Development and Validation of Prediction Models for the Risk of Knee Replacement Using Data from Multi-Center Cohort Studies

Background:Accelerometry has been established as an objective method that can be used to assess physical activity behavior in large groups. The purpose of the current study was to provide a validated equation to translate accelerometer counts of the triaxial GT3X into energy expenditure in young children.Methods:Thirty-two children aged 5–9 years performed locomotor and play activities that are typical for their age group. Children wore a GT3X accelerometer and their energy expenditure was measured with indirect calorimetry. Twenty-one children were randomly selected to serve as development group. A cubic 2-regression model involving separate equations for locomotor and play activities was developed on the basis of model fit. It was then validated using data of the remaining children and compared with a linear 2-regression model and a linear 1-regression model.Results:All 3 regression models produced strong correlations between predicted and measured MET values. Agreement was acceptable for the cubic model and good for both linear regression approaches.Conclusions:The current linear 1-regression model provides valid estimates of energy expenditure for ActiGraph GT3X data for 5- to 9-year-old children and shows equal or better predictive validity than a cubic or a linear 2-regression model.

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Update and, internal and temporal-validation of the FRANCE-2 and ACC-TAVI early-mortality prediction models for Transcatheter aortic Valve Implantation (TAVI) using data from the Netherlands heart registration (NHR)

IJC Heart & Vasculature ◽

10.1016/j.ijcha.2021.100716 ◽

2021 ◽

Vol 32 ◽

pp. 100716 ◽

Cited By ~ 1

Author(s):

Hatem Al-Farra ◽

Bas A.J.M. de Mol ◽

Anita C.J. Ravelli ◽

W.J.P.P. ter Burg ◽

Saskia Houterman ◽

...

Keyword(s):

Aortic Valve ◽

Prediction Models ◽

Mortality Prediction ◽

Early Mortality ◽

Transcatheter Aortic Valve ◽

Temporal Validation ◽

Mortality Prediction Models ◽

Aortic Valve Implantation ◽

Using Data ◽

Valve Implantation

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Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models

Energies ◽

10.3390/en14082332 ◽

2021 ◽

Vol 14 (8) ◽

pp. 2332

Author(s):

Cecilia Martinez-Castillo ◽

Gonzalo Astray ◽

Juan Carlos Mejuto

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Random Forests ◽

Prediction Models ◽

Meteorological Station ◽

Ann Model ◽

Global Irradiation ◽

Artificial Neural ◽

Input Variables ◽

Using Data

Different prediction models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to model the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best model was checked in two independent stations. The results obtained confirmed that the best methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 1226 kJ/(m2∙day) and 1136 kJ/(m2∙day), respectively, and predict conveniently for independent stations, 2013 kJ/(m2∙day) and 2094 kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

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The value of total knee replacement revisited: A comparative analysis of its effectiveness and impact on healthcare costs using data from the osteoarthritis initiative

Osteoarthritis and Cartilage ◽

10.1016/j.joca.2016.01.094 ◽

2016 ◽

Vol 24 ◽

pp. S39

Author(s):

B.S. Ferket ◽

Z. Feldman ◽

J. Zhou ◽

E.H. Oei ◽

S.M. Bierma-Zeinstra ◽

...

Keyword(s):

Comparative Analysis ◽

Total Knee Replacement ◽

Knee Replacement ◽

Healthcare Costs ◽

Osteoarthritis Initiative ◽

Total Knee ◽

Using Data

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Development and validation of prediction models for blood concentrations of dioxins and PCBs using dietary intakes

Environment International ◽

10.1016/j.envint.2012.09.003 ◽

2012 ◽

Vol 50 ◽

pp. 15-21 ◽

Cited By ~ 19

Author(s):

Helen Engelstad Kvalem ◽

Anne Lise Brantsæter ◽

Helle Margrete Meltzer ◽

Hein Stigum ◽

Cathrine Thomsen ◽

...

Keyword(s):

Prediction Models ◽

Dietary Intakes ◽

Blood Concentrations ◽

Development And Validation

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An Objective Search for Unrecognized Bias in Validated COVID-19 Prediction Models

10.1101/2021.10.28.21265629 ◽

2021 ◽

Author(s):

Hossein Estiri ◽

Zachary Strasser ◽

Sina Rashidian ◽

Jeffrey Klann ◽

Kavishwar Wagholikar ◽

...

Keyword(s):

Predictive Models ◽

Medical Records ◽

Prediction Models ◽

Evaluation Studies ◽

Protected Group ◽

Increased Risk ◽

Using Data ◽

Bias Evaluation ◽

Algorithmic Bias

The growing recognition of algorithmic bias has spurred discussions about fairness in artificial intelligence (AI) / machine learning (ML) algorithms. The increasing translation of predictive models into clinical practice brings an increased risk of direct harm from algorithmic bias; however, bias remains incompletely measured in many medical AI applications. Using data from over 56 thousand Mass General Brigham (MGB) patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we evaluate unrecognized bias in four AI models developed during the early months of the pandemic in Boston, Massachusetts that predict risks of hospital admission, ICU admission, mechanical ventilation, and death after a SARS-CoV-2 infection purely based on their pre-infection longitudinal medical records. We discuss that while a model can be biased against certain protected groups (i.e., perform worse) in certain tasks, it can be at the same time biased towards another protected group (i.e., perform better). As such, current bias evaluation studies may lack a full depiction of the variable effects of a model on its subpopulations. If the goal is to make a change in a positive way, the underlying roots of bias need to be fully explored in medical AI. Only a holistic evaluation, a diligent search for unrecognized bias, can provide enough information for an unbiased judgment of AI bias that can invigorate follow-up investigations on identifying the underlying roots of bias and ultimately make a change.

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Reliable Deep Learning–Based Detection of Misplaced Chest Electrodes During Electrocardiogram Recording: Algorithm Development and Validation

JMIR Medical Informatics ◽

10.2196/25347 ◽

2021 ◽

Vol 9 (4) ◽

pp. e25347

Author(s):

Khaled Rjoob ◽

Raymond Bond ◽

Dewar Finlay ◽

Victoria McGilligan ◽

Stephen J Leslie ◽

...

Keyword(s):

Deep Learning ◽

Ventricular Hypertrophy ◽

Left Ventricular ◽

Intercostal Space ◽

The Past ◽

Body Surface Potential Maps ◽

Body Surface Potential ◽

Using Data ◽

Development And Validation ◽

Electrocardiogram Ecg

Background A 12-lead electrocardiogram (ECG) is the most commonly used method to diagnose patients with cardiovascular diseases. However, there are a number of possible misinterpretations of the ECG that can be caused by several different factors, such as the misplacement of chest electrodes. Objective The aim of this study is to build advanced algorithms to detect precordial (chest) electrode misplacement. Methods In this study, we used traditional machine learning (ML) and deep learning (DL) to autodetect the misplacement of electrodes V1 and V2 using features from the resultant ECG. The algorithms were trained using data extracted from high-resolution body surface potential maps of patients who were diagnosed with myocardial infarction, diagnosed with left ventricular hypertrophy, or a normal ECG. Results DL achieved the highest accuracy in this study for detecting V1 and V2 electrode misplacement, with an accuracy of 93.0% (95% CI 91.46-94.53) for misplacement in the second intercostal space. The performance of DL in the second intercostal space was benchmarked with physicians (n=11 and age 47.3 years, SD 15.5) who were experienced in reading ECGs (mean number of ECGs read in the past year 436.54, SD 397.9). Physicians were poor at recognizing chest electrode misplacement on the ECG and achieved a mean accuracy of 60% (95% CI 56.09-63.90), which was significantly poorer than that of DL (P<.001). Conclusions DL provides the best performance for detecting chest electrode misplacement when compared with the ability of experienced physicians. DL and ML could be used to help flag ECGs that have been incorrectly recorded and flag that the data may be flawed, which could reduce the number of erroneous diagnoses.

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