Comparisons of Forecasting for Survival Outcome for Head and Neck Squamous Cell Carcinoma by using Machine Learning Models based on Multi-omics.

Background: Machine learning methods showed excellent predictive ability in a wide range of fields. For the survival of head and neck squamous cell carcinoma (HNSC), its multi-omics influence is crucial. This study attempts to establish a variety of machine learning multi-omics models to predict the survival of HNSC and find the most suitable machine learning prediction method. Methods: The HNSC clinical data and multi-omics data were downloaded from the TCGA database. The important variables were screened by the LASSO algorithm. We used a total of 12 supervised machine learning models to predict the outcome of HNSC survival and compared the results. In vitro qPCR was performed to verify core genes predicted by the random forest algorithm. Results: For omics of HNSC, the results of the twelve models showed that the performance of multi-omics was better than each single-omic alone. Results were presented, which showed that the Bayesian network(BN) model (area under the curve [AUC] 0.8250, F1 score=0.7917) and random forest(RF) model (area under the curve [AUC] 0.8002,F1 score=0.7839) played good prediction performance in HNSC multi-omics data. The results of in vitro qPCR were consistent with the RF algorithm. Conclusion: Machine learning methods could better forecast the survival outcome of HNSC. Meanwhile, this study found that the BN model and the RF model were the most superior. Moreover, the forecast result of multi-omics was better than single-omic alone in HNSC.

Machine learning to predict no reflow and in-hospital mortality in patients with ST-segment elevation myocardial infarction that underwent primary percutaneous coronary intervention.

BACKGROUND: The machine learning algorithm (MLA) was implemented to establish an optimal model to predict the no reflow (NR) process and in-hospital death that occurred in ST-elevation myocardial infarction (STEMI) patients who underwent primary percutaneous coronary intervention (pPCI). METHODS: The data were obtained retrospectively from 854 STEMI patients who underwent pPCI. MLA was applied to predict the potential NR phenomenon and confirm the in-hospital mortality. A random sampling method was used to split the data into the training (66.7%) and testing (33.3%) sets. The final results were an average of 10 repeated procedures. The area under the curve (AUC) and the associated 95% confidence intervals (CIs) of the receiver operator characteristic were measured. RESULTS: A random forest algorithm (RAN) had optimal discrimination for the NR phenomenon with an AUC of 0.7891 (95% CI: 0.7093-0.8688) compared with 0.6437 (95% CI: 0.5506-0.7368) for the decision tree (CTREE), 0.7488 (95% CI: 0.6613-0.8363) for the support vector machine (SVM), and 0.681 (95% CI: 0.5767-0.7854) for the neural network algorithm (NNET). The optimal RAN AUC for in-hospital mortality was 0.9273 (95% CI: 0.8819-0.9728), for SVM, 0.8935 (95% CI: 0.826-0.9611); NNET, 0.7756 (95% CI: 0.6559-0.8952); and CTREE, 0.7885 (95% CI: 0.6738-0.9033). CONCLUSIONS: The MLA had a relatively higher performance when evaluating the NR risk and in-hospital mortality in patients with STEMI who underwent pPCI and could be utilized in clinical decision making.

UC-stack: a deep learning computer automatic detection system for diabetic retinopathy classification.

Object. The existing diagnostic paradigm for diabetic retinopathy (DR) greatly relies on subjective assessments by medical practitioners utilizing optical imaging, introducing susceptibility to individual interpretation. This work presents a novel system for the early detection and grading of DR, providing an automated alternative to the manual examination.Approach. First, we use advanced image preprocessing techniques, specifically contrast-limited adaptive histogram equalization and Gaussian filtering, with the goal of enhancing image quality and module learning capabilities. Second, a deep learning-based automatic detection system is developed. The system consists of a feature segmentation module, a deep learning feature extraction module, and an ensemble classification module. The feature segmentation module accomplishes vascular segmentation, the deep learning feature extraction module realizes the global feature and local feature extraction of retinopathy images, and the ensemble module performs the diagnosis and classification of DR for the extracted features. Lastly, nine performance evaluation metrics are applied to assess the quality of the model's performance.Main results. Extensive experiments are conducted on four retinal image databases (APTOS 2019, Messidor, DDR, and EyePACS). The proposed method demonstrates promising performance in the binary and multi-classification tasks for DR, evaluated through nine indicators, including AUC and quadratic weighted Kappa score. The system shows the best performance in the comparison of three segmentation methods, two convolutional neural network architecture models, four Swin Transformer structures, and the latest literature methods.Significance. In contrast to existing methods, our system demonstrates superior performance across multiple indicators, enabling accurate screening of DR and providing valuable support to clinicians in the diagnostic process. Our automated approach minimizes the reliance on subjective assessments, contributing to more consistent and reliable DR evaluations.

Use of survival support vector machine combined with random survival forest to predict the survival of nasopharyngeal carcinoma patients.

Background: The Cox regression model is not sufficiently accurate to predict the survival prognosis of nasopharyngeal carcinoma (NPC) patients. It is impossible to calculate and rank the importance of impact factors due to the low predictive accuracy of the Cox regression model. So, we developed a system. Using the SEER (The Surveillance, Epidemiology, and End Results) database data on NPC patients, we proposed the use of random survival forest (RSF) and survival-support vector machine (SVM) from the machine learning methods to develop a survival prediction system specifically for NPC patients. This approach aimed to make up for the insufficiency of the Cox regression model. We also used the Cox regression model to validate the development of the nomogram and compared it with machine learning methods. Methods: A total of 1,683 NPC patients were extracted from the SEER database from January 2010 to December 2015. We used R language for modeling work, established the nomogram of survival prognosis of NPC patients by Cox regression model, ranked the correlation of influencing factors by RSF model VIMP (variable important) method, developed a survival prognosis system for NPC patients based on survival-SVM, and used C-index for model evaluation and performance comparison. Results: Although the Cox regression models can be developed to predict the prognosis of NPC patients, their accuracy was lower than that of machine learning methods. When we substituted the data for the Cox model, the C-index for the training set was only 0.740, and the C-index for the test set was 0.721. In contrast, the C index of the survival-SVM model was 0.785. The C-index of the RSF model was 0.729. The importance ranking of each variable could be obtained according to the VIMP method. Conclusions: The prediction results from the Cox model are not as good as those of the RSF method and survival-SVM based on the machine learning method. For the survival prognosis of NPC patients, the machine learning method can be considered for clinical application.

Findings About Patient Preferences for Medical Care Based on a Decision Tree Method Study Design for Influencing Factors.

Aim: This study aimed to investigate the influencing factors of the medical-seeking behavior of patients in a hospital in Nanning and descriptively analyze the main factors to further improve the medical system and optimize the allocation of health resources. Subject and methods: The willingness to seek medical treatment questionnaire survey was conducted on patients who were in the outpatient clinic of a hospital in Nanning from Jun 2018 to Aug 2019. The patients' basic information was analyzed descriptively using the SPSS 23.0 software package, and the influencing factors of the willingness to seek medical treatment were analyzed by univariate analysis method. In addition, the importance of influencing factors in patient preference to seek medical treatment was explored by constructing a decision tree model. Results: A total of 3428 questionnaires were valid and the effective rate was 93.78%. Region, age, occupation, educational level, monthly income, insurance type, and disease type demonstrated diverse influences on the medical expenses of patients. In addition, differences were found between occupation and patient insurance situation, personal willingness to seek medical treatment, reasons for visiting the hospital, medical selection standard, preferred medical treatment location for common diseases, waiting time, treatment time, and manner of understanding the disease. Conclusion: Increasing attention has been paid on the patients' preference for medical treatment and their satisfaction with medical services. Medical institutions should reasonably allocate the proportion of medical insurance reimbursement and diversify the registration and appointment methods. Patients should be treated in different periods and properly allocated to improve the service mechanism of primary medical institutions. In addition, it is necessary to improve the medical publicity model and the efficiency of medical services according to the needs of patients, so as to relieve the pressure of medical treatment in large general hospitals.

A Method for Improving the Prediction of Outpatient Visits for Hospital Management: Bayesian Autoregressive Analysis.

The number of outpatient visits is generally influenced by various factors that are difficult to quantify and obtain, resulting in some irregular fluctuations. The traditional statistical methodology seldom considers these uncertainties. Accordingly, this paper presents a Bayesian autoregressive (AR) analysis to propose a forecasting framework to cope with the strict requirements. The AR model was conducted to identify the linear and autocorrelation relationships of historical series, and Bayesian inference was used to correct and optimize the AR model parameters. Posterior distribution of parameters was stably and reliably obtained by Gibbs sampling on the condition of the convergent Markov chain. Meanwhile, the lag orders of the AR model were adjusted based on the series characteristics. To increase the variability and generality of the dataset, the developed Bayesian AR model was evaluated at seven hospitals in China. The results demonstrated that the Bayesian AR model had varying degrees of decline in the MAPE value in the seven sets of experimental data. The reductions ranged from 0.1431% to 0.0342%, indicating effective optimization of the Bayesian inference in the AR model parameters and reflecting the useful correction of the lag order adjustment strategy. The proposed Bayesian AR framework showed high accuracy index and stable prediction accuracy, thereby outperforming the traditional AR model.

Assessment of kitchen emissions using a backpropagation neural network model based on urinary hydroxy polycyclic aromatic hydrocarbons.

Kitchen emissions are mixed indoor air pollutants with adverse health effects, but the large-scale assessment is limited by costly equipment and survey methods. This study aimed to discuss the application of backpropagation (BP) neural network models in the assessment of kitchen emissions based on the exposure marker. A total of 3686 participants were recruited for the kitchen survey, and their sleep quality was measured by the Pittsburgh sleep quality index (PSQI). After excluding the confounders, 365 participants were selected to assess their urinary hydroxy polycyclic aromatic hydrocarbons (OH-PAHs) concentrations by ultra-high-performance liquid chromatography/tandem mass spectrometry. Two BP neural network models were then set up using the survey and detection data from the 365 participants and used to predict the total urinary OH-PAHs concentrations of all participants. The total urinary OH-PAHs and 1-hydroxy-naphthalene (1-OHNap) concentrations were significantly higher among the 365 participants with poor sleep quality (global PSQI score > 5; P < 0.05). Results from internal and external validation showed that our model has high credibility (model 2). Further, the participants with higher predicted total urinary OH-PAHs concentrations were associated with the global PSQI score of >5 (odds ratio (OR) = 1.284, 95% confidence interval (CI) = 1.082-1.525 for participants with predicted total urinary OH-PAHs concentrations of over 1.897 µg/mmol creatinine in model 1, and OR = 1.467, 95% CI = 1.240-1.735 for participants with predicted total urinary OH-PAHs concentrations of over 2.253 µg/mmol creatinine in model 2) after adjusting for the confounders. Findings suggest that the BP neural network model is suitable for assessing kitchen emissions, and the urinary OH-PAHs concentrations can be taken as the model outlay.

Multiple-life-stage probabilistic risk assessment for the exposure of Chinese population to PBDEs and risk managements.

Studies assessing body burden of polybrominated diphenyl ethers (PBDEs) exposure have been conducted in the United States and Europe. However, the long-term assessment that is associated with multimedia exposure of PBDEs for the Chinese population is not available. The current study estimated the health risks using large PBDEs data to quantify the contributions of various media from different regions and distinguished the most vulnerable periods in life. We summarized media-specific (soil, dust, outdoor and indoor air, human milk and food) concentration of PBDEs in China from 2005 to 2016. Probabilistic risk assessment was adopted to estimate the health risks of infants, toddlers, children, teenagers and adults through ingestion, inhalation and dermal absorption. Monte Carlo simulation and sensitivity analysis were performed to quantify risk estimates uncertainties. E-waste areas had the highest PBDEs concentration, which was at least an order of magnitude higher than in other areas. BDE209 was the primary congener, accounting for 38-99% of the estimated daily intake. The dominant exposure pathway for infants was dietary intake through human milk, whereas dust ingestion was a higher contributing factor for toddlers, children, teenagers and adults. The 95th percentile of hazard index for infants and toddlers from e-waste areas of Guangdong and Zhejiang provinces exceeded one. Our estimates also suggested that infants may have the highest body burdens of PBDEs compared to other age groups. Sensitivity analyses indicated that PBDEs concentrations and ingestion rates contributed to major variances in the risk model. In this study, e-waste was found as a significant source of PBDEs, and PBDEs-containing e-waste are likely to be a threat to human health especially during early period of life. Risk strategies for better managing environmental PBDEs-exposure and human health are needed, due to the high intake of PBDEs and their persistence in the environment.

Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization.

Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods.

Probabilistic risk assessment of Chinese residents' exposure to fluoride in improved drinking water in endemic fluorosis areas.

Studies have yet to evaluate the effects of water improvement on fluoride concentrations in drinking water and the corresponding health risks to Chinese residents in endemic fluorosis areas (EFAs) at a national level. This paper summarized available data in the published literature (2008-2016) on water fluoride from the EFAs in China before and after water quality was improved. Based on these obtained data, health risk assessment of Chinese residents' exposure to fluoride in improved drinking water was performed by means of a probabilistic approach. The uncertainties in the risk estimates were quantified using Monte Carlo simulation and sensitivity analysis. Our results showed that in general, the average fluoride levels (0.10-2.24 mg/L) in the improved drinking water in the EFAs of China were lower than the pre-intervention levels (0.30-15.24 mg/L). The highest fluoride levels were detected in North and Southwest China. The mean non-carcinogenic risks associated with consumption of the improved drinking water for Chinese residents were mostly accepted (hazard quotient < 1), but the non-carcinogenic risk of children in most of the EFAs at the 95th percentile exceeded the safe level of 1, indicating the potential non-cancer-causing health effects on this fluoride-exposed population. Sensitivity analyses indicated that fluoride concentration in drinking water, ingestion rate of water, and the exposure time in the shower were the most relevant variables in the model, therefore, efforts should focus mainly on the definition of their probability distributions for a more accurate risk assessment.

Comparisons of forecasting for hepatitis in Guangxi Province, China by using three neural networks models.

This study compares and evaluates the prediction of hepatitis in Guangxi Province, China by using back propagation neural networks based genetic algorithm (BPNN-GA), generalized regression neural networks (GRNN), and wavelet neural networks (WNN). In order to compare the results of forecasting, the data obtained from 2004 to 2013 and 2014 were used as modeling and forecasting samples, respectively. The results show that when the small data set of hepatitis has seasonal fluctuation, the prediction result by BPNN-GA will be better than the two other methods. The WNN method is suitable for predicting the large data set of hepatitis that has seasonal fluctuation and the same for the GRNN method when the data increases steadily.

Application of a hybrid method combining grey model and back propagation artificial neural networks to forecast hepatitis B in china.

Accurate incidence forecasting of infectious disease provides potentially valuable insights in its own right. It is critical for early prevention and may contribute to health services management and syndrome surveillance. This study investigates the use of a hybrid algorithm combining grey model (GM) and back propagation artificial neural networks (BP-ANN) to forecast hepatitis B in China based on the yearly numbers of hepatitis B and to evaluate the method's feasibility. The results showed that the proposal method has advantages over GM (1, 1) and GM (2, 1) in all the evaluation indexes.