Predicting outcomes in older ED patients with influenza in real time using a big data-driven and machine learning approach to the hospital information system

Abstract Background Predicting outcomes in older patients with influenza in the emergency department (ED) by machine learning (ML) has never been implemented. Therefore, we conducted this study to clarify the clinical utility of implementing ML. Methods We recruited 5508 older ED patients (≥65 years old) in three hospitals between 2009 and 2018. Patients were randomized into a 70%/30% split for model training and testing. Using 10 clinical variables from their electronic health records, a prediction model using the synthetic minority oversampling technique preprocessing algorithm was constructed to predict five outcomes. Results The best areas under the curves of predicting outcomes were: random forest model for hospitalization (0.840), pneumonia (0.765), and sepsis or septic shock (0.857), XGBoost for intensive care unit admission (0.902), and logistic regression for in-hospital mortality (0.889) in the testing data. The predictive model was further applied in the hospital information system to assist physicians’ decisions in real time. Conclusions ML is a promising way to assist physicians in predicting outcomes in older ED patients with influenza in real time. Evaluations of the effectiveness and impact are needed in the future.

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Predicting Outcomes in Older ED Patients with Influenza in Real Time Using a Big Data-Driven and Machine Learning Approach to the Hospital Information System

10.21203/rs.3.rs-55908/v1 ◽

2020 ◽

Author(s):

Chien-Chin Hsu ◽

Tian-Hoe Tan ◽

Chia-Jung Chen ◽

Shu-Lien Hsu ◽

Tzu-Lan Liu ◽

...

Keyword(s):

Machine Learning ◽

Information System ◽

Random Forest ◽

Real Time ◽

Hospital Information System ◽

Random Forest Model ◽

Forest Model ◽

Machine Learning Approach ◽

Testing Data ◽

Model Training

Abstract Background: Predicting outcomes in older patients with influenza in the emergency department (ED) by machine learning (ML) has never been implemented. Therefore, we conducted this study to clarify the clinical utility of implementing ML.Methods: We recruited 5508 older ED patients (≥65 years old) in three hospitals between 2009 and 2018. Patients were randomized into a 70%/30% split for ML model training and testing. Using 10 clinical variables from their electronic health records, a random forest model using the synthetic minority oversampling technique preprocessing algorithm was constructed to predict five outcomes. Results: The areas under the curves of predicting outcomes using the random forest model were 0.807 for hospitalizations, 0.974 for pneumonia, 0.994 for sepsis or septic shock, 0.981 for intensive care unit admission, and 0.851 for death in the testing data. The predictive model was further applied in the hospital information system to assist physicians’ decisions in real time.Conclusions: ML is a promising way to assist physicians in predicting outcomes in older ED patients with influenza in real time. Evaluations of the effectiveness and impact are needed in the future.

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Predicting Outcomes in Older ED Patients with Influenza in Real Time Using A Big Data-Driven and Machine Learning Approach to the Hospital Information System

10.21203/rs.3.rs-94535/v1 ◽

2020 ◽

Author(s):

Chien-Chin Hsu ◽

Tian-Hoe Tan ◽

Chia-Jung Chen ◽

Shu-Lien Hsu ◽

Tzu-Lan Liu ◽

...

Keyword(s):

Machine Learning ◽

Information System ◽

Random Forest ◽

Real Time ◽

Hospital Information System ◽

Random Forest Model ◽

Forest Model ◽

Machine Learning Approach ◽

Testing Data ◽

Model Training

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Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

10.31234/osf.io/eqcdx ◽

2019 ◽

Author(s):

Oskar Flygare ◽

Jesper Enander ◽

Erik Andersson ◽

Brjánn Ljótsson ◽

Volen Z Ivanov ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Random Forests ◽

Clinical Utility ◽

Body Dysmorphic Disorder ◽

Prediction Models ◽

Behavioral Therapy ◽

Learning Approach ◽

Learning Approaches ◽

Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

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Joint Statistical and Machine Learning Approach for Practical Data-Driven Assessment of User Throughput Quality in Microcellular Radio Networks

Wireless Personal Communications ◽

10.1007/s11277-021-08300-x ◽

2021 ◽

Author(s):

Isabona Joseph

Keyword(s):

Machine Learning ◽

Radio Networks ◽

Data Driven ◽

Learning Approach ◽

Machine Learning Approach ◽

User Throughput

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Accurate Energy Forecast in Buildings: A Data Driven Machine Learning Approach

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) ◽

10.1109/ccece.2019.8861583 ◽

2019 ◽

Cited By ~ 1

Author(s):

Alain B. Tchagang ◽

Araz Ashouri

Keyword(s):

Machine Learning ◽

Data Driven ◽

Learning Approach ◽

Machine Learning Approach ◽

Energy Forecast

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Application of data driven machine learning approach for modelling of non-linear filtration through granular porous media

International Journal of Heat and Mass Transfer ◽

10.1016/j.ijheatmasstransfer.2021.121650 ◽

2021 ◽

Vol 179 ◽

pp. 121650

Author(s):

Ashes Banerjee ◽

Srinivas Pasupuleti ◽

Koushik Mondal ◽

M. Mousavi Nezhad

Keyword(s):

Machine Learning ◽

Porous Media ◽

Data Driven ◽

Learning Approach ◽

Machine Learning Approach ◽

Non Linear ◽

Granular Porous Media ◽

Linear Filtration

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A Machine Learning Approach for Predicting Real-time Risk of Intraoperative Hypotension in Traumatic Brain Injury

Journal of Neurosurgical Anesthesiology ◽

10.1097/ana.0000000000000819 ◽

2021 ◽

Vol Publish Ahead of Print ◽

Author(s):

Shara I. Feld ◽

Daniel S. Hippe ◽

Ljubomir Miljacic ◽

Nayak L. Polissar ◽

Shu-Fang Newman ◽

...

Keyword(s):

Machine Learning ◽

Traumatic Brain Injury ◽

Brain Injury ◽

Real Time ◽

Learning Approach ◽

Intraoperative Hypotension ◽

Machine Learning Approach

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Anomaly based Real Time Prevention of under Rated App-DDOS Attacks on Web: An Experiential Metrics based Machine Learning Approach

Indian Journal of Science and Technology ◽

10.17485/ijst/2016/v9i27/87872 ◽

2016 ◽

Vol 9 (27) ◽

Cited By ~ 1

Author(s):

K. Munivara Prasad ◽

A. Rama Mohan Reddy ◽

K. Venugopal Rao

Keyword(s):

Machine Learning ◽

Real Time ◽

Learning Approach ◽

Ddos Attacks ◽

Machine Learning Approach

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Evaluation of the COVID-19 Era by Using Machine Learning and Interpretation of Confidential Dataset

Electronics ◽

10.3390/electronics10232910 ◽

2021 ◽

Vol 10 (23) ◽

pp. 2910

Author(s):

Andreas Andreou ◽

Constandinos X. Mavromoustakis ◽

George Mastorakis ◽

Jordi Mongay Batalla ◽

Evangelos Pallis

Keyword(s):

Machine Learning ◽

Real Time ◽

Research Study ◽

Nonlinear Least Squares ◽

Data Driven ◽

Machine Learning Technique ◽

Marquardt Algorithm ◽

Learning Technique ◽

Iot Devices ◽

Algorithmic Techniques

Various research approaches to COVID-19 are currently being developed by machine learning (ML) techniques and edge computing, either in the sense of identifying virus molecules or in anticipating the risk analysis of the spread of COVID-19. Consequently, these orientations are elaborating datasets that derive either from WHO, through the respective website and research portals, or from data generated in real-time from the healthcare system. The implementation of data analysis, modelling and prediction processing is performed through multiple algorithmic techniques. The lack of these techniques to generate predictions with accuracy motivates us to proceed with this research study, which elaborates an existing machine learning technique and achieves valuable forecasts by modification. More specifically, this study modifies the Levenberg–Marquardt algorithm, which is commonly beneficial for approaching solutions to nonlinear least squares problems, endorses the acquisition of data driven from IoT devices and analyses these data via cloud computing to generate foresight about the progress of the outbreak in real-time environments. Hence, we enhance the optimization of the trend line that interprets these data. Therefore, we introduce this framework in conjunction with a novel encryption process that we are proposing for the datasets and the implementation of mortality predictions.

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