Nonparametric Decision Support Systems in Medical Diagnosis

2011 ◽  
pp. 562-579
Author(s):  
Steven Walczak ◽  
Bradley B. Brimhall ◽  
Jerry B. Lefkowitz
Keyword(s):  
Medical Diagnosis ◽  
Laboratory Tests ◽  
Clinical Laboratory ◽  
Predictive Performance ◽  
Diagnostic Model ◽  
Medical Problems ◽  
Diagnostic Models ◽  
Trained Neural Network ◽  
Diagnostic Capabilities ◽  
D Dimer

Patients face a multitude of diseases, trauma, and related medical problems that are difficult to diagnose and have large treatment and diagnostic direct costs, including pulmonary embolism (PE), which has mortality rates as high as 10%. Advanced decision-making tools, such as nonparametric neural networks (NN), may improve diagnostic capabilities for these problematic medical conditions. The research develops a backpropagation trained neural network diagnostic model to predict the occurrence of PE. Laboratory database values for 292 patients who were determined to be at risk for PE, with almost 15% suffering a confirmed PE, were collected and used to evaluate various NN models’ performances. Results indicate that using NN diagnostic models enables the leveraging of knowledge gained from standard clinical laboratory tests, specifically the d-dimer assay and reactive glucose, significantly improving overall positive predictive value, compared to using either test in isolation, and also increasing negative predictive performance.

Nonparametric Decision Support Systems in Medical Diagnosis

2011 ◽  
pp. 1483-1500
Author(s):  
Steven Walczak ◽  
Bradley B. Brimhall ◽  
Jerry B. Lefkowitz
Keyword(s):  
Medical Diagnosis ◽  
Laboratory Tests ◽  
Clinical Laboratory ◽  
Predictive Performance ◽  
Diagnostic Model ◽  
Medical Problems ◽  
Diagnostic Models ◽  
Trained Neural Network ◽  
Diagnostic Capabilities ◽  
D Dimer

Patients face a multitude of diseases, trauma, and related medical problems that are difficult to diagnose and have large treatment and diagnostic direct costs, including pulmonary embolism (PE), which has mortality rates as high as 10%. Advanced decision-making tools, such as nonparametric neural networks (NN), may improve diagnostic capabilities for these problematic medical conditions. The research develops a backpropagation trained neural network diagnostic model to predict the occurrence of PE. Laboratory database values for 292 patients who were determined to be at risk for PE, with almost 15% suffering a confirmed PE, were collected and used to evaluate various NN models’ performances. Results indicate that using NN diagnostic models enables the leveraging of knowledge gained from standard clinical laboratory tests, specifically the d-dimer assay and reactive glucose, significantly improving overall positive predictive value, compared to using either test in isolation, and also increasing negative predictive performance.

Diagnostic Cost Reduction Using Artificial Neural Networks

2011 ◽  
pp. 1812-1830
Author(s):  
Steven Walczak ◽  
Bradley B. Brimhall ◽  
Jerry B. Lefkowitz
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Clinical Laboratory ◽  
Predictive Performance ◽  
Diagnostic Model ◽  
Medical Problems ◽  
Ann Models ◽  
Artificial Neural ◽  
Diagnostic Capabilities ◽  
Performance Results

Patients face a multitude of diseases, trauma, and related medical problems that are difficult and costly to diagnose with respect to direct costs, including pulmonary embolism (PE). Advanced decision-making tools such as artificial neural networks (ANNs) improve diagnostic capabilities for these problematic medical conditions. The research in this chapter develops a backpropagation trained ANN diagnostic model to predict the occurrence of PE. Laboratory database values for 292 patients who were determined to be at risk for a PE, with 15% suffering a confirmed PE, are collected and used to evaluate various ANN models’ performance. Results indicate that using ANN diagnostic models enables the leveraging of knowledge gained from standard clinical laboratory tests, significantly improving both overall positive predictive and negative predictive performance.

Diagnostic Cost Reduction Using Artificial Neural Networks

2011 ◽  
pp. 108-130
Author(s):  
Steven Walczak ◽  
Bradley B. Brimhall ◽  
Jerry B. Lefkowitz
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Clinical Laboratory ◽  
Predictive Performance ◽  
Diagnostic Model ◽  
Medical Problems ◽  
Ann Models ◽  
Artificial Neural ◽  
Diagnostic Capabilities ◽  
Performance Results

Patients face a multitude of diseases, trauma, and related medical problems that are difficult and costly to diagnose with respect to direct costs, including pulmonary embolism (PE). Advanced decision-making tools such as artificial neural networks (ANNs) improve diagnostic capabilities for these problematic medical conditions. The research in this chapter develops a backpropagation trained ANN diagnostic model to predict the occurrence of PE. Laboratory database values for 292 patients who were determined to be at risk for a PE, with 15% suffering a confirmed PE, are collected and used to evaluate various ANN models’ performance. Results indicate that using ANN diagnostic models enables the leveraging of knowledge gained from standard clinical laboratory tests, significantly improving both overall positive predictive and negative predictive performance.

The clinical chemistry and immunology of long-duration space missions

1993 ◽  
Vol 39 (1) ◽  
pp. 22-36 ◽  
Author(s):  
A H Wu ◽  
G R Taylor ◽  
G A Graham ◽  
B A McKinley
Keyword(s):  
Medical Care ◽  
Clinical Laboratory ◽  
Clinical Chemistry ◽  
Space Missions ◽  
Laboratory Diagnostics ◽  
Medical Problems ◽  
Long Duration ◽  
Physiological Adaptations ◽  
Diagnostic Capabilities ◽  
Cellular Immune

Abstract Clinical laboratory diagnostic capabilities are needed to guide health and medical care of astronauts during long-duration space missions. Clinical laboratory diagnostics, as defined for medical care on Earth, offers a model for space capabilities. Interpretation of laboratory results for health and medical care of humans in space requires knowledge of specific physiological adaptations that occur, primarily because of the absence of gravity, and how these adaptations affect reference values. Limited data from American and Russian missions have indicated shifts of intra- and extracellular fluids and electrolytes, changes in hormone concentrations related to fluid shifts and stresses of the missions, reductions in bone and muscle mass, and a blunting of the cellular immune response. These changes could increase susceptibility to space-related illness or injury during a mission and after return to Earth. We review physiological adaptations and the risk of medical problems that occur during space missions. We describe the need for laboratory diagnostics as a part of health and medical care in space, and how this capability might be delivered.

ERROR LOCALIZATION DIAGNOSTIC MODEL OPTIMIZATION IN DIGITAL STATE MACHINES NETWORKS

2019 ◽  
Author(s):  
Irina Bystrova ◽  
E. Danil'chuk ◽  
Boris Podkopaev
Keyword(s):  
Single Component ◽  
Diagnostic Model ◽  
State Machines ◽  
Model Optimization ◽  
Error Localization ◽  
Diagnostic Models

The problem of constructing a diagnostic model for a network S consisting of a number of digital automata is considered, provided that the diagnostic models of all network components are known. It is assumed that these models are given by systems of logical equations, and the errors to be detected are localized in any but a single component of the network.

scholarly journals Quality control stress test for deep learning-based diagnostic model in digital pathology

2021 ◽  
Author(s):  
Birgid Schömig-Markiefka ◽  
Alexey Pryalukhin ◽  
Wolfgang Hulla ◽  
Andrey Bychkov ◽  
Junya Fukuoka ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cancer Detection ◽  
Stress Testing ◽  
Computational Analysis ◽  
Stress Test ◽  
Digital Pathology ◽  
Model Performance ◽  
Diagnostic Model ◽  
Model Accuracy ◽  
Diagnostic Models

AbstractDigital pathology provides a possibility for computational analysis of histological slides and automatization of routine pathological tasks. Histological slides are very heterogeneous concerning staining, sections’ thickness, and artifacts arising during tissue processing, cutting, staining, and digitization. In this study, we digitally reproduce major types of artifacts. Using six datasets from four different institutions digitized by different scanner systems, we systematically explore artifacts’ influence on the accuracy of the pre-trained, validated, deep learning-based model for prostate cancer detection in histological slides. We provide evidence that any histological artifact dependent on severity can lead to a substantial loss in model performance. Strategies for the prevention of diagnostic model accuracy losses in the context of artifacts are warranted. Stress-testing of diagnostic models using synthetically generated artifacts might be an essential step during clinical validation of deep learning-based algorithms.

scholarly journals COVID-19 infection: epidemiological, clinical, and radiological expression among adult population

2020 ◽  
Vol 51 (1) ◽  
Author(s):  
Eman Ragab ◽  
Asrar Helal Mahrous ◽  
Ghadeer Maher El Sheikh
Keyword(s):  
Serum Ferritin ◽  
Clinical Laboratory ◽  
Adult Population ◽  
Significant Risk ◽  
High Serum ◽  
Male Gender ◽  
Lower Sensitivity ◽  
Significant Difference ◽  
D Dimer

Abstract Background High-resolution computed tomography (HRCT) has proved to be an important diagnostic tool throughout the COVID-19 pandemic outbreaks. Increasing number of the infected personnel and shortage of real-time transcriptase polymerase chain reaction (RT-PCR) as well as its lower sensitivity made the CT a backbone in diagnosis, assessment of severity, and follow-up of the cases. Results Two hundred forty patients were evaluated retrospectively for clinical, laboratory, and radiological expression in COVID-19 infection. One hundred eighty-six non-severe cases with home isolation and outpatient treatment and 54 severe cases needed hospitalization and oxygen support. Significant difference between both groups was encountered regarding the age, male gender, > 38° fever, dyspnea, chest pain, hypertension, ≤ 93 oxygen saturation, intensive care unit (ICU) admission, elevated D-dimer, high serum ferritin and troponin levels, and high CT-severity score (CT-SS) of the severe group. CT-SS showed a negative correlation with O2 saturation and patients’ outcome (r − 0.73/p 0.001 and r − 0.56/p 0.001, respectively). Bilateral peripherally distributed ground glass opacities (GGOs) were the commonest imaging feature similar to the literature. Conclusion Older age, male gender, smoking, hypertension, low O2 saturation, increased CT score, high serum ferritin, and high D-dimer level are the most significant risk factors for severe COVID-19 pneumonia. Follow-up of the recovered severe cases is recommended to depict possible post COVID-19 lung fibrosis.

CAT SCRATCH SYNDROME

PEDIATRICS ◽  
1952 ◽  
Vol 10 (3) ◽  
pp. 311-318
Author(s):  
WILLIAM J. WATERS ◽  
SEYMOUR S. KALTER ◽  
JOHN T. PRIOR
Keyword(s):  
Skin Test ◽  
Laboratory Tests ◽  
Complement Fixation Test ◽  
Complement Fixation ◽  
Clinical Laboratory ◽  
Clinical Course ◽  
Diagnostic Value ◽  
Clinical Findings ◽  
Pathologic Findings ◽  
History Of

The clinical, laboratory and pathologic findings of a series of cases of cat scratch syndrome have been reviewed. In spite of a variable clinical course, certain features associated with a selected group of laboratory tests appear to be constant enough to be of diagnostic value. A history of contact with a cat and/or scratch which is usually associated with a peripheral skin lesion, lack of lymphangitis, presence of regional lymphadenopathy with tenderness to palpation are the most constant clinical findings. Fever, so frequently emphasized as a characteristic clinical sign, may be extremely variable in type and duration or entirely absent. A skin test with cat scratch antigen has been positive in all cases. Lacking this antigen, a negative Frei skin test in conjunction with a positive complement fixation test (Lygranum C. F.) is suggestive evidence for the diagnosis. With positive evidence from the above data, biopsy of an affected gland with its relatively nonspecific pathologic picture is not considered essential for the establishment of the diagnosis of cat scratch syndrome.

scholarly journals Effect of Bromelain on Clinical Laboratory Tests after Oral Administration

1971 ◽  
Vol 12 (6) ◽  
pp. 517-527
Author(s):  
R.M. HEINICKE ◽  
T. ITO ◽  
L. MCCARTHY ◽  
M. YOKOYAMA
Keyword(s):  
Oral Administration ◽  
Laboratory Tests ◽  
Clinical Laboratory
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