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 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.

Download Full-text

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

Download Full-text

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

BMC Geriatrics ◽

10.1186/s12877-021-02229-3 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Tian-Hoe Tan ◽

Chien-Chin Hsu ◽

Chia-Jung Chen ◽

Shu-Lien Hsu ◽

Tzu-Lan Liu ◽

...

Keyword(s):

Machine Learning ◽

Information System ◽

Real Time ◽

Hospital Information System ◽

Clinical Utility ◽

Data Driven ◽

Health Records ◽

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 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.

Download Full-text

Data efficient Random Forest model for avalanche forecasting

10.5194/nhess-2019-379 ◽

2019 ◽

Author(s):

Manesh Chawla ◽

Amreek Singh

Keyword(s):

Machine Learning ◽

Random Forest ◽

Direct Reduction ◽

Random Forest Model ◽

Machine Learning Techniques ◽

Control Structures ◽

Avalanche Forecasting ◽

Forest Model ◽

Avalanche Flow ◽

Data Efficiency

Abstract. Fast downslope release of snow (avalanche) is a serious hazard to people living in snow bound mountains. Released snow mass can gain sufficient momentum on its down slope path to kill humans, uproot trees and rocks, destroy buildings. Direct reduction of avalanche threat is done by building control structures to add mechanical support to snowpack and reduce or deflect downward avalanche flow. On large terrains it is economically infeasible to use these methods on each high risk site.Therefore predicting and avoiding avalanches is the only feasible method to reduce threat but sufficient snow stability data for accurate forecasting is generally unavailable and difficult to collect. Forecasters infer snow stability from their knowledge of local weather, terrain and sparsely available snowpack observations. This inference process is vulnerable to human bias therefore machine learning models are used to find patterns from past data and generate helpful outputs to minimise and quantify uncertainty in forecasting process. These machine learning techniques require long past records of avalanches which are difficult to obtain. In this paper we propose a data efficient Random Forest model to address this problem. The model can generate a descriptive forecast showing reasoning and patterns which are difficult to observe manually. Our model advances the field by being inexpensive and convenient for operational forecasting due to its data efficiency, ease of automation and ability to describe its decisions.

Download Full-text

Machine Learning Models That Integrate Tumor Texture and Perfusion Characteristics Using Low-Dose Breast Computed Tomography Are Promising for Predicting Histological Biomarkers and Treatment Failure in Breast Cancer Patients

Cancers ◽

10.3390/cancers13236013 ◽

2021 ◽

Vol 13 (23) ◽

pp. 6013

Author(s):

Hyun-Soo Park ◽

Kwang-sig Lee ◽

Bo-Kyoung Seo ◽

Eun-Sil Kim ◽

Kyu-Ran Cho ◽

...

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Random Forest ◽

Cancer Patients ◽

Low Dose ◽

Random Forest Model ◽

Breast Cancer Patients ◽

Learning Models ◽

Forest Model ◽

Machine Learning Models

This prospective study enrolled 147 women with invasive breast cancer who underwent low-dose breast CT (80 kVp, 25 mAs, 1.01–1.38 mSv) before treatment. From each tumor, we extracted eight perfusion parameters using the maximum slope algorithm and 36 texture parameters using the filtered histogram technique. Relationships between CT parameters and histological factors were analyzed using five machine learning algorithms. Performance was compared using the area under the receiver-operating characteristic curve (AUC) with the DeLong test. The AUCs of the machine learning models increased when using both features instead of the perfusion or texture features alone. The random forest model that integrated texture and perfusion features was the best model for prediction (AUC = 0.76). In the integrated random forest model, the AUCs for predicting human epidermal growth factor receptor 2 positivity, estrogen receptor positivity, progesterone receptor positivity, ki67 positivity, high tumor grade, and molecular subtype were 0.86, 0.76, 0.69, 0.65, 0.75, and 0.79, respectively. Entropy of pre- and postcontrast images and perfusion, time to peak, and peak enhancement intensity of hot spots are the five most important CT parameters for prediction. In conclusion, machine learning using texture and perfusion characteristics of breast cancer with low-dose CT has potential value for predicting prognostic factors and risk stratification in breast cancer patients.

Download Full-text

Identification of the diagnostic signature of sepsis based on bioinformatic analysis of gene expression and machine learning

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207323666201204130031 ◽

2020 ◽

Vol 23 ◽

Author(s):

Qian Zhao ◽

Ning Xu ◽

Hui Guo ◽

Jianguo Li

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Random Forest ◽

Early Diagnosis ◽

Bioinformatic Analysis ◽

Random Forest Model ◽

Diagnostic Methods ◽

Ppi Network ◽

Hub Genes ◽

Forest Model

Background: Sepsis is a life-threatening disease caused by the dysregulated host response to the infection, and being the major cause of death to patients in intensive care unit (ICU). Objective: Early diagnosis of sepsis could significantly reduce in-hospital mortality. Though generated from infection, the development of sepsis follows its own psychological process and disciplines, alters with gender, health status and other factors. Hence, the analysis of mass data by bioinformatic tools and machine learning is a promising method for exploring early diagnosis manners. Method: We collected miRNA and mRNA expression data of sepsis blood samples from Gene Expression Omnibus (GEO) and ArrayExpress databases, screened out differentially expressed genes (DEGs) by R software, predicted miRNA targets on TargetScanHuman and miRTarBase websites, conducted Gene Ontology (GO) term and KEGG pathway enrichment based on overlapping DEGs. The STRING database and Cytoscape were used to build protein-protein interaction (PPI) network and predict hub genes. Then we constructed a Random Forest model by using the hub genes to assess sample type. Results: Bioinformatic analysis of GEO dataset revealed 46 overlapping DEGs in sepsis. The PPI network analysis identified five hub genes, SOCS3, KBTBD6, FBXL5, FEM1C and WSB1. Random Forest model based on these five hub genes was used to assess GSE95233 and GSE95233 datasets, and the area under curve (AUC) of ROC are 0.900 and 0.7988, respectively, which confirmed the efficacy of this model. Conclusion: The integrated analysis of gene expression in sepsis and the effective Random Forest model built in this study may provide promising diagnostic methods for sepsis.

Download Full-text

Machine Learning Approaches to Radiogenomics of Breast Cancer using Low-Dose Perfusion Computed Tomography: Predicting Prognostic Biomarkers and Molecular Subtypes

Scientific Reports ◽

10.1038/s41598-019-54371-z ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 2

Author(s):

Eun Kyung Park ◽

Kwang-sig Lee ◽

Bo Kyoung Seo ◽

Kyu Ran Cho ◽

Ok Hee Woo ◽

...

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Random Forest ◽

Invasive Breast Cancer ◽

Low Dose ◽

Molecular Subtypes ◽

Random Forest Model ◽

Prognostic Biomarkers ◽

Learning Approaches ◽

Forest Model

AbstractRadiogenomics investigates the relationship between imaging phenotypes and genetic expression. Breast cancer is a heterogeneous disease that manifests complex genetic changes and various prognosis and treatment response. We investigate the value of machine learning approaches to radiogenomics using low-dose perfusion computed tomography (CT) to predict prognostic biomarkers and molecular subtypes of invasive breast cancer. This prospective study enrolled a total of 723 cases involving 241 patients with invasive breast cancer. The 18 CT parameters of cancers were analyzed using 5 machine learning models to predict lymph node status, tumor grade, tumor size, hormone receptors, HER2, Ki67, and the molecular subtypes. The random forest model was the best model in terms of accuracy and the area under the receiver-operating characteristic curve (AUC). On average, the random forest model had 13% higher accuracy and 0.17 higher AUC than the logistic regression. The most important CT parameters in the random forest model for prediction were peak enhancement intensity (Hounsfield units), time to peak (seconds), blood volume permeability (mL/100 g), and perfusion of tumor (mL/min per 100 mL). Machine learning approaches to radiogenomics using low-dose perfusion breast CT is a useful noninvasive tool for predicting prognostic biomarkers and molecular subtypes of invasive breast cancer.

Download Full-text

Characterization of Molecular Cluster Detection and Evaluation of Cluster Investigation Criteria Using Machine Learning Methods and Statewide Surveillance Data in Washington State

Viruses ◽

10.3390/v12020142 ◽

2020 ◽

Vol 12 (2) ◽

pp. 142 ◽

Cited By ~ 1

Author(s):

Steven J. Erly ◽

Joshua T. Herbeck ◽

Roxanne P. Kerani ◽

Jennifer R. Reuer

Keyword(s):

Machine Learning ◽

Random Forest ◽

Hiv Transmission ◽

White Male ◽

Washington State ◽

Cluster Detection ◽

Random Forest Model ◽

Molecular Cluster ◽

Forest Model ◽

Sex With Men

Molecular cluster detection can be used to interrupt HIV transmission but is dependent on identifying clusters where transmission is likely. We characterized molecular cluster detection in Washington State, evaluated the current cluster investigation criteria, and developed a criterion using machine learning. The population living with HIV (PLWH) in Washington State, those with an analyzable genotype sequences, and those in clusters were described across demographic characteristics from 2015 to2018. The relationship between 3- and 12-month cluster growth and demographic, clinical, and temporal predictors were described, and a random forest model was fit using data from 2016 to 2017. The ability of this model to identify clusters with future transmission was compared to Centers for Disease Control and Prevention (CDC) and the Washington state criteria in 2018. The population with a genotype was similar to all PLWH, but people in a cluster were disproportionately white, male, and men who have sex with men. The clusters selected for investigation by the random forest model grew on average 2.3 cases (95% CI 1.1–1.4) in 3 months, which was not significantly larger than the CDC criteria (2.0 cases, 95% CI 0.5–3.4). Disparities in the cases analyzed suggest that molecular cluster detection may not benefit all populations. Jurisdictions should use auxiliary data sources for prediction or continue using established investigation criteria.

Download Full-text

Real-Time Sentiment Analysis of 2019 Election Tweets using Word2vec and Random Forest Model

2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT) ◽

10.1109/icct46177.2019.8969049 ◽

2019 ◽

Cited By ~ 1

Author(s):

MSR Hitesh ◽

Vedhosi Vaibhav ◽

Y.J Abhishek Kalki ◽

Suraj Harsha Kamtam ◽

Santoshi Kumari

Keyword(s):

Random Forest ◽

Sentiment Analysis ◽

Real Time ◽

Random Forest Model ◽

Forest Model

Download Full-text

Accurate prediction of birth implementing a statistical model through the determination of steroid hormones in saliva

Scientific Reports ◽

10.1038/s41598-021-84924-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Silvia Alonso ◽

Sara Cáceres ◽

Daniel Vélez ◽

Luis Sanz ◽

Gema Silvan ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Area Under The Curve ◽

Machine Learning Algorithms ◽

Random Forest Model ◽

Forest Model ◽

Spontaneous Labour ◽

Hormonal Mechanism ◽

First Time ◽

Estrone Sulphate

AbstractSteroidal hormone interaction in pregnancy is crucial for adequate fetal evolution and preparation for childbirth and extrauterine life. Estrone sulphate, estriol, progesterone and cortisol play important roles in the initiation of labour mechanism at the start of contractions and cervical effacement. However, their interaction remains uncertain. Although several studies regarding the hormonal mechanism of labour have been reported, the prediction of date of birth remains a challenge. In this study, we present for the first time machine learning algorithms for the prediction of whether spontaneous labour will occur from week 37 onwards. Estrone sulphate, estriol, progesterone and cortisol were analysed in saliva samples collected from 106 pregnant women since week 34 by enzyme-immunoassay (EIA) techniques. We compared a random forest model with a traditional logistic regression over a dataset constructed with the values observed of these measures. We observed that the results, evaluated in terms of accuracy and area under the curve (AUC) metrics, are sensibly better in the random forest model. For this reason, we consider that machine learning methods contribute in an important way to the obstetric practice.

Download Full-text

Utilizing physics-based input features within a machine learning model to predict wind speed forecasting error

Wind Energy Science ◽

10.5194/wes-6-295-2021 ◽

2021 ◽

Vol 6 (1) ◽

pp. 295-309

Author(s):

Daniel Vassallo ◽

Raghavendra Krishnamurthy ◽

Harindra J. S. Fernando

Keyword(s):

Machine Learning ◽

Wind Speed ◽

Random Forest ◽

Hybrid Model ◽

Arima Model ◽

Time Of Day ◽

Random Forest Model ◽

Forest Model ◽

Forecasting Error ◽

Atmospheric Variables

Abstract. Machine learning is quickly becoming a commonly used technique for wind speed and power forecasting. Many machine learning methods utilize exogenous variables as input features, but there remains the question of which atmospheric variables are most beneficial for forecasting, especially in handling non-linearities that lead to forecasting error. This question is addressed via creation of a hybrid model that utilizes an autoregressive integrated moving-average (ARIMA) model to make an initial wind speed forecast followed by a random forest model that attempts to predict the ARIMA forecasting error using knowledge of exogenous atmospheric variables. Variables conveying information about atmospheric stability and turbulence as well as inertial forcing are found to be useful in dealing with non-linear error prediction. Streamwise wind speed, time of day, turbulence intensity, turbulent heat flux, vertical velocity, and wind direction are found to be particularly useful when used in unison for hourly and 3 h timescales. The prediction accuracy of the developed ARIMA–random forest hybrid model is compared to that of the persistence and bias-corrected ARIMA models. The ARIMA–random forest model is shown to improve upon the latter commonly employed modeling methods, reducing hourly forecasting error by up to 5 % below that of the bias-corrected ARIMA model and achieving an R2 value of 0.84 with true wind speed.

Download Full-text