scholarly journals Identification of high-risk COVID-19 patients using machine learning

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257234
Author(s):  
Mario A. Quiroz-Juárez ◽  
Armando Torres-Gómez ◽  
Irma Hoyo-Ulloa ◽  
Roberto de J. León-Montiel ◽  
Alfred B. U’Ren
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Capacity Planning ◽  
Learning Algorithm ◽  
Human Life ◽  
Demographic Data ◽  
Health Crisis ◽  
Related Information ◽  
Optimal Estimators ◽  
Hospital Capacity Planning

The current COVID-19 public health crisis, caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), has produced a devastating toll both in terms of human life loss and economic disruption. In this paper we present a machine-learning algorithm capable of identifying whether a given patient (actually infected or suspected to be infected) is more likely to survive than to die, or vice-versa. We train this algorithm with historical data, including medical history, demographic data, as well as COVID-19-related information. This is extracted from a database of confirmed and suspected COVID-19 infections in Mexico, constituting the official COVID-19 data compiled and made publicly available by the Mexican Federal Government. We demonstrate that the proposed method can detect high-risk patients with high accuracy, in each of four identified clinical stages, thus improving hospital capacity planning and timely treatment. Furthermore, we show that our method can be extended to provide optimal estimators for hypothesis-testing techniques commonly-used in biological and medical statistics. We believe that our work could be of use in the context of the current pandemic in assisting medical professionals with real-time assessments so as to determine health care priorities.

scholarly journals Identification of high-risk COVID-19 patients using machine learning

2021 ◽  
Author(s):  
Mario A. Quiroz-Juárez ◽  
Armando Torres-Gómez ◽  
Irma Hoyo-Ulloa ◽  
Roberto de J. León-Montiel ◽  
Alfred B. U’Ren
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Capacity Planning ◽  
Learning Algorithm ◽  
Human Life ◽  
Demographic Data ◽  
Health Crisis ◽  
Related Information ◽  
Optimal Estimators ◽  
Hospital Capacity Planning

The current COVID-19 public health crisis, caused by SARSCoV-2 (severe acute respiratory syndrome coronavirus 2), has produced a devastating toll both in terms of human life loss and economic disruption. In this paper we present a machine-learning algorithm capable of identifying whether a given patient (actually infected or suspected to be infected) is more likely to survive than to die, or vice-versa. We train this algorithm with historical data, including medical history, demographic data, as well as COVID-19-related information. This is extracted from a database of confirmed and suspected COVID-19 infections in Mexico, constituting the official COVID-19 data compiled and made publicly available by the Mexican Federal Government. We demonstrate that the proposed method can detect high-risk patients with high accuracy, in each of four identified treatment stages, thus improving hospital capacity planning and timely treatment. Furthermore, we show that our method can be extended to provide optimal estimators for hypothesis-testing techniques commonly-used in biological and medical statistics. We believe that our work could be of use in the context of the current pandemic in assisting medical professionals with real-time assessments so as to determine health care priorities.

Personalized stratification of back to work risk amidst COVID-19: A machine learning approach (Preprint)

2020 ◽  
Author(s):  
Carson Lam ◽  
Jacob Calvert ◽  
Gina Barnes ◽  
Emily Pellegrini ◽  
Anna Lynn-Palevsky ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Learning Algorithm ◽  
Severe Disease ◽  
High Specificity ◽  
Population Level ◽  
The United States ◽  
Health Condition ◽  
Available P ◽  
Severe Illness

BACKGROUND In the wake of COVID-19, the United States has developed a three stage plan to outline the parameters to determine when states may reopen businesses and ease travel restrictions. The guidelines also identify subpopulations of Americans that should continue to stay at home due to being at high risk for severe disease should they contract COVID-19. These guidelines were based on population level demographics, rather than individual-level risk factors. As such, they may misidentify individuals at high risk for severe illness and who should therefore not return to work until vaccination or widespread serological testing is available. OBJECTIVE This study evaluated a machine learning algorithm for the prediction of serious illness due to COVID-19 using inpatient data collected from electronic health records. METHODS The algorithm was trained to identify patients for whom a diagnosis of COVID-19 was likely to result in hospitalization, and compared against four U.S policy-based criteria: age over 65, having a serious underlying health condition, age over 65 or having a serious underlying health condition, and age over 65 and having a serious underlying health condition. RESULTS This algorithm identified 80% of patients at risk for hospitalization due to COVID-19, versus at most 62% that are identified by government guidelines. The algorithm also achieved a high specificity of 95%, outperforming government guidelines. CONCLUSIONS This algorithm may help to enable a broad reopening of the American economy while ensuring that patients at high risk for serious disease remain home until vaccination and testing become available.

scholarly journals Predicting Venous Thrombosis in Osteoarthritis Using a Machine Learning Algorithm: A Population-Based Cohort Study

2022 ◽  
Vol 12 (1) ◽  
pp. 114
Author(s):  
Chao Lu ◽  
Jiayin Song ◽  
Hui Li ◽  
Wenxing Yu ◽  
Yangquan Hao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cohort Study ◽  
High Risk ◽  
Venous Thrombosis ◽  
Learning Algorithm ◽  
Population Based ◽  
Joint Disease ◽  
Recursive Feature Elimination ◽  
Machine Learning Algorithm ◽  
Interpretable Machine Learning

Osteoarthritis (OA) is the most common joint disease associated with pain and disability. OA patients are at a high risk for venous thrombosis (VTE). Here, we developed an interpretable machine learning (ML)-based model to predict VTE risk in patients with OA. To establish a prediction model, we used six ML algorithms, of which 35 variables were employed. Recursive feature elimination (RFE) was used to screen the most related clinical variables associated with VTE. SHapley additive exPlanations (SHAP) were applied to interpret the ML mode and determine the importance of the selected features. Overall, 3169 patients with OA (average age: 66.52 ± 7.28 years) were recruited from Xi’an Honghui Hospital. Of these, 352 and 2817 patients were diagnosed with and without VTE, respectively. The XGBoost algorithm showed the best performance. According to the RFE algorithms, 15 variables were retained for further modeling with the XGBoost algorithm. The top three predictors were Kellgren–Lawrence grade, age, and hypertension. Our study showed that the XGBoost model with 15 variables has a high potential to predict VTE risk in patients with OA.

scholarly journals Machine Learning Algorithm Using Clinical Data and Demographic Data for Preterm Birth Prediction

2022 ◽  
Vol 226 (1) ◽  
pp. S362-S363
Author(s):  
Matthew Hoffman ◽  
Wei Liu ◽  
Jade Tunguhan ◽  
Ghamar Bitar ◽  
Kaveeta Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Preterm Birth ◽  
Clinical Data ◽  
Learning Algorithm ◽  
Demographic Data ◽  
Machine Learning Algorithm

scholarly journals Machine Learning for Landslides Prevention: A Survey

2020 ◽  
Author(s):  
Zhengjing Ma ◽  
Gang Mei
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Human Life ◽  
Machine Learning Algorithms ◽  
Warning Systems ◽  
Negative Effects ◽  
Relevant Research ◽  
Related Information ◽  
Urgent Task ◽  
Comprehensive Survey

Landslides are one of the most critical categories of natural disasters worldwide and induce severely destructive outcomes to human life and the overall economic system. To reduce its negative effects, landslides prevention has become an urgent task, which includes investigating landslide-related information and predicting potential landslides. Machine learning is a state-of-the-art analytics tool that has been widely used in landslides prevention. This paper presents a comprehensive survey of relevant research on machine learning applied in landslides prevention, mainly focusing on (1) landslides detection based on images, (2) landslides susceptibility assessment, and (3) the development of landslide warning systems. Moreover, this paper discusses the current challenges and potential opportunities in the application of machine learning algorithms for landslides prevention.

scholarly journals Intrusion Detection in IoT Networks Using Deep Learning Algorithm

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 279 ◽  
Author(s):  
Bambang Susilo ◽  
Riri Fitri Sari
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Strategies ◽  
Learning Algorithm ◽  
Human Life ◽  
Denial Of Service ◽  
The Internet ◽  
Dos Attacks ◽  
Deep Learning Algorithm ◽  
Iot Devices

The internet has become an inseparable part of human life, and the number of devices connected to the internet is increasing sharply. In particular, Internet of Things (IoT) devices have become a part of everyday human life. However, some challenges are increasing, and their solutions are not well defined. More and more challenges related to technology security concerning the IoT are arising. Many methods have been developed to secure IoT networks, but many more can still be developed. One proposed way to improve IoT security is to use machine learning. This research discusses several machine-learning and deep-learning strategies, as well as standard datasets for improving the security performance of the IoT. We developed an algorithm for detecting denial-of-service (DoS) attacks using a deep-learning algorithm. This research used the Python programming language with packages such as scikit-learn, Tensorflow, and Seaborn. We found that a deep-learning model could increase accuracy so that the mitigation of attacks that occur on an IoT network is as effective as possible.

scholarly journals Intelligent SMOTE Based Machine learning Classification for Fetal State on Cardiotocography Dataset

2021 ◽  
Author(s):  
Pijush Dutta ◽  
Shobhandeb Paul ◽  
Madhurima Majumder
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Learning Algorithm ◽  
Monitoring Tool ◽  
Statistical Parameters ◽  
Machine Learning Classification ◽  
High Risk Women ◽  
Previous State ◽  
The University ◽  
Intrapartum Complications

Abstract A major contributor to under-five mortality is the death of children in the 1st month of life. Intrapartum complications are one of the major causes of perinatal mortality. Fetal cardiotocograph (CTGs) can be used as a monitoring tool to identify high-risk women during labor. The objective of this study was to study the precision of machine learning algorithm techniques on CTG data in identifying high-risk fetuses. CTG data of 2126 pregnant women were obtained from the University of California Irvine Machine Learning Repository. Out of 2126 CTG dataset 78% of them were normal, 14% were suspect, and 8 % had a pathological fetal state. To improve data imbalance SMOTE is applied followed by five different machine learning classification models were trained using CTG data. Sensitivity, precision, and F1 score for each class and overall accuracy of each model were obtained to predict normal, suspect, and pathological fetal states. For the model validity two statistical parameters MCC & kappa (k) are used. SMOTE based all the classification algorithm provides the higher degree of accuracy with minimum value is 96% and RF algorithm had the highest prediction accuracy about 98.01% which is quite satisfactory. Model validation statistical parameters MCC & kappa is maximum achieved by RF about 0.968 & 1 and for SVC is 0.977 & 1 respectively. Finally proposed work also compared with previous state of art techniques.

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