scholarly journals Small sample-based disease diagnosis model acquisition in medical human-centered computing

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Xueqing Jia ◽  
Tao Luo ◽  
Sheng Ren ◽  
Kehua Guo ◽  
Fangfang Li
Keyword(s):  
Disease Diagnosis ◽  
Small Sample ◽  
Model Acquisition ◽  
Diagnosis Model

scholarly journals Intelligent Diagnosis Method for New Diseases Based on Fuzzy SVM Incremental Learning

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Shi Song-men
Keyword(s):  
Incremental Learning ◽  
Model Updating ◽  
Disease Diagnosis ◽  
Small Sample ◽  
Support Vector ◽  
Intelligent Diagnosis ◽  
Training Samples ◽  
Diagnosis Method ◽  
Diagnosis Model ◽  
The Impact

The diagnosis of new diseases is a challenging problem. In the early stage of the emergence of new diseases, there are few case samples; this may lead to the low accuracy of intelligent diagnosis. Because of the advantages of support vector machine (SVM) in dealing with small sample problems, it is selected for the intelligent diagnosis method. The standard SVM diagnosis model updating needs to retrain all samples. It costs huge storage and calculation costs and is difficult to adapt to the changing reality. In order to solve this problem, this paper proposes a new disease diagnosis method based on Fuzzy SVM incremental learning. According to SVM theory, the support vector set and boundary sample set related to the SVM diagnosis model are extracted. Only these sample sets are considered in incremental learning to ensure the accuracy and reduce the cost of calculation and storage. To reduce the impact of noise points caused by the reduction of training samples, FSVM is used to update the diagnosis model, and the generalization is improved. The simulation results on the banana dataset show that the proposed method can improve the classification accuracy from 86.4% to 90.4%. Finally, the method is applied in COVID-19’s diagnostic. The diagnostic accuracy reaches 98.2% as the traditional SVM only gets 84%. With the increase of the number of case samples, the model is updated. When the training samples increase to 400, the number of samples participating in training is only 77; the amount of calculation of the updated model is small.

scholarly journals Multi-Layer Perceptron Neural Network Model Development for Chili Pepper Disease Diagnosis Using Filter and Wrapper Feature Selection Methods

2021 ◽  
Vol 11 (5) ◽  
pp. 7714-7719
Author(s):  
S. Nuanmeesri ◽  
W. Sriurai
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Information Gain ◽  
Disease Diagnosis ◽  
Chili Pepper ◽  
Growth Stages ◽  
Multi Layer Perceptron ◽  
Selection Methods ◽  
Diagnosis Model ◽  
Wrapper Feature Selection

The goal of the current study is to develop a diagnosis model for chili pepper disease diagnosis by applying filter and wrapper feature selection methods as well as a Multi-Layer Perceptron Neural Network (MLPNN). The data used for developing the model include 1) types, 2) causative agents, 3) areas of infection, 4) growth stages of infection, 5) conditions, 6) symptoms, and 7) 14 types of chili pepper diseases. These datasets were applied to the 3 feature selection techniques, including information gain, gain ratio, and wrapper. After selecting the key features, the selected datasets were utilized to develop the diagnosis model towards the application of MLPNN. According to the model’s effectiveness evaluation results, estimated by 10-fold cross-validation, it can be seen that the diagnosis model developed by applying the wrapper method along with MLPNN provided the highest level of effectiveness, with an accuracy of 98.91%, precision of 98.92%, and recall of 98.89%. The findings showed that the developed model is applicable.

scholarly journals Predictors of Life Expectancy After an Alzheimer’s Disease Diagnosis in a National Multi-Center Autopsy-Confirmed Sample

2019 ◽  
Vol 34 (6) ◽  
pp. 845-845
Author(s):  
J Schaffert ◽  
C LoBue ◽  
C Presley ◽  
L Hynan ◽  
K Wilmoth ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Life Expectancy ◽  
Psychiatric Symptoms ◽  
Significant Proportion ◽  
Disease Diagnosis ◽  
Small Sample ◽  
Clinical Diagnoses ◽  
Race Ethnicity ◽  
Small Sample Sizes

Abstract Objective Life expectancy varies between 3-12 years following the diagnosis of Alzheimer’s disease (AD) and is an important clinical question for patients and families. Current literature is limited by relatively small sample sizes and a reliance on clinical diagnoses. This study sought to evaluate predictors of AD life expectancy in a large autopsy-confirmed sample. Methods Baseline data from individuals 50 years and older clinically and neuropathologically diagnosed with AD (N=764) were obtained from the National Alzheimer’s Coordinating Center. Life expectancy was calculated in months from AD diagnosis to death. Nineteen variables (demographic, medical/health, disease severity, and psychiatric) obtained at dementia diagnosis were examined. Variables that showed significant differences in life expectancy using t-tests and Pearson correlations (14 of 19) were then entered into a forward multiple regression. Results Seven predictors in the model explained 27% of the variance in life expectancy (F= 40.7, R-squared= 0.267). Lower MMSE scores (β= 0.339, p < .001), male sex (β= -0.144, p < .001), older age (β= -0.130, p < .001), non-Hispanic Caucasian race/ethnicity (β= 0.115, p < .001), greater impairment on the Functional Activities Questionnaire (β= -0.091, p=.042), abnormal neurological/physical exam (β= -0.083, p=.011), and higher Neuropsychiatric Inventory Questionnaire total scores (β= -0.079, p=.016) predicted shorter life expectancy. Conclusions Global cognitive impairment, sex, age, race/ethnicity, functional impairment, abnormal neurological exam findings, and psychiatric symptoms explain a significant proportion of life expectancy following an AD diagnosis. Future studies should explore the relationship between life expectancy, specific neurological abnormalities, and psychiatric symptoms. These 7 predictors could potentially be used to predict life expectancy in individuals diagnosed with AD.

IoT with Cloud Based End to End Secured Disease Diagnosis Model using Light Weight Cryptography and Gradient Boosting Tree

2020 ◽  
Vol 13 ◽  
Author(s):  
K. Shankar
Keyword(s):  
Internet Of Things ◽  
Disease Diagnosis ◽  
Classification Model ◽  
Gradient Boosting ◽  
The Internet ◽  
Pima Indians ◽  
Healthcare Applications ◽  
Diagnosis Model ◽  
Consistent Performance ◽  
The Internet Of Things

Background: With the evolution of the Internet of Things (IoT) technology and connected devices employed in the medicinal domain, the different characteristics of the online healthcare applications become advantageous. Aim: The objective of this paper is to present an IoT and cloud-based secured disease diagnosis model. At present, various e-healthcare applications with the use of the Internet of Things (IoT) offers diverse dimensions and services online. Method: In this paper, an efficient IoT and cloud-based secured classification model are proposed for disease diagnosis. It is used to avail efficient and secured services to the people globally over online healthcare applications. The presented model includes an effective gradient boosting tree (GBT) based data classification and lightweight cryptographic technique named rectangle. The presented GBT–R model offers a better diagnosis in a secure way. Results: It is validated using the Pima Indians diabetes data, and extensive simulation takes place to verify the consistent performance of the employed GBT-R model. Conclusion: The experimental outcome strongly suggested that the presented model shows maximum performance with an accuracy of 94.92.

scholarly journals Label-Free Optical Resonator-Based Biosensors

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5901
Author(s):  
Donggee Rho ◽  
Caitlyn Breaux ◽  
Seunghyun Kim
Keyword(s):  
Point Of Care ◽  
Health And Safety ◽  
Disease Diagnosis ◽  
Medical Diagnostics ◽  
Sample Volume ◽  
Small Sample ◽  
Optical Resonator ◽  
Future Research ◽  
Label Free ◽  
Label Free Detection

The demand for biosensor technology has grown drastically over the last few decades, mainly in disease diagnosis, drug development, and environmental health and safety. Optical resonator-based biosensors have been widely exploited to achieve highly sensitive, rapid, and label-free detection of biological analytes. The advancements in microfluidic and micro/nanofabrication technologies allow them to be miniaturized and simultaneously detect various analytes in a small sample volume. By virtue of these advantages and advancements, the optical resonator-based biosensor is considered a promising platform not only for general medical diagnostics but also for point-of-care applications. This review aims to provide an overview of recent progresses in label-free optical resonator-based biosensors published mostly over the last 5 years. We categorized them into Fabry-Perot interferometer-based and whispering gallery mode-based biosensors. The principles behind each biosensor are concisely introduced, and recent progresses in configurations, materials, test setup, and light confinement methods are described. Finally, the current challenges and future research topics of the optical resonator-based biosensor are discussed.

scholarly journals Real-world survival outcomes with immune checkpoint inhibitors in large-cell neuroendocrine tumors of lung

2021 ◽  
Vol 9 (2) ◽  
pp. e001999
Author(s):  
Elizabeth Dudnik ◽  
Samuel Kareff ◽  
Mor Moskovitz ◽  
Chul Kim ◽  
Stephen V Liu ◽  
...  
Keyword(s):  
Real World ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Eastern Cooperative Oncology Group ◽  
Large Cell ◽  
Disease Diagnosis ◽  
Small Sample ◽  
Rank Test ◽  
Log Rank Test

BackgroundLittle is known regarding the efficacy of immune checkpoint inhibitors (ICI) in patients with advanced large-cell neuroendocrine lung carcinoma (aLCNEC).Methods125 consecutive patients with aLCNEC were identified in the electronic databases of 4 participating cancer centers. The patients were divided into group A (patients who received ICI, n=41) and group B (patients who did not receive ICI, n=84). Overall survival since advanced disease diagnosis (OS DX) and OS since ICI initiation (OS ICI) were captured.ResultsWith a median follow-up of 11.8 months (mo) (IQR 7.5–17.9) and 6.0mo (IQR 3.1–10.9), 66% and 76% of patients died in groups A and B, respectively. Median OS DX was 12.4mo (95% CI 10.7 to 23.4) and 6.0mo (95% CI 4.7 to 9.4) in groups A and B, respectively (log-rank test, p=0.02). For ICI administration, HR for OS DX was 0.59 (95% CI 0.38 to 0.93, p=0.02—unadjusted), and 0.58 (95% CI 0.34 to 0.98, p=0.04—adjusted for age, Eastern Cooperative Oncology Group (ECOG) performance status (PS), presence of liver metastases and chemotherapy administration). In a propensity score matching analysis (n=74; 37 patients in each group matched for age and ECOG PS), median OS DX was 12.5 mo (95% CI 10.6 to 25.2) and 8.4 mo (95% CI 5.4 to 16.9) in matched groups A and B, respectively (log-rank test, p=0.046). OS ICI for patients receiving ICI as monotherapy (n=36) was 11.0 mo (95% CI 6.1 to 19.4).ConclusionsWith the limitations of retrospective design and small sample size, the results of this real-world cohort analysis suggest a positive impact of ICI on OS in aLCNEC.

Sign in / Sign up

Export Citation Format

Share Document