scholarly journals Predictive Model of Chemotherapy-induced Myelosuppression for Patients With Esophageal Cancer

2021 ◽  
Author(s):  
Ziming Zheng ◽  
Qilin Zhang ◽  
Yong Han ◽  
Tingting Wu ◽  
Yu Zhang
Keyword(s):  
Esophageal Cancer ◽  
Prediction Model ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Influential Factors ◽  
Training Data ◽  
Central China ◽  
Ethical Guidelines ◽  
Validation Data ◽  
Data Set

Abstract Background: The influential factors of chemotherapy-induced myelosuppression in esophageal cancer in central China are unclear. This study aimed to develop a model for prediction of incidence of myelosuppression during chemotherapy among patients with esophageal cancer. Methods: A total of 1446 patients with esophageal cancer who underwent five different chemotherapy regimens between 2013 and 2020 at our institute were randomly assigned in a 7:3 ratio to training and validation data sets. Clinical and drug-related variables were used to develop the prediction model from the training data set by the machine learning method of random forest. Finally, this model were tested in the validation data set.Results: The prediction model were established with 16 indispensable variables selected from 46 variables. The model obtained an area under the receiver-operating characteristic curve of 0.883 and accompanied by prediction accuracy of 80.0%, sensitivity of 77.8% and specificity of 81.8%. Conclusion: This new prediction model showed excellent predictive ability of incidence of myelosuppression in turn providing preventative measures for patients with esophageal cancer during chemotherapy.Trial registration: This study protocol was approved by the institutional ethics board of the Union Hospital of Huazhong University of Science and Technology (retrospectively registered No. 2018S333). This study was performed in accordance with the ethical guidelines of the Declaration of Helsinki and Strengthening the Reporting of Observational Studies in Epidemiology (STROBE).

Diagnostic assessment of a deep learning system for detecting atrial fibrillation in pulse waveforms

Heart ◽  
2018 ◽  
Vol 104 (23) ◽  
pp. 1921-1928 ◽  
Author(s):  
Ming-Zher Poh ◽  
Yukkee Cheung Poh ◽  
Pak-Hei Chan ◽  
Chun-Ka Wong ◽  
Louise Pun ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Deep Learning ◽  
Test Data ◽  
Predictive Value ◽  
Characteristic Curve ◽  
Performance Comparison ◽  
Learning System ◽  
Training Data ◽  
Validation Data ◽  
Data Set

ObjectiveTo evaluate the diagnostic performance of a deep learning system for automated detection of atrial fibrillation (AF) in photoplethysmographic (PPG) pulse waveforms.MethodsWe trained a deep convolutional neural network (DCNN) to detect AF in 17 s PPG waveforms using a training data set of 149 048 PPG waveforms constructed from several publicly available PPG databases. The DCNN was validated using an independent test data set of 3039 smartphone-acquired PPG waveforms from adults at high risk of AF at a general outpatient clinic against ECG tracings reviewed by two cardiologists. Six established AF detectors based on handcrafted features were evaluated on the same test data set for performance comparison.ResultsIn the validation data set (3039 PPG waveforms) consisting of three sequential PPG waveforms from 1013 participants (mean (SD) age, 68.4 (12.2) years; 46.8% men), the prevalence of AF was 2.8%. The area under the receiver operating characteristic curve (AUC) of the DCNN for AF detection was 0.997 (95% CI 0.996 to 0.999) and was significantly higher than all the other AF detectors (AUC range: 0.924–0.985). The sensitivity of the DCNN was 95.2% (95% CI 88.3% to 98.7%), specificity was 99.0% (95% CI 98.6% to 99.3%), positive predictive value (PPV) was 72.7% (95% CI 65.1% to 79.3%) and negative predictive value (NPV) was 99.9% (95% CI 99.7% to 100%) using a single 17 s PPG waveform. Using the three sequential PPG waveforms in combination (<1 min in total), the sensitivity was 100.0% (95% CI 87.7% to 100%), specificity was 99.6% (95% CI 99.0% to 99.9%), PPV was 87.5% (95% CI 72.5% to 94.9%) and NPV was 100% (95% CI 99.4% to 100%).ConclusionsIn this evaluation of PPG waveforms from adults screened for AF in a real-world primary care setting, the DCNN had high sensitivity, specificity, PPV and NPV for detecting AF, outperforming other state-of-the-art methods based on handcrafted features.

scholarly journals Development and Validation of a Score for Screening Suicide of Patients With Neuroendocrine Neoplasms

2021 ◽  
Vol 12 ◽  
Author(s):  
Lili Lu ◽  
Yuru Shang ◽  
Dietmar Zechner ◽  
Christina Susanne Mullins ◽  
Michael Linnebacher ◽  
...  
Keyword(s):  
Life Expectancy ◽  
Test Data ◽  
Characteristic Curve ◽  
Life Quality ◽  
Training Data ◽  
Neuroendocrine Neoplasm ◽  
Neuroendocrine Neoplasms ◽  
Validation Data ◽  
Data Set ◽  
Commit Suicide

Background: If the diagnosis of neuroendocrine neoplasm (NEN) increases the risk of patients to commit suicide has not been investigated so far. Identifying NEN patients at risk to commit suicide is important to increase their life quality and life expectancy.Methods and findings: Cancer cases were extracted from the Surveillance, Epidemiology, and End Results program and were divided into the NEN and the non-NEN cohorts. Subsequently, the NEN patients were randomly split into a training data set and a validation data set. Analyzing the training data set, we developed a score for assessing the risk to commit suicide for patients with NEN. In addition, we validated the score using the validation data set and evaluated, if this score could also be applied to other cancer entities by using the test data set, a non-NEN cohort. The odds ratio (OR) of suicide between NEN and non-NEN patients was determined. Moreover, the performance of a score was evaluated by the receiver operating characteristic curve and the area under the curve (AUC). Compared to non-NEN, NEN significantly increased the risk of suicide to 1.8-fold (NEN vs. non-NEN; OR, 1.832; P &lt; 0.001). In addition, we observed that age, gender, race, marital status, tumor stage, histologic grade, surgery, and chemotherapy were associated with suicide among NEN patients; and a synthesized score based on these factors could significantly distinguish suicide individuals from non-suicide individuals in the training data set (AUC, 0.829; P &lt; 0.001) and in the validation data set (AUC, 0.735; P &lt; 0.001). This score also had a good performance when it was assessed by the test data set (AUC, 0.690; P &lt; 0.001). This demonstrates that the score might also be applicable to other cancer entities.Conclusions: This population-based study suggests that NEN patients have a higher risk of suicide than non-NEN patients. In addition, this study provided a score, which can identify NEN patients at high-risk of committing suicide. Thus, this score in combination with current screening and prevention strategies for suicide may improve life quality and life expectancy of NEN patients.

scholarly journals A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients in China

2021 ◽  
Author(s):  
Dong Wang ◽  
JinBo Li ◽  
Yali Sun ◽  
Xianfei Ding ◽  
Xiaojuan Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Prediction Model ◽  
Secondary Analysis ◽  
Adverse Outcomes ◽  
Training Data ◽  
Medical Systems ◽  
Validation Data ◽  
Data Set ◽  
Icu Patients

Abstract Background: Although numerous studies are conducted every year on how to reduce the fatality rate associated with sepsis, it is still a major challenge faced by patients, clinicians, and medical systems worldwide. Early identification and prediction of patients at risk of sepsis and adverse outcomes associated with sepsis are critical. We aimed to develop an artificial intelligence algorithm that can predict sepsis early.Methods: This was a secondary analysis of an observational cohort study from the Intensive Care Unit of the First Affiliated Hospital of Zhengzhou University. A total of 4449 infected patients were randomly assigned to the development and validation data set at a ratio of 4:1. After extracting electronic medical record data, a set of 55 features (variables) was calculated and passed to the random forest algorithm to predict the onset of sepsis.Results: The pre-procedure clinical variables were used to build a prediction model from the training data set using the random forest machine learning method; a 5-fold cross-validation was used to evaluate the prediction accuracy of the model. Finally, we tested the model using the validation data set. The area obtained by the model under the receiver operating characteristic (ROC) curve (AUC) was 0.91, the sensitivity was 87%, and the specificity was 89%.Conclusions: The newly established model can accurately predict the onset of sepsis in ICU patients in clinical settings as early as possible. Prospective studies are necessary to determine the clinical utility of the proposed sepsis prediction model.

scholarly journals A machine learning-based treatment prediction model using whole genome variants of hepatitis C virus

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242028
Author(s):  
Hiroaki Haga ◽  
Hidenori Sato ◽  
Ayumi Koseki ◽  
Takafumi Saito ◽  
Kazuo Okumoto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Prediction Model ◽  
Predictive Model ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Whole Genome ◽  
Validation Data ◽  
Data Set

In recent years, the development of diagnostics using artificial intelligence (AI) has been remarkable. AI algorithms can go beyond human reasoning and build diagnostic models from a number of complex combinations. Using next-generation sequencing technology, we identified hepatitis C virus (HCV) variants resistant to directing-acting antivirals (DAA) by whole genome sequencing of full-length HCV genomes, and applied these variants to various machine-learning algorithms to evaluate a preliminary predictive model. HCV genomic RNA was extracted from serum from 173 patients (109 with subsequent sustained virological response [SVR] and 64 without) before DAA treatment. HCV genomes from the 109 SVR and 64 non-SVR patients were randomly divided into a training data set (57 SVR and 29 non-SVR) and a validation-data set (52 SVR and 35 non-SVR). The training data set was subject to nine machine-learning algorithms selected to identify the optimized combination of functional variants in relation to SVR status following DAA therapy. Subsequently, the prediction model was tested by the validation-data set. The most accurate learning method was the support vector machine (SVM) algorithm (validation accuracy, 0.95; kappa statistic, 0.90; F-value, 0.94). The second-most accurate learning algorithm was Multi-layer perceptron. Unfortunately, Decision Tree, and Naive Bayes algorithms could not be fitted with our data set due to low accuracy (< 0.8). Conclusively, with an accuracy rate of 95.4% in the generalization performance evaluation, SVM was identified as the best algorithm. Analytical methods based on genomic analysis and the construction of a predictive model by machine-learning may be applicable to the selection of the optimal treatment for other viral infections and cancer.

scholarly journals A novel ferroptosis related gene signature is associated with prognosis in patients with ovarian serous cystadenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhixiang Yu ◽  
Haiyan He ◽  
Yanan Chen ◽  
Qiuhe Ji ◽  
Min Sun
Keyword(s):  
Cox Regression ◽  
Expression Profiles ◽  
Critical Role ◽  
Gene Signature ◽  
Cancer Genome ◽  
Training Data ◽  
Hub Genes ◽  
Validation Data ◽  
Data Set ◽  
Cox Regression Analysis

AbstractOvarian cancer (OV) is a common type of carcinoma in females. Many studies have reported that ferroptosis is associated with the prognosis of OV patients. However, the mechanism by which this occurs is not well understood. We utilized Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) to identify ferroptosis-related genes in OV. In the present study, we applied Cox regression analysis to select hub genes and used the least absolute shrinkage and selection operator to construct a prognosis prediction model with mRNA expression profiles and clinical data from TCGA. A series of analyses for this signature was performed in TCGA. We then verified the identified signature using International Cancer Genome Consortium (ICGC) data. After a series of analyses, we identified six hub genes (DNAJB6, RB1, VIMP/ SELENOS, STEAP3, BACH1, and ALOX12) that were then used to construct a model using a training data set. The model was then tested using a validation data set and was found to have high sensitivity and specificity. The identified ferroptosis-related hub genes might play a critical role in the mechanism of OV development. The gene signature we identified may be useful for future clinical applications.

SaltSeg: Automatic 3D salt segmentation using a deep convolutional neural network

2019 ◽  
Vol 7 (3) ◽  
pp. SE113-SE122 ◽  
Author(s):  
Yunzhi Shi ◽  
Xinming Wu ◽  
Sergey Fomel
Keyword(s):  
Large Scale ◽  
Model Building ◽  
Ground Truth ◽  
Velocity Model ◽  
Training Data ◽  
Data Sets ◽  
Validation Data ◽  
Data Set ◽  
Seismic Image ◽  
Data Generator

Salt boundary interpretation is important for the understanding of salt tectonics and velocity model building for seismic migration. Conventional methods consist of computing salt attributes and extracting salt boundaries. We have formulated the problem as 3D image segmentation and evaluated an efficient approach based on deep convolutional neural networks (CNNs) with an encoder-decoder architecture. To train the model, we design a data generator that extracts randomly positioned subvolumes from large-scale 3D training data set followed by data augmentation, then feed a large number of subvolumes into the network while using salt/nonsalt binary labels generated by thresholding the velocity model as ground truth labels. We test the model on validation data sets and compare the blind test predictions with the ground truth. Our results indicate that our method is capable of automatically capturing subtle salt features from the 3D seismic image with less or no need for manual input. We further test the model on a field example to indicate the generalization of this deep CNN method across different data sets.

scholarly journals Prediction of Cardiac Arrest in the Emergency Department Based on Machine Learning and Sequential Characteristics: Model Development and Retrospective Clinical Validation Study (Preprint)

2019 ◽  
Author(s):  
Sungjun Hong ◽  
Sungjoo Lee ◽  
Jeonghoon Lee ◽  
Won Chul Cha ◽  
Kyunga Kim
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Cardiac Arrest ◽  
Prediction Model ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Clinical Usefulness ◽  
Class Imbalance Problem ◽  
Data Set

BACKGROUND The development and application of clinical prediction models using machine learning in clinical decision support systems is attracting increasing attention. OBJECTIVE The aims of this study were to develop a prediction model for cardiac arrest in the emergency department (ED) using machine learning and sequential characteristics and to validate its clinical usefulness. METHODS This retrospective study was conducted with ED patients at a tertiary academic hospital who suffered cardiac arrest. To resolve the class imbalance problem, sampling was performed using propensity score matching. The data set was chronologically allocated to a development cohort (years 2013 to 2016) and a validation cohort (year 2017). We trained three machine learning algorithms with repeated 10-fold cross-validation. RESULTS The main performance parameters were the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). The random forest algorithm (AUROC 0.97; AUPRC 0.86) outperformed the recurrent neural network (AUROC 0.95; AUPRC 0.82) and the logistic regression algorithm (AUROC 0.92; AUPRC=0.72). The performance of the model was maintained over time, with the AUROC remaining at least 80% across the monitored time points during the 24 hours before event occurrence. CONCLUSIONS We developed a prediction model of cardiac arrest in the ED using machine learning and sequential characteristics. The model was validated for clinical usefulness by chronological visualization focused on clinical usability.

scholarly journals Amino Acids and Lipids Associated with Long-Term and Short-Term Red Meat Consumption in the Chinese Population: An Untargeted Metabolomics Study

Nutrients ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 4567
Author(s):  
Fangxu Guan ◽  
Wenwen Du ◽  
Jiguo Zhang ◽  
Chang Su ◽  
Bing Zhang ◽  
...  
Keyword(s):  
Amino Acids ◽  
Prediction Model ◽  
Chinese Population ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Red Meat ◽  
Short Term ◽  
Nutrition Survey ◽  
Negatively Associated

Red meat (RM) consumption is correlated with multiple health outcomes. This study aims to identify potential biomarkers of RM consumption in the Chinese population and evaluate their predictive ability. We selected 500 adults who participated in the 2015 China Health and Nutrition Survey and examined their overall metabolome differences by RM consumption by using elastic-net regression, then evaluate the predictivity of a combination of filtered metabolites; 1108 metabolites were detected. In the long-term RM consumption analysis 12,13-DiHOME, androstenediol (3α, 17α) monosulfate 2, and gamma-Glutamyl-2-aminobutyrate were positively associated, 2-naphthol sulfate and S-methylcysteine were negatively associated with long-term high RM consumption, the combination of metabolites prediction model evaluated by area under the receiver operating characteristic curve (AUC) was 70.4% (95% CI: 59.9–80.9%). In the short-term RM consumption analysis, asparagine, 4-hydroxyproline, and 3-hydroxyisobutyrate were positively associated, behenoyl sphingomyelin (d18:1/22:0) was negatively associated with short-term high RM consumption. Combination prediction model AUC was 75.6% (95% CI: 65.5–85.6%). We identified 10 and 11 serum metabolites that differed according to LT and ST RM consumption which mainly involved branch-chained amino acids, arginine and proline, urea cycle and polyunsaturated fatty acid metabolism. These metabolites may become a mediator of some chronic diseases among high RM consumers and provide new evidence for RM biomarkers.

scholarly journals The Stunting Tool for Early Prevention: development and external validation of a novel tool to predict risk of stunting in children at 3 years of age

2019 ◽  
Vol 4 (6) ◽  
pp. e001801
Author(s):  
Sarah Hanieh ◽  
Sabine Braat ◽  
Julie A Simpson ◽  
Tran Thi Thu Ha ◽  
Thach D Tran ◽  
...  
Keyword(s):  
Characteristic Curve ◽  
External Validation ◽  
Model Development ◽  
Validation Data ◽  
Data Set ◽  
Growth Faltering ◽  
Development Data ◽  
Gestational Age At Birth ◽  
Development And Validation ◽  
The Impact

IntroductionGlobally, an estimated 151 million children under 5 years of age still suffer from the adverse effects of stunting. We sought to develop and externally validate an early life predictive model that could be applied in infancy to accurately predict risk of stunting in preschool children.MethodsWe conducted two separate prospective cohort studies in Vietnam that intensively monitored children from early pregnancy until 3 years of age. They included 1168 and 475 live-born infants for model development and validation, respectively. Logistic regression on child stunting at 3 years of age was performed for model development, and the predicted probabilities for stunting were used to evaluate the performance of this model in the validation data set.ResultsStunting prevalence was 16.9% (172 of 1015) in the development data set and 16.4% (70 of 426) in the validation data set. Key predictors included in the final model were paternal and maternal height, maternal weekly weight gain during pregnancy, infant sex, gestational age at birth, and infant weight and length at 6 months of age. The area under the receiver operating characteristic curve in the validation data set was 0.85 (95% Confidence Interval, 0.80–0.90).ConclusionThis tool applied to infants at 6 months of age provided valid prediction of risk of stunting at 3 years of age using a readily available set of parental and infant measures. Further research is required to examine the impact of preventive measures introduced at 6 months of age on those identified as being at risk of growth faltering at 3 years of age.

Sign in / Sign up

Export Citation Format

Share Document