Evolutionary Fuzzy Modeling Human Diagnostic Decisions

Peña-Reyes, Carlos Andrés

doi:10.1196/annals.1310.017

Cited by 16 publications

(16 citation statements)

References 19 publications

(2 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Classifiers and multiple classifier systems were generated by penalized logistic regression (21-23) and fuzzy logic (24,25) modeling techniques. First, an algorithm based on the 29-gene signature was defined following a multiple classifier combination strategy.…”

Section: Statistical Design and Modelingmentioning

confidence: 99%

Development and Clinical Validation of a Blood Test Based on 29-Gene Expression for Early Detection of Colorectal Cancer

Ciarloni¹,

Ehrensberger²,

Imaizumi³

et al. 2016

Clinical Cancer Research

View full text Add to dashboard Cite

Purpose: A blood test for early detection of colorectal cancer is a valuable tool for testing asymptomatic individuals and reducing colorectal cancer-related mortality. The objective of this study was to develop and validate a novel blood test able to differentiate patients with colorectal cancer and adenomatous polyps (AP) from individuals with a negative colonoscopy.Experimental Design: A case-control, multicenter clinical study was designed to collect blood samples from patients referred for colonoscopy or surgery. Predictive algorithms were developed on 75 controls, 61 large AP (LAP) !1 cm, and 45 colorectal cancer cases and independently validated on 74 controls, 42 LAP, and 52 colorectal cancer cases (23 stages I-II) as well as on 245 cases including other colorectal findings and diseases other than colorectal cancer. The test is based on a 29-gene panel expressed in peripheral blood mononuclear cells alone or in combination with established plasma tumor markers.Results: The 29-gene algorithm detected colorectal cancer and LAP with a sensitivity of 79.5% and 55.4%, respectively, with 90.0% specificity. Combination with the protein tumor markers carcinoembryonic antigen (CEA) and CYFRA21-2 resulted in a specificity increase (92.2%) with a sensitivity for colorectal cancer and LAP detection of 78.1% and 52.3%, respectively.Conclusions: We report the validation of a novel blood test, Colox Ò , for the detection of colorectal cancer and LAP based on a 29-gene panel and the CEA and CYFRA21-1 plasma biomarkers. The performance and convenience of this routine blood test provide physicians a useful tool to test average-risk individuals unwilling to undergo upfront colonoscopy.

show abstract

Section: Statistical Design and Modelingmentioning

confidence: 99%

Development and Clinical Validation of a Blood Test Based on 29-Gene Expression for Early Detection of Colorectal Cancer

Ciarloni¹,

Ehrensberger²,

Imaizumi³

et al. 2016

Clinical Cancer Research

View full text Add to dashboard Cite

show abstract

“…Several other studies investigated different technologies for the assessment of CHD, including logistic regression [17], association rules [7], [15], fuzzy modeling [22], [23], neural networks [24], and other.…”

Section: Assessment Of the Risk Factors Of Coronary Heart Events Basementioning

confidence: 99%

Assessment of the Risk Factors of Coronary Heart Events Based on Data Mining With Decision Trees

Karaolis¹,

Moutiris²,

Hadjipanayi³

et al. 2010

IEEE Trans. Inform. Technol. Biomed.

166

View full text Add to dashboard Cite

Coronary heart disease (CHD) is one of the major causes of disability in adults as well as one of the main causes of death in the developed countries. Although significant progress has been made in the diagnosis and treatment of CHD, further investigation is still needed. The objective of this study was to develop a data-mining system for the assessment of heart event-related risk factors targeting in the reduction of CHD events. The risk factors investigated were: 1) before the event: a) nonmodifiable-age, sex, and family history for premature CHD, b) modifiable-smoking before the event, history of hypertension, and history of diabetes; and 2) after the event: modifiable-smoking after the event, systolic blood pressure, diastolic blood pressure, total cholesterol, high-density lipoprotein, low-density lipoprotein, triglycerides, and glucose. The events investigated were: myocardial infarction (MI), percutaneous coronary intervention (PCI), and coronary artery bypass graft surgery (CABG). A total of 528 cases were collected from the Paphos district in Cyprus, most of them with more than one event. Data-mining analysis was carried out using the C4.5 decision tree algorithm for the aforementioned three events using five different splitting criteria. The most important risk factors, as extracted from the classification rules analysis were: 1) for MI, age, smoking, and history of hypertension; 2) for PCI, family history, history of hypertension, and history of diabetes; and 3) for CABG, age, history of hypertension, and smoking. Most of these risk factors were also extracted by other investigators. The highest percentages of correct classifications achieved were 66%, 75%, and 75% for the MI, PCI, and CABG models, respectively. It is anticipated that data mining could help in the identification of high and low risk subgroups of subjects, a decisive factor for the selection of therapy, i.e., medical or surgical. However, further investigation with larger datasets is still needed.

show abstract

“…[4896] Fuzzy cluster can be used to accurately characterize gliomas into high grade and low grade ( P value <0.001) and determine glioma volume before surgery. [276577] The diagnosis of grade of gliomas, based on interpretation of MRI scans using fuzzy logic, has been shown to have an accuracy of 86.4%. [107] It is also able to detect brain tumor response to radiation therapy by measuring changes in volume after treatment.…”

Section: Neuroradiologymentioning

confidence: 99%

“…[103104] Additionally, it can differentiate tumor tissue from surrounding edema and hemorrhage. [7779] Apart from the tumor volume, fuzzy clustering can detect CSF and gray and white matter volume changes in children with hydrocephalus. [17]…”

Section: Neuroradiologymentioning

confidence: 99%

Fuzzy logic: A "simple" solution for complexities in neurosciences?

Godil¹,

Shamim²,

Enam³

et al. 2011

Surg Neurol Int

View full text Add to dashboard Cite

Background:Fuzzy logic is a multi-valued logic which is similar to human thinking and interpretation. It has the potential of combining human heuristics into computer-assisted decision making, which is applicable to individual patients as it takes into account all the factors and complexities of individuals. Fuzzy logic has been applied in all disciplines of medicine in some form and recently its applicability in neurosciences has also gained momentum.Methods:This review focuses on the use of this concept in various branches of neurosciences including basic neuroscience, neurology, neurosurgery, psychiatry and psychology.Results:The applicability of fuzzy logic is not limited to research related to neuroanatomy, imaging nerve fibers and understanding neurophysiology, but it is also a sensitive and specific tool for interpretation of EEGs, EMGs and MRIs and an effective controller device in intensive care units. It has been used for risk stratification of stroke, diagnosis of different psychiatric illnesses and even planning neurosurgical procedures.Conclusions:In the future, fuzzy logic has the potential of becoming the basis of all clinical decision making and our understanding of neurosciences.

show abstract

Evolutionary Fuzzy Modeling Human Diagnostic Decisions

Cited by 16 publications

References 19 publications

Development and Clinical Validation of a Blood Test Based on 29-Gene Expression for Early Detection of Colorectal Cancer

Development and Clinical Validation of a Blood Test Based on 29-Gene Expression for Early Detection of Colorectal Cancer

Assessment of the Risk Factors of Coronary Heart Events Based on Data Mining With Decision Trees

Fuzzy logic: A "simple" solution for complexities in neurosciences?

Contact Info

Product

Resources

About