of the 841 radiomic features were used to classify progression patterns using different machine learning models with an overall accuracy of 81% (range: 77.5–82.5%, AUC = 0.83–0.89). Further application of the pretrained ResNet50 and VGG 16 neural network models demonstrated an overall accuracy of 93.1 and 96.1%. The progression patterns of GBM are an important prognostic factor and can potentially be predicted by combining multimodal MR radiomics with machine learning.

scholarly journals Early Detection of Hypotension Using a Multivariate Machine Learning Approach

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 440-444
Author(s):  
Navid Rashedi ◽  
Yifei Sun ◽  
Vikrant Vaze ◽  
Parikshit Shah ◽  
Ryan Halter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Early Detection ◽  
Short Term Memory ◽  
Learning Technologies ◽  
Physiological Signals ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Trauma Patients ◽  
Arterial Blood

ABSTRACT Introduction The ability to accurately detect hypotension in trauma patients at the earliest possible time is important in improving trauma outcomes. The earlier an accurate detection can be made, the more time is available to take corrective action. Currently, there is limited research on combining multiple physiological signals for an early detection of hemorrhagic shock. We studied the viability of early detection of hypotension based on multiple physiologic signals and machine learning methods. We explored proof of concept with a small (5 minutes) prediction window for application of machine learning tools and multiple physiologic signals to detecting hypotension. Materials and Methods Multivariate physiological signals from a preexisting dataset generated by an experimental hemorrhage model were employed. These experiments were conducted previously by another research group and the data made available publicly through a web portal. This dataset is among the few publicly available which incorporate measurement of multiple physiological signals from large animals during experimental hemorrhage. The data included two hemorrhage studies involving eight sheep. Supervised machine learning experiments were conducted in order to develop deep learning (viz., long short-term memory or LSTM), ensemble learning (viz., random forest), and classical learning (viz., support vector machine or SVM) models for the identification of physiological signals that can detect whether or not overall blood loss exceeds a predefined threshold 5 minutes ahead of time. To evaluate the performance of the machine learning technologies, 3-fold cross-validation was conducted and precision (also called positive predictive value) and recall (also called sensitivity) values were compared. As a first step in this development process, 5 minutes prediction windows were utilized. Results The results showed that SVM and random forest outperform LSTM neural networks, likely because LSTM tends to overfit the data on small sized datasets. Random forest has the highest recall (84%) with 56% precision while SVM has 62% recall with 82% precision. Upon analyzing the feature importance, it was observed that electrocardiogram has the highest significance while arterial blood pressure has the least importance among all other signals. Conclusion In this research, we explored the viability of early detection of hypotension based on multiple signals in a preexisting animal hemorrhage dataset. The results show that a multivariate approach might be more effective than univariate approaches for this detection task.

scholarly journals Modern Senicide in the Face of a Pandemic: An Examination of Public Discourse and Sentiment About Older Adults and COVID-19 Using Machine Learning

2020 ◽  
Author(s):  
Xiaoling Xiang ◽  
Xuan Lu ◽  
Alex Halavanau ◽  
Jia Xue ◽  
Yihang Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Public Discourse ◽  
Data Science ◽  
Supervised Machine Learning ◽  
Severe Illness ◽  
Causality Test ◽  
Perceptions Of Risk ◽  
Qualitative Thematic Analysis ◽  
The Face

Abstract Objectives This study examined public discourse and sentiment regarding older adults and COVID-19 on social media and assessed the extent of ageism in public discourse. Methods Twitter data (N = 82,893) related to both older adults and COVID-19 and dated from January 23 to May 20, 2020, were analyzed. We used a combination of data science methods (including supervised machine learning, topic modeling, and sentiment analysis), qualitative thematic analysis, and conventional statistics. Results The most common category in the coded tweets was “personal opinions” (66.2%), followed by “informative” (24.7%), “jokes/ridicule” (4.8%), and “personal experiences” (4.3%). The daily average of ageist content was 18%, with the highest of 52.8% on March 11, 2020. Specifically, more than 1 in 10 (11.5%) tweets implied that the life of older adults is less valuable or downplayed the pandemic because it mostly harms older adults. A small proportion (4.6%) explicitly supported the idea of just isolating older adults. Almost three-quarters (72.9%) within “jokes/ridicule” targeted older adults, half of which were “death jokes.” Also, 14 themes were extracted, such as perceptions of lockdown and risk. A bivariate Granger causality test suggested that informative tweets regarding at-risk populations increased the prevalence of tweets that downplayed the pandemic. Discussion Ageist content in the context of COVID-19 was prevalent on Twitter. Information about COVID-19 on Twitter influenced public perceptions of risk and acceptable ways of controlling the pandemic. Public education on the risk of severe illness is needed to correct misperceptions.

scholarly journals Estimating Spatiotemporal Information from Behavioral Sensing Data of Wheelchair Users by Machine Learning Technologies

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 114 ◽  
Author(s):  
Ikuko Yairi ◽  
Hiroki Takahashi ◽  
Takumi Watanabe ◽  
Kouya Nagamine ◽  
Yusuke Fukushima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Surface Condition ◽  
Learning Technologies ◽  
Road Surface ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Prototype System ◽  
Wheelchair Users ◽  
The Road

Recent expansion of intelligent gadgets, such as smartphones and smart watches, familiarizes humans with sensing their activities. We have been developing a road accessibility evaluation system inspired by human sensing technologies. This paper introduces our methodology to estimate road accessibility from the three-axis acceleration data obtained by a smart phone attached on a wheelchair seat, such as environmental factors, e.g., curbs and gaps, which directly influence wheelchair bodies, and human factors, e.g., wheelchair users’ feelings of tiredness and strain. Our goal is to realize a system that provides the road accessibility visualization services to users by online/offline pattern matching using impersonal models, while gradually learning to improve service accuracy using new data provided by users. As the first step, this paper evaluates features acquired by the DCNN (deep convolutional neural network), which learns the state of the road surface from the data in supervised machine learning techniques. The evaluated results show that the features can capture the difference of the road surface condition in more detail than the label attached by us and are effective as the means for quantitatively expressing the road surface condition. This paper developed and evaluated a prototype system that estimated types of ground surfaces focusing on knowledge extraction and visualization.

Effect of directional augmentation using supervised machine learning technologies: A case study of strawberry powdery mildew detection

2020 ◽  
Vol 194 ◽  
pp. 49-60 ◽  
Author(s):  
Jaemyung Shin ◽  
Young K. Chang ◽  
Brandon Heung ◽  
Tri Nguyen-Quang ◽  
Gordon W. Price ◽  
...  
Keyword(s):  
Machine Learning ◽  
Powdery Mildew ◽  
Learning Technologies ◽  
Supervised Machine Learning

Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

2020 ◽  
Vol 14 (2) ◽  
pp. 140-159
Author(s):  
Anthony-Paul Cooper ◽  
Emmanuel Awuni Kolog ◽  
Erkki Sutinen
Keyword(s):  
Machine Learning ◽  
Online Community ◽  
High Volume ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Social Media Data ◽  
Twitter Data ◽  
Resource Intensity ◽  
Media Data ◽  
Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

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