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01:11Residuals and Least-Squares Property
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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
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.8K
01:22Purposive Learning
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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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01:03Prediction Intervals
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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.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
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Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation.
Subendhu Rongali1, Adam J Rose2, David D McManus3
1College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA, United States.
Journal of Medical Internet Research
|March 24, 2020
Summary
A new model called CLOUT (long short-term memory outcome prediction using comprehensive feature relations) accurately predicts patient mortality using electronic health records. CLOUT outperforms existing models and identifies key risk factors, aiding clinical decision-making.
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Area of Science:
- * Health informatics
- * Artificial intelligence in medicine
- * Clinical prediction models
Background:
- * Electronic health record (EHR) data is crucial for health outcome prediction.
- * Existing machine learning models often fail to integrate diverse clinical data types (e.g., lab results, diagnoses, medications).
- * There is a need for models that capture relationships between different clinical features for improved predictive accuracy.
Purpose of the Study:
- * To develop and evaluate novel neural network models that incorporate relations between various clinical features within EHR data.
- * To predict patient mortality in intensive care units (ICUs) using longitudinal EHR data.
- * To compare the performance of the proposed models against established methods like logistic regression and other neural networks.
Main Methods:
- * Development of a novel neural network architecture named CLOUT (long short-term memory outcome prediction using comprehensive feature relations).
- * CLOUT utilizes a correlational neural network to create a latent space representation of discrete clinical features.
- * Integration of this latent representation into a long short-term memory (LSTM)-based predictive framework, including ablation studies for risk factor identification.
Main Results:
- * CLOUT achieved a superior area under the receiver operating characteristic curve (0.89) compared to logistic regression (0.82) and other baseline neural network models (<0.86).
- * Experiments were conducted on the Medical Information Mart for Intensive Care-III (MIMIC-III) dataset, involving 7537 patients.
- * Risk factors identified by CLOUT showed significantly higher agreement with physician assessments than those from logistic regression.
Conclusions:
- * The CLOUT model demonstrates significant potential for real-world clinical application in predicting patient mortality.
- * The model's ability to capture complex feature relations enhances its predictive power.
- * CLOUT offers a valuable tool for identifying high-risk patients, supporting timely clinical interventions.