ROC Curve in GAMLSS as Prediction Tool for Big Data

2020 ◽  
pp. 247-257
Author(s):  
Andrea Marletta
Keyword(s):  
Big Data ◽  
Roc Curve ◽  
Prediction Tool

scholarly journals Predicting an Unfavourable Course in Patients Hospitalised for COVID-19: The PREDICT-COVID Study

2020 ◽  
Author(s):  
Michel Ducher ◽  
Christelle Elias ◽  
Nans Florens ◽  
Philippe Vanhems ◽  
jean pierre fauvel
Keyword(s):  
Roc Curve ◽  
Primary Outcome ◽  
External Validation ◽  
Clinical Decision ◽  
Median Number ◽  
Validation Dataset ◽  
Prediction Tool ◽  
University Hospitals ◽  
Clinical Tool ◽  
Internal Validation

Abstract Background. Clinical decision tools that have been proposed to predict the clinical course of patients admitted to hospital with COVID-19 are poorly presented and are at high risk of selection bias. The aim of the study was to propose a prediction clinical tool to predict an unfavourable outcome at the admission of a SARS-CoV2 infected patient that was carefully developed using a large learning database and that was developed from models derived from artificial intelligence.Methods. The PREDICT-COVID study is a post hoc analysis of the Noso-Cor study, a multicenter prospective, observational study. All patients infected by SARS-CoV2 hospitalized in one of the 11 Lyon-University hospitals since 8-March-2020 have been included. The PREDICT-COVID database was split in two separate datasets: the learning dataset (80%) was used for the development of the model and the validation dataset (20%) for internal validation. The primary composite outcome was the need for mechanical ventilation or admission into an intensive care unit, or death within 21 days of admission.Results. Data from 823 patients were analysed: age 70.6±16·9 years; body mass index 26.7±5·4 kg/m2 and median number of comorbidities was 2. Out of the 44 recorded variables, 11 that were the most linked to the primary outcome criteria were retained to develop the optimised risk prediction tool. At admission the 5 most informative predictors were, in descending order: C-Reactive Protein, neutrophil-to-lymphocyte ratio, aspartate transaminase, shortness of breath, and prothrombin time. The ten-fold cross validation of the optimised model had an area under the ROC curve of 0.76±0.06. The performance of the developed Bayesian model to predict the primary outcome of the validation dataset had a mean area under the ROC curve of 0.78, sensitivity of 60%, and specificity of 77%.Conclusions. The proposed optimised prediction tool that uses 11 routinely determined variables to predict an unfavourable course at admission for COVID-19 had satisfactory performance. For an external validation, the PREDICT-COVID prediction tool is available online at: https://www.hed.cc/?a=covid&n=NETCRIT21J.netaTrial registration: The Noso-Cor study was registered on ClinicalTrials (NCT04290780). The present analysis was registered on ClinicalTrials (NCT04412031) the June 2, 2020.

scholarly journals SBP-SITA: A sequence-based prediction tools for S-itaconation

2021 ◽  
Author(s):  
Laizhi Zhang ◽  
Xuanwen Wang ◽  
Lin Zhang ◽  
Yanzheng Meng ◽  
Yu Chen ◽  
...  
Keyword(s):  
Roc Curve ◽  
Regulatory Mechanism ◽  
Ensemble Model ◽  
Prediction Tool ◽  
Post Translational Modification ◽  
Prediction Tools ◽  
Essential Step ◽  
On Line ◽  
Auc Value ◽  
Ensemble Algorithm

As a recently-reported post-translational modification, S-itaconation plays an important role in inflammation suppression. In order to understand its regulatory mechanism in many life activities, the essential step is the recognition of S-itaconation. However, it is difficult to identify S-itaconation in the proteome for the high cost, which limits further investigation. In this study, we constructed an ensemble algorithm based on Soft Voting Classifier. The area under the ROC curve (AUC) value 0.73 for ensemble model. Accordingly, we constructed the on-line prediction tool dubbed SBP-SITA for easily identifying Cystine sites. SBP-SITA is available at http://www.bioinfogo.org/sbp-sita.

scholarly journals MRI Image Segmentation, Prediction and Diagnostic Accuracy: Deep Learning Framework and Machine Learning Techniques Analysis for Reducing the impact of Cardiac Diseases

2019 ◽  
Vol 9 (2) ◽  
pp. 333-342
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Big Data ◽  
Image Segmentation ◽  
Cardiovascular Diseases ◽  
Real Time ◽  
Roc Curve ◽  
Machine Learning Algorithms ◽  
Segmentation Method ◽  
Mri Image

Background: Usage of tele - monitoring system of electronic patient record (EHR) and magnetic reasoning is expected to increase rapidly in near future, yet numerous studies have examined cardiovascular risk prediction and statistic adoptive approach could improve clinical risk prediction. Objectives: To assess the performance outcomes of various techniques for predicting the risk of cardiovascular diseases and MRI image segmentation method on the basis of systematic review. Research Design: Retrospective Cardiovascular study. We associate UCI dataset, AHA dataset, real time patient datasets, hospital dataset and sunny broken dataset from 2017 to 2019, and predicted risk using the logistic regression, stochastic gradient boosted, random forest, SVM, ROC Curve, KNN algorithm, MXNET UNET. Measures: The proposed methods have been developed in four categories to accurately diagnose cardiovascular diseases. We assessed to analyze and compared the accuracy of four different machine learning algorithms with the ROC for assessing and diagnosing cardiovascular disease from UCI cardiac datasets. The research will then focus on to predict heart diseases automatically by segmenting and classifying the patients’ heart data in real- time with the help of machine learning algorithms, big data, wireless heart monitor and smart phones. We further improve the prediction accuracy by using logistic regression and ROC Curve to improve the prediction performance. Consequently, K- Nearest-Neighbor (KNN) method, R programming language and big data where applied to easily find the nearest hospitals, monitor and provide on-time visualization to the medical professionals. Finally, we propose automatic myocardial segmentation method for cardiac MRI on the basis of Deep Convolutional neural network. Results: Logistic Regression methods outperformed the standard accuracy rate even with application of ROC curve (AUC increased

Head Position Identification

1967 ◽  
Vol 10 (3) ◽  
pp. 438-448
Author(s):  
H. N. Wright
Keyword(s):  
Roc Curve ◽  
Head Position ◽  
Hearing Impaired ◽  
Type Ii ◽  
Experimental Conditions ◽  
Partial Explanation ◽  
Complex Sounds ◽  
Binaural Cues ◽  
Criterion Behavior ◽  
Stimulus Degradation

A binaural recording of traffic sounds that reached an artificial head oriented in five different positions was presented to five subjects, each of whom responded under four different criteria. The results showed that it is possible to examine the ability of listeners to localize sound while listening through earphones and that the criterion adopted by an individual listener is independent of his performance. For the experimental conditions used, the Type II ROC curve generated by manipulating criterion behavior was linear and consistent with a guessing model. Further experiments involving different degrees of stimulus degradation suggested a partial explanation for this finding and illustrated the various types of monaural and binaural cues used by normal and hearing-impaired listeners to localize complex sounds.

Find Out About 'Big Data' to Track Outcomes

ASHA Leader ◽  
2013 ◽  
Vol 18 (2) ◽  
pp. 59-59
Keyword(s):  
Big Data

Find Out About 'Big Data' to Track Outcomes

Correlating Personality and Actual Phone Usage

2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  
Keyword(s):  
Big Data ◽  
Social Network ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Self Report ◽  
Psychological Variables ◽  
New Approach ◽  
Report Data ◽  
Self Report Data ◽  
Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

“Big Data in Psychology: Methods and Applications”

2017 ◽  
Vol 225 (3) ◽  
pp. 287-288
Keyword(s):  
Big Data

An associated conference will take place at ZPID – Leibniz Institute for Psychology Information in Trier, Germany, on June 7–9, 2018. For further details, see: http://bigdata2018.leibniz-psychology.org

When the Sample Is the Population: Big Data

PsycCRITIQUES ◽  
2014 ◽  
Vol 59 (2) ◽  
Author(s):  
David J. Pittenger
Keyword(s):  
Big Data
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