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Intern 曾順承. Introduction 1. Computerized medical decision support systems Discriminant analysis Logistic regression Recursive partitioning Neural networks. Introduction 2.

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Intern

Intern 曾順承


Introduction 1
Introduction1

  • Computerized medical decision support systems

    • Discriminant analysis

    • Logistic regression

    • Recursive partitioning

    • Neural networks


Introduction 2
Introduction2

  • This report compares three different mathematical models for building a traumatic brain injury (TBI) medical decision support system (MDSS)

    • A logistic regression model

    • A multi-layer perceptron (MLP) neural network

    • A radial-basis-function (RBF) neural network




Introduction 5
Introduction5

  • These models were developed based on a large TBI patient database.

  • This MDSS accepts a set of patient data

    • The types of skull fracture

    • Glasgow Coma Scale (GCS)

    • Episode of convulsion

    • Return the chance that a neurosurgeon would recommend an open-skull surgery for this patient.


Method 1
Method1

  • From the 12640 patients selected from the database

    • A randomly drawn 9480 cases were used as the training group to develop/train our models

    • The other 3160 cases were in the validation group which we used to evaluate the performance of these models


Method 2
Method2

  • The indicator of how accurate these models are in predicting a neurosurgeon’s decision on open-skull surgery

    • Sensitivity

    • Specificity

    • Areas under receiver-operating characteristics (ROC) curve and calibration curves


Result 1
Result1

  • Assuming equal importance of sensitivity and specificity

    • The logistic regression model (sensitivity, specificity) of(73%, 68%)

    • The RBF model (80%, 80%)

    • The MLP model (88%, 80%)



Result 3
Result3

  • The resultant areas under ROC curve

    • Logistic regression: 0.761

    • RBF: 0.880

    • MLP: 0.897

    • P < 0.05

  • Among these models, the logistic regression has noticeably poorer calibration.



Conclusion 1
Conclusion1

  • This study demonstrated the feasibility of applying neural networks as the mechanism for TBI decision support systems based on clinical databases.


Conclusion 2
Conclusion2

  • The results also suggest that neural networks may be a better solution for complex, non-linear medical decision support systems than conventional statistical techniques such as logistic regression.



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