scholarly journals Prognosis Patients with COVID-19 using Deep Learning

2021 ◽  
Author(s):  
Jose Luis Guadiana Alvare ◽  
Ruben Morales-Menendez ◽  
Fida Hussain ◽  
Etna Rojas Flores ◽  
Arturo García Zendejas ◽  
...  
Keyword(s):  
Deep Learning ◽  
Risk Prediction ◽  
Mortality Risk ◽  
Research Work ◽  
Area Under The Curve ◽  
Sampling Technique ◽  
Front Line ◽  
Maximum Probability ◽  
Data Set ◽  
The Everyday

Abstract Background: Prognostics study the prediction of an event before it happens, to enable critical decision making to be more efficient. The prognostics are very useful for front line physicians to predict how a disease may affect a patient and react accordingly to save the patients’ lives. The coronavirus (COVID-19) is novel and not enough knowledge about the virus’ behaviour and Key performance indicators (KPIs) to assess the mortality risk prediction. However, using a lot of complex and expensive medical biomarkers could be impossible for many low-budget hospitals. This motivates the development of a prediction model that not only maximizes performance but does so using the least number of biomarkers possible. Methods: For the mortality risk prediction, this research work proposes aCOVID-19 mortality risk calculator based on a Deep Learning (DL) model, and based on a data set provided by the HM Hospitals from Madrid, Spain. A pre-processing strategy for unbalanced classes and feature selection is proposed. Results: The DL model is tested, and the following results are achieved include area under the curve (AUC) 0.93, F2 score 0.93, recall 1.00, accuracy, 0.95, precision 0.91, specificity 0.9279 and maximum probability of correct decision(MPCD) 0.93. Conclusion: The MPCD score shows that the proposed DL outperforms on the everyday set when evaluating even with an over-sampling technique. The benefits of imputating unavailable biomarker data are also evaluated. The results are compared against a random forest (RF) algorithm and the newly proposed methods. The results show that the proposed method is significantly best for the risk prediction of the patients with COVID-19.

P3430Classification of pronounced subclinical atherosclerosis: deep learning approach of carotid intima media ultrasound images is superior to clinical risk factors

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
E Nyman ◽  
M Karlsson ◽  
U Naslund ◽  
C Gronlund
Keyword(s):  
Risk Factors ◽  
Deep Learning ◽  
Subclinical Atherosclerosis ◽  
Ultrasound Image ◽  
Area Under The Curve ◽  
Image Texture ◽  
Learning Approach ◽  
Data Set ◽  
Vascular Age ◽  
Traditional Risk Factors

Abstract Background Carotid ultrasound measurements of subclinical atherosclerosis is extensively used in the research field of cardiovascular disease. Increased intima media thickness (IMT) and plaque detection have predictive value for cardiovascular events when added to traditional risk factors. However, among studies different protocols for measuring IMT (projections, mean or max values and sites) are used and methodological difficulties of plaque detection, together result in conflicting results. Recently, Deep Learning image driven classification methods, has been successfully applied in several medical imaging applications. Here we hypothesize that ultrasound image texture of the intima media complex accurately reflects the disease burden without the need to measure IMT values or detect plaques. Purpose To evaluate classification accuracy of ultrasound based deep learning approach of the intima media complex image compared to traditional risk factors for participants with no vs pronounced subclinical atherosclerosis. Methods Subjects from the VIPVIZA study (Visualization of asymptomatic atherosclerotic disease for optimum cardiovascular prevention, n: 3532, 40, 50 and 60 year old, 53% women) were selected for analysis. Bilateral carotid ultrasound examinations were performed according to a standardized protocol. Subjects were categorized in two groups as 1) pronounced subclinical atherosclerosis (n: 401) – bilateral plaques and estimated vascular age 10 years older, or 2) No subclinical atherosclerosis (n: 592) – no plaques and estimated ordinary vascular age. Traditional risk factors for the participants were estimated by the SCORE risk chart. A 1-cm wide region of the distal common carotid artery intima media complex was automatically segmented from the original B-mode images. The images were fed to a Deep Learning model, convolution neural network (CNN), trained using transfer learning model with 60% training data set and 40% evaluation data set. Classification performance was quantified using accuracy of ROC analysis. Results The mean age was 58 and 56 years in groups 1 and 2, respectively (with 43% and 56% women, respectively). The mean SCORE was 1.74 in group 1 and 1.09 in group 2. Classification based on SCORE had an area under the curve of 0.69 with an accuracy of 38%. The Deep learning approach had an area under the curve of 0.89 with an accuracy of 78%. Intima media image based classification Conclusion The results shows that ultrasound image texture of the intima media with Deep Learning approach can be used to detect pronounced disease without explicit measurement of IMT values or detection of plaques. With hard end-points, the approach could be used for risk stratification of subclinical atherosclerosis. Acknowledgement/Funding Västerbotten County Council, Swedish Research Council, Heart and Lung Foundation, Carl Bennet Ltd, Sweden.

scholarly journals Marrying Medical Domain Knowledge With Deep Learning on Electronic Health Records: A Deep Visual Analytics Approach (Preprint)

2020 ◽  
Author(s):  
Rui Li ◽  
Changchang Yin ◽  
Samuel Yang ◽  
Buyue Qian ◽  
Ping Zhang
Keyword(s):  
Heart Failure ◽  
Deep Learning ◽  
Risk Prediction ◽  
Visual Analytics ◽  
Domain Knowledge ◽  
Knowledge Graph ◽  
Data Set ◽  
Medical Domain ◽  
Clinical Risks

BACKGROUND Deep learning models have attracted significant interest from health care researchers during the last few decades. There have been many studies that apply deep learning to medical applications and achieve promising results. However, there are three limitations to the existing models: (1) most clinicians are unable to interpret the results from the existing models, (2) existing models cannot incorporate complicated medical domain knowledge (eg, a disease causes another disease), and (3) most existing models lack visual exploration and interaction. Both the electronic health record (EHR) data set and the deep model results are complex and abstract, which impedes clinicians from exploring and communicating with the model directly. OBJECTIVE The objective of this study is to develop an interpretable and accurate risk prediction model as well as an interactive clinical prediction system to support EHR data exploration, knowledge graph demonstration, and model interpretation. METHODS A domain-knowledge–guided recurrent neural network (DG-RNN) model is proposed to predict clinical risks. The model takes medical event sequences as input and incorporates medical domain knowledge by attending to a subgraph of the whole medical knowledge graph. A global pooling operation and a fully connected layer are used to output the clinical outcomes. The middle results and the parameters of the fully connected layer are helpful in identifying which medical events cause clinical risks. DG-Viz is also designed to support EHR data exploration, knowledge graph demonstration, and model interpretation. RESULTS We conducted both risk prediction experiments and a case study on a real-world data set. A total of 554 patients with heart failure and 1662 control patients without heart failure were selected from the data set. The experimental results show that the proposed DG-RNN outperforms the state-of-the-art approaches by approximately 1.5%. The case study demonstrates how our medical physician collaborator can effectively explore the data and interpret the prediction results using DG-Viz. CONCLUSIONS In this study, we present DG-Viz, an interactive clinical prediction system, which brings together the power of deep learning (ie, a DG-RNN–based model) and visual analytics to predict clinical risks and visually interpret the EHR prediction results. Experimental results and a case study on heart failure risk prediction tasks demonstrate the effectiveness and usefulness of the DG-Viz system. This study will pave the way for interactive, interpretable, and accurate clinical risk predictions.

scholarly journals Development and Validation of a Multiparameterized Artificial Neural Network for Prostate Cancer Risk Prediction and Stratification

2018 ◽  
pp. 1-10 ◽  
Author(s):  
David A. Roffman ◽  
Gregory R. Hart ◽  
Michael S. Leapman ◽  
James B. Yu ◽  
Fangliang L. Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Prostate Cancer ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Prostate Cancer Risk ◽  
Area Under The Curve ◽  
Personal Health Information ◽  
Personal Health ◽  
Training Set ◽  
Data Set

Purpose To develop and validate a multiparameterized artificial neural network (ANN) on the basis of personal health information for prostate cancer risk prediction and stratification. Methods The 1997 to 2015 National Health Interview Survey adult survey data were used to train and validate a multiparameterized ANN, with parameters including age, body mass index, diabetes status, smoking status, emphysema, asthma, race, ethnicity, hypertension, heart disease, exercise habits, and history of stroke. We developed a training set of patients ≥ 45 years of age with a first primary prostate cancer diagnosed within 4 years of the survey. After training, the sensitivity and specificity were obtained as functions of the cutoff values of the continuous output of the ANN. We also evaluated the ANN with the 2016 data set for cancer risk stratification. Results We identified 1,672 patients with prostate cancer and 100,033 respondents without cancer in the 1997 to 2015 data sets. The training set had a sensitivity of 21.5% (95% CI, 19.2% to 23.9%), specificity of 91% (95% CI, 90.8% to 91.2%), area under the curve of 0.73 (95% CI, 0.71 to 0.75), and positive predictive value of 28.5% (95% CI, 25.5% to 31.5%). The validation set had a sensitivity of 23.2% (95% CI, 19.5% to 26.9%), specificity of 89.4% (95% CI, 89% to 89.7%), area under the curve of 0.72 (95% CI, 0.70 to 0.75), and positive predictive value of 26.5% (95% CI, 22.4% to 30.6%). For the 2016 data set, the ANN classified all 13,031 patients into low-, medium-, and high-risk subgroups and identified 5% of the cancer population as high risk. Conclusion A multiparameterized ANN that is based on personal health information could be used for prostate cancer risk prediction with high specificity and low sensitivity. The ANN can further stratify the population into three subgroups that may be helpful in refining prescreening estimates of cancer risk.

scholarly journals Classifiers ensemble and synthetic minority oversampling techniques for academic performance prediction

2019 ◽  
Vol 8 (3) ◽  
pp. 122
Author(s):  
Abdulazeez Yusuf ◽  
Ayuba John
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Research Work ◽  
Educational Data Mining ◽  
Sampling Technique ◽  
Ensemble Model ◽  
Data Set ◽  
Grade Point ◽  
Matriculation Examination ◽  
The Individual

The increasing need for data driven decision making recently has resulted in the application of data mining in various fields including the educational sector which is referred to as educational data mining. The need for improving the performance of data mining models has also been identified as a gap for future researcher. In Nigeria, higher educational institutions collect various students’ data, but these data are rarely used in any decision or policy making to improve the academic performance of students. This research work, attempts to improve the performance of data mining models for predicting students’ academic performance using stacking classifiers ensemble and synthetic minority over-sampling techniques. The research was conducted by adopting and evaluating the performance of J48, IBK and SMO classifiers. The individual classifiers models, standard stacking classifier ensemble model and stacking classifiers ensemble model were trained and tested on 206 students’ data set from the faculty of science federal university Dutse. Students’ specific previous academic performance records at Unified Tertiary Matriculation Examination, Senior Secondary Certificate Examination and first year Cumulative Grade Point Average of students are used as data inputs in WEKA 3.9.1 data mining tool to predict students’ graduation classes of degrees at undergraduate level. The result shows that application of synthetic minority over-sampling technique for class balancing improves all the various models performance with the proposed modified stacking classifiers ensemble model outperforming the various classifiers models in both performance accuracy and RSME values making it the best model.<strong></strong>

scholarly journals A transferrable and interpretable multiple instance learning model for microsatellite instability prediction based on histopathology images

2020 ◽  
Author(s):  
Rui Cao ◽  
Fan Yang ◽  
Si-Cong Ma ◽  
Li Liu ◽  
Yan Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Microsatellite Instability ◽  
Transfer Learning ◽  
Area Under The Curve ◽  
Learning Model ◽  
Multiple Instance Learning ◽  
The Cancer Genome Atlas ◽  
Data Set ◽  
Histopathology Images ◽  
Deep Learning Model

ABSTRACTBackgroundMicrosatellite instability (MSI) is a negative prognostic factor for colorectal cancer (CRC) and can be used as a predictor of success for immunotherapy in pan-cancer. However, current MSI identification methods are not available for all patients. We propose an ensemble multiple instance learning (MIL)-based deep learning model to predict MSI status directly from histopathology images.DesignTwo cohorts of patients were collected, including 429 from The Cancer Genome Atlas (TCGA-COAD) and 785 from a self-collected Asian data set (Asian-CRC). The initial model was developed and validated in TCGA-COAD, and then generalized in Asian-CRC through transfer learning. The pathological signatures extracted from the model are associated with genotypes for model interpretation.ResultsA model called Ensembled Patch Likelihood Aggregation (EPLA) was developed in the TCGA-COAD training set based on two consecutive stages: patch-level prediction and WSI-level prediction. The EPLA model achieved an area-under-the -curve (AUC) of 0.8848 in the TCGA-COAD test set, which outperformed the state-of-the-art approach, and an AUC of 0.8504 in the Asian-CRC after transfer learning. Furthermore, the five pathological imaging signatures identified using the model are associated with genomic and transcriptomic profiles, which makes the MIL model interpretable. Results show that our model recognizes pathological signatures related to mutation burden, DNA repair pathways, and immunity.ConclusionOur MIL-based deep learning model can effectively predict MSI from histopathology images and are transferable to a new patient cohort. The interpretability of our model by association with genomic and transcriptomic biomarkers lays the foundation for prospective clinical research.

Early prediction and analysis of corona pandemic outbreak using deep learning technique

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Veerraju Gampala ◽  
Praful Vijay Nandankar ◽  
M. Kathiravan ◽  
S. Karunakaran ◽  
Arun Reddy Nalla ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Research Work ◽  
Activation Function ◽  
Learning Model ◽  
Dense Layer ◽  
Data Set ◽  
Content Type ◽  
The Government ◽  
Deep Learning Model

Purpose The purpose of this paper is to analyze and build a deep learning model that can furnish statistics of COVID-19 and is able to forecast pandemic outbreak using Kaggle open research COVID-19 data set. As COVID-19 has an up-to-date data collection from the government, deep learning techniques can be used to predict future outbreak of coronavirus. The existing long short-term memory (LSTM) model is fine-tuned to forecast the outbreak of COVID-19 with better accuracy, and an empirical data exploration with advanced picturing has been made to comprehend the outbreak of coronavirus. Design/methodology/approach This research work presents a fine-tuned LSTM deep learning model using three hidden layers, 200 LSTM unit cells, one activation function ReLu, Adam optimizer, loss function is mean square error, the number of epochs 200 and finally one dense layer to predict one value each time. Findings LSTM is found to be more effective in forecasting future predictions. Hence, fine-tuned LSTM model predicts accurate results when applied to COVID-19 data set. Originality/value The fine-tuned LSTM model is developed and tested for the first time on COVID-19 data set to forecast outbreak of pandemic according to the authors’ knowledge.

scholarly journals Hyoid Bone Tracking in a Videofluoroscopic Swallowing Study Using a Deep-Learning-Based Segmentation Network

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1147
Author(s):  
Hyun-Il Kim ◽  
Yuna Kim ◽  
Bomin Kim ◽  
Dae Youp Shin ◽  
Seong Jae Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hyoid Bone ◽  
Area Under The Curve ◽  
Learning Model ◽  
Computer Assisted ◽  
Trajectory Prediction ◽  
Data Set ◽  
Improved Performance ◽  
Assisted Analysis ◽  
Deep Learning Model

Kinematic analysis of the hyoid bone in a videofluorosopic swallowing study (VFSS) is important for assessing dysphagia. However, calibrating the hyoid bone movement is time-consuming, and its reliability shows wide variation. Computer-assisted analysis has been studied to improve the efficiency and accuracy of hyoid bone identification and tracking, but its performance is limited. In this study, we aimed to design a robust network that can track hyoid bone movement automatically without human intervention. Using 69,389 frames from 197 VFSS files as the data set, a deep learning model for detection and trajectory prediction was constructed and trained by the BiFPN-U-Net(T) network. The present model showed improved performance when compared with the previous models: an area under the curve (AUC) of 0.998 for pixelwise accuracy, an accuracy of object detection of 99.5%, and a Dice similarity of 90.9%. The bounding box detection performance for the hyoid bone and reference objects was superior to that of other models, with a mean average precision of 95.9%. The estimation of the distance of hyoid bone movement also showed higher accuracy. The deep learning model proposed in this study could be used to detect and track the hyoid bone more efficiently and accurately in VFSS analysis.

scholarly journals Classification of caries in third molars on panoramic radiographs using deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shankeeth Vinayahalingam ◽  
Steven Kempers ◽  
Lorenzo Limon ◽  
Dionne Deibel ◽  
Thomas Maal ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Third Molar ◽  
Area Under The Curve ◽  
Third Molars ◽  
Data Set ◽  
Panoramic Radiographs ◽  
Carious Lesions ◽  
Panoramic Images

AbstractThe objective of this study is to assess the classification accuracy of dental caries on panoramic radiographs using deep-learning algorithms. A convolutional neural network (CNN) was trained on a reference data set consisted of 400 cropped panoramic images in the classification of carious lesions in mandibular and maxillary third molars, based on the CNN MobileNet V2. For this pilot study, the trained MobileNet V2 was applied on a test set consisting of 100 cropped PR(s). The classification accuracy and the area-under-the-curve (AUC) were calculated. The proposed method achieved an accuracy of 0.87, a sensitivity of 0.86, a specificity of 0.88 and an AUC of 0.90 for the classification of carious lesions of third molars on PR(s). A high accuracy was achieved in caries classification in third molars based on the MobileNet V2 algorithm as presented. This is beneficial for the further development of a deep-learning based automated third molar removal assessment in future.

scholarly journals Comparison and Prediction of Hyperthyroidism Accuracy Rate Using Novel Deep Learning Technology and Vivo Monitoring

2021 ◽  
Vol 36 (1) ◽  
pp. 698-703
Author(s):  
Krushitha Reddy ◽  
D. Jenila Rani
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Research Work ◽  
Learning Technology ◽  
Accuracy Rate ◽  
Data Set ◽  
In Vivo Monitoring ◽  
Deep Learning Algorithm

Aim: The aim of this research work is to determine the presence of hyperthyroidism using modern algorithms, and comparing the accuracy rate between deep learning algorithms and vivo monitoring. Materials and methods: Data collection containing ultrasound images from kaggle's website was used in this research. Samples were considered as (N=23) for Deep learning algorithm and (N=23) for vivo monitoring in accordance to total sample size calculated using clinical.com. The accuracy was calculated by using DPLA with a standard data set. Results: Comparison of accuracy rate is done by independent sample test using SPSS software. There is a statistically indifference between Deep learning algorithm and in vivo monitoring. Deep learning algorithm (87.89%) showed better results in comparison to vivo monitoring (83.32%). Conclusion: Deep learning algorithms appear to give better accuracy than in vivo monitoring to predict hyperthyroidism.

scholarly journals Marrying Medical Domain Knowledge With Deep Learning on Electronic Health Records: A Deep Visual Analytics Approach

10.2196/20645 ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. e20645
Author(s):  
Rui Li ◽  
Changchang Yin ◽  
Samuel Yang ◽  
Buyue Qian ◽  
Ping Zhang
Keyword(s):  
Heart Failure ◽  
Deep Learning ◽  
Risk Prediction ◽  
Visual Analytics ◽  
Domain Knowledge ◽  
Knowledge Graph ◽  
Data Set ◽  
Medical Domain ◽  
Clinical Risks

Background Deep learning models have attracted significant interest from health care researchers during the last few decades. There have been many studies that apply deep learning to medical applications and achieve promising results. However, there are three limitations to the existing models: (1) most clinicians are unable to interpret the results from the existing models, (2) existing models cannot incorporate complicated medical domain knowledge (eg, a disease causes another disease), and (3) most existing models lack visual exploration and interaction. Both the electronic health record (EHR) data set and the deep model results are complex and abstract, which impedes clinicians from exploring and communicating with the model directly. Objective The objective of this study is to develop an interpretable and accurate risk prediction model as well as an interactive clinical prediction system to support EHR data exploration, knowledge graph demonstration, and model interpretation. Methods A domain-knowledge–guided recurrent neural network (DG-RNN) model is proposed to predict clinical risks. The model takes medical event sequences as input and incorporates medical domain knowledge by attending to a subgraph of the whole medical knowledge graph. A global pooling operation and a fully connected layer are used to output the clinical outcomes. The middle results and the parameters of the fully connected layer are helpful in identifying which medical events cause clinical risks. DG-Viz is also designed to support EHR data exploration, knowledge graph demonstration, and model interpretation. Results We conducted both risk prediction experiments and a case study on a real-world data set. A total of 554 patients with heart failure and 1662 control patients without heart failure were selected from the data set. The experimental results show that the proposed DG-RNN outperforms the state-of-the-art approaches by approximately 1.5%. The case study demonstrates how our medical physician collaborator can effectively explore the data and interpret the prediction results using DG-Viz. Conclusions In this study, we present DG-Viz, an interactive clinical prediction system, which brings together the power of deep learning (ie, a DG-RNN–based model) and visual analytics to predict clinical risks and visually interpret the EHR prediction results. Experimental results and a case study on heart failure risk prediction tasks demonstrate the effectiveness and usefulness of the DG-Viz system. This study will pave the way for interactive, interpretable, and accurate clinical risk predictions.

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