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Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

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In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
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Receiver Operating Characteristic Plot01:15

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A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Updated: Jul 4, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Cost-sensitive ordinal classification methods to predict SARS-CoV-2 pneumonia severity.

Fernando Garcia-Garcia, Dae-Jin Lee, Pedro Pablo Espana Yandiola

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    Cost-sensitive ordinal artificial intelligence-machine learning strategies effectively predict SARS-CoV-2 pneumonia severity. These advanced AI-ML models outperformed traditional scores, offering improved clinical prognosis.

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    Area of Science:

    • Artificial Intelligence and Machine Learning
    • Medical Informatics
    • Public Health

    Background:

    • Prognosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia is critical for patient management.
    • Traditional clinical severity scores have limitations in accurately predicting patient outcomes.
    • Artificial intelligence-machine learning (AI-ML) offers potential for enhanced prognostic capabilities.

    Purpose of the Study:

    • To evaluate the suitability of cost-sensitive ordinal AI-ML strategies for SARS-CoV-2 pneumonia severity prognosis.
    • To develop and compare novel AI-ML models against established clinical scores.

    Main Methods:

    • An observational, retrospective cohort study involving 1548 patients across four Spanish hospitals.
    • Development of 260 distinct AI-ML models using ordinal decomposition and cost-sensitive resampling techniques.
    • Performance evaluation via nested cross-validation, comparing the best model against five clinical scores and a standard AI-ML baseline.

    Main Results:

    • The best AI-ML model, utilizing no imputation, full feature set, ordinal partitions, cost-based rebalancing, and Gradient Boosting, achieved a median accuracy of 68.1% and an AUC of 0.802.
    • This model demonstrated superior performance compared to all five clinical severity scores and the standard AI-ML baseline.
    • The model successfully leveraged ordinal information and managed class imbalance and asymmetric costs.

    Conclusions:

    • Cost-sensitive ordinal AI-ML strategies are suitable and effective for SARS-CoV-2 pneumonia severity prognosis.
    • The developed AI-ML model outperformed existing methods, highlighting the potential of these advanced techniques.
    • Ordinal and cost-sensitive aspects in AI-ML for clinical prognosis are under-explored but offer significant advantages.