Difference between revisions of "Ontology driven decision support for the diagnosis of mild cognitive impairment"

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===Introduction===
 
===Introduction===
 
This paper focuses on the development of an ontology for mild cognitive impairment (MCI). Alzheimer’s Disease (AD) can be diagnosed with reasonable accuracy at the stage of dementia, which is a point at which little can be done to achieve a favorable outcome. Hence there is a keen interest in being able to diagnose AD before the dementia stage is reached. Studies at Mayo’s Alzheimer’s Disease Research Center (ADRC) show that 8 out of 10 patients with MCI will convert to AD. Being able to accurately diagnose MCI can help with diagnosing AD pre-dementia stage.
 
This paper focuses on the development of an ontology for mild cognitive impairment (MCI). Alzheimer’s Disease (AD) can be diagnosed with reasonable accuracy at the stage of dementia, which is a point at which little can be done to achieve a favorable outcome. Hence there is a keen interest in being able to diagnose AD before the dementia stage is reached. Studies at Mayo’s Alzheimer’s Disease Research Center (ADRC) show that 8 out of 10 patients with MCI will convert to AD. Being able to accurately diagnose MCI can help with diagnosing AD pre-dementia stage.
Prior methods for diagnosing MCI used observation based criteria which are subject to bias and could result in misdiagnosis<ref name=”main_article”> Zhang, X., Hu, B., Ma, X., Moore, P., & Chen, J. (2014). Ontology driven decision support for the diagnosis of mild cognitive impairment. Computer Methods and Programs in Biomedicine, 113(3), 781–791. http://doi.org/10.1016/j.cmpb.2013.12.023</ref>. To remove the possibility of subjectivity resulting in misdiagnosis, an ontology for MCI containing specialized MRI knowledge about the cortical thickness of the brain structure was created to be used by clinical decision support systems when analyzing brain MRI scans for a patient.
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Prior methods for diagnosing MCI used observation based criteria which are subject to bias and could result in misdiagnosis<ref name="main_article"> Zhang, X., Hu, B., Ma, X., Moore, P., & Chen, J. (2014). Ontology driven decision support for the diagnosis of mild cognitive impairment. Computer Methods and Programs in Biomedicine, 113(3), 781–791. http://doi.org/10.1016/j.cmpb.2013.12.023</ref>. To remove the possibility of subjectivity resulting in misdiagnosis, an ontology for MCI containing specialized MRI knowledge about the cortical thickness of the brain structure was created to be used by clinical decision support systems when analyzing brain MRI scans for a patient.
  
 
===Method===
 
===Method===
The components of their framework consist of a MCI knowledge repository, an inference mechanism (rule set), a feature obtaining process (measurements of the cortical thickness) and data processing mechanism<ref name="main_article"></ref>. The inference mechanism uses the C4.5 algorithm and it was trained using MRI data for 187 MCI patients and 177 non-MCI patients who served as normal controls.
+
The components of their framework consist of a MCI knowledge repository, an inference mechanism (rule sets extracted using machine learning algorithms), a feature obtaining process (measurements of the cortical thickness) and data processing mechanism
 +
<ref name="main_article"></ref>. The inference mechanism uses the C4.5 algorithm and it was trained using MRI data for 187 MCI patients and 177 non-MCI patients who served as normal controls.
  
 
===Results===
 
===Results===
The obtained a sensitivity score of 80.2% and with 10-fold cross validation, they were able to show that it performed better than other algorithms like support vector machines (SVM) and Bayesian network (BN) and back propagation (BP) <ref name="main_article"></ref>.
+
They obtained a sensitivity score of 80.2% and with 10-fold cross validation, they were able to show that it performed better than other algorithms like support vector machines (SVM) and Bayesian network (BN) and back propagation (BP) <ref name="main_article"></ref>.
 +
 
 +
==Comments==
 +
This is an extensive study using machine learning algorithms with MRI data for patient diagnosis. Validation using independent data sets would be important for clinical translation.
  
 
==Second review==
 
==Second review==
 
Write something here
 
Write something here
 +
 +
==Related Articles==
 +
[[Improving Clinical Practice Using Clinical Decision Support Systems: A Systematic Review of Trials to Identify Features Critical to Success]]
 +
 +
[[Clinical Decision Support Systems (CDSS) for preventive management of COPD patients]]
 +
 +
[[Clinical Decision Support for Early Recognition of Sepsis]]
  
 
===References===
 
===References===
 
<references/>
 
<references/>
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[[Category: Reviews]]
 +
[[Category: CDS]]
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[[Category:Evidence Based Medicine (EBM)]]

Latest revision as of 14:30, 11 October 2015

First Review

Introduction

This paper focuses on the development of an ontology for mild cognitive impairment (MCI). Alzheimer’s Disease (AD) can be diagnosed with reasonable accuracy at the stage of dementia, which is a point at which little can be done to achieve a favorable outcome. Hence there is a keen interest in being able to diagnose AD before the dementia stage is reached. Studies at Mayo’s Alzheimer’s Disease Research Center (ADRC) show that 8 out of 10 patients with MCI will convert to AD. Being able to accurately diagnose MCI can help with diagnosing AD pre-dementia stage. Prior methods for diagnosing MCI used observation based criteria which are subject to bias and could result in misdiagnosis[1]. To remove the possibility of subjectivity resulting in misdiagnosis, an ontology for MCI containing specialized MRI knowledge about the cortical thickness of the brain structure was created to be used by clinical decision support systems when analyzing brain MRI scans for a patient.

Method

The components of their framework consist of a MCI knowledge repository, an inference mechanism (rule sets extracted using machine learning algorithms), a feature obtaining process (measurements of the cortical thickness) and data processing mechanism [1]. The inference mechanism uses the C4.5 algorithm and it was trained using MRI data for 187 MCI patients and 177 non-MCI patients who served as normal controls.

Results

They obtained a sensitivity score of 80.2% and with 10-fold cross validation, they were able to show that it performed better than other algorithms like support vector machines (SVM) and Bayesian network (BN) and back propagation (BP) [1].

Comments

This is an extensive study using machine learning algorithms with MRI data for patient diagnosis. Validation using independent data sets would be important for clinical translation.

Second review

Write something here

Related Articles

Improving Clinical Practice Using Clinical Decision Support Systems: A Systematic Review of Trials to Identify Features Critical to Success

Clinical Decision Support Systems (CDSS) for preventive management of COPD patients

Clinical Decision Support for Early Recognition of Sepsis

References

  1. 1.0 1.1 1.2 Zhang, X., Hu, B., Ma, X., Moore, P., & Chen, J. (2014). Ontology driven decision support for the diagnosis of mild cognitive impairment. Computer Methods and Programs in Biomedicine, 113(3), 781–791. http://doi.org/10.1016/j.cmpb.2013.12.023