Rigorous performance assessment of computer-aided medical diagnosis and prognosis systems: a biostatistical perspective on data mining

2020 ◽  
Author(s):  
Marjan Mansourian ◽  
Hamid Reza Marateb ◽  
Mahsa Mansourian ◽  
Mohammad Reza Mohebbian ◽  
Harald Binder ◽  
...  
Keyword(s):  
Data Mining ◽  
Performance Assessment ◽  
Medical Diagnosis ◽  
Diagnosis And Prognosis ◽  
Computer Aided

scholarly journals A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 393
Author(s):  
Mahsa Mansourian ◽  
Sadaf Khademi ◽  
Hamid Reza Marateb
Keyword(s):  
Data Mining ◽  
Mental Disorders ◽  
Neurological Disorders ◽  
External Validation ◽  
Computer Aided Diagnosis ◽  
World Health ◽  
Performance Indices ◽  
Life Years ◽  
Computer Aided ◽  
Aided Diagnosis

The World Health Organization (WHO) suggests that mental disorders, neurological disorders, and suicide are growing causes of morbidity. Depressive disorders, schizophrenia, bipolar disorder, Alzheimer’s disease, and other dementias account for 1.84%, 0.60%, 0.33%, and 1.00% of total Disability Adjusted Life Years (DALYs). Furthermore, suicide, the 15th leading cause of death worldwide, could be linked to mental disorders. More than 68 computer-aided diagnosis (CAD) methods published in peer-reviewed journals from 2016 to 2021 were analyzed, among which 75% were published in the year 2018 or later. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was adopted to select the relevant studies. In addition to the gold standard, the sample size, neuroimaging techniques or biomarkers, validation frameworks, the classifiers, and the performance indices were analyzed. We further discussed how various performance indices are essential based on the biostatistical and data mining perspective. Moreover, critical information related to the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines was analyzed. We discussed how balancing the dataset and not using external validation could hinder the generalization of the CAD methods. We provided the list of the critical issues to consider in such studies.

scholarly journals Data Mining Technique to Interpret Lung Nodule for Computer Aided Diagnosis

2011 ◽  
pp. 153-158
Author(s):  
Aswini Kumar Mohanty ◽  
Saroj Kumar Lenka
Keyword(s):  
Data Mining ◽  
Diagnostic Criteria ◽  
Lung Nodule ◽  
Computer Aided Diagnosis ◽  
Image Features ◽  
Data Mining Technique ◽  
Cad Systems ◽  
Computer Aided ◽  
Using Data ◽  
Aided Diagnosis

Diagnostic decision-making in pulmonary medical imaging has been improved by computer-aided diagnosis (CAD) systems, serving as second readers to detect suspicious nodules for diagnosis by a radiologist. Though increasing the accuracy, these CAD systems rarely offer useful descriptions of the suspected nodule or their decision criteria, mainly due to lack of nodule data. In this paper, we present a framework for mapping image features to radiologist-defined diagnostic criteria based on the newly available data). Using data mining, we found promising mappings to clinically relevant, human-interpretable nodule characteristics such as malignancy, margin, spiculation, subtlety, and texture. Bridging the semantic gap between computed image features and radiologist defined diagnostic criteria allows CAD systems to offer not only a second opinion but also decision-support criteria usable by radiologists. Presenting transparent decisions will improve the clinical acceptance of CAD.

scholarly journals Computer-Aided Teaching System Based on Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yonghua Tang ◽  
Qiang Fan ◽  
Peng Liu
Keyword(s):  
Data Mining ◽  
Spectral Clustering ◽  
Traditional Teaching ◽  
Clustering Methods ◽  
Mining System ◽  
Teaching Resources ◽  
Teaching System ◽  
Data Mining Algorithms ◽  
Computer Aided ◽  
Mining Algorithms

The traditional teaching model cannot adapt to the teaching needs of the era of smart teaching. Based on this, this paper combines data mining technology to carry out teaching reforms, constructs a computer-aided system based on data mining, and constructs teaching system functions based on actual conditions. The constructed system can carry out multisubject teaching. Moreover, this paper uses a data mining system to mine teaching resources and uses spectral clustering methods to integrate multiple teaching resources to improve the practicability of data mining algorithms. In addition, this paper combines digital technology to deal with teaching resources. Finally, after building the system, this paper designs experiments to verify the performance of the system. From the research results, it can be seen that the system constructed in this paper has certain teaching and practical effects, and it can be applied to a larger teaching scope in subsequent research.

The Design and Testing of a New Approach to Computer-aided Differential Diagnosis

1983 ◽  
Vol 22 (03) ◽  
pp. 156-166 ◽  
Author(s):  
Dana Ludwig ◽  
D. Heilbronn
Keyword(s):  
Statistical Models ◽  
Medical Diagnosis ◽  
Logistic Regression Model ◽  
Medical Knowledge ◽  
Bayes Rule ◽  
Causal Network ◽  
New Approach ◽  
Linear Discriminant ◽  
Computer Aided ◽  
Design And Testing

An algorithm is presented for making diagnostic inferences on the basis of a causal network model of medical knowledge. The algorithm is based on Bayes Rule, but is unique in the way that it accounts for the presence of conditional non-independence of observations and for the presence of multiple diseases in the same patient. An evaluation of the system is performed on a database of patients with chest pain. In this evaluation, the diagnostic accuracy of the system is found to be inferior to that of a logistic regression model and comparable to that of a linear discriminant function. In a review of selected cases from this database, the system can be shown to provide inferences that are not possible with other simpler statistical models. The practicality of this and other computer aids to medical diagnosis is discussed.

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