Abstract 1634: A topology based data analysis method to predict survival time

This focus issue of Physiological Measurement follows the 38th Annual International Computing in Cardiology (CinC) Conference, hosted in Hangzhou, China in September 2011 by Zhejiang University. Each year, the NIH-sponsored PhysioNet resource (http://physionet.org/) runs an open competition lasting several months, aimed at encouraging the development of solutions to an unsolved or poorly solved problem in biomedicine, in most cases making use of relevant clinical and experimental data provided freely by PhysioNet. Participants in these annual challenges discuss their diverse approaches to the Challenge problems during dedicated scientific sessions at CinC. The topics of these PhysioNet/CinC Challenges range from physiologic signal processing and analysis to forecasting and modelling clinically important events and processes.

Real-World Surveillance of FDA-Cleared Artificial Intelligence Models: Rationale and Logistics.

The role of pertinently diversified and balanced training as well as testing data sets in achieving the true performance of classifiers in predicting the antifreeze proteins

The process of extracting meaningful rules from big and complex data is called data mining. Data mining has an increasing popularity in every field today. Data units are established in customer-oriented industries such as marketing, finance and telecommunication to work on the customer churn and acquisition, in particular. Among the data mining methods, classification algorithms are used in studies conducted for customer acquisition to predict the potential customers of the company in question in the related industry. In this study, bank marketing data set in UCI Machine Learning Data Set was used by creating models with the same classification algorithms in different data mining programs. Accuracy, precision and f- measure criteria were used to test performances of the classification models. When creating the classification models, the test and training data sets were randomly divided by the holdout method to evaluate the performance of the data set. The data set was divided into training and test data sets with the 60-40%, 75­25% and 80-20% separation ratios. Data mining programs used for these processes are the R, Knime, RapidMiner and WEKA. And, classification algorithms commonly used in these platforms are the k-nearest neighbor (k-nn), Naive Bayes, and C4.5 decision tree.

Objective: To compare the performance of multiple machine learning algorithms in predicting recurrence after resection of early-stage hepatocellular carcinoma(HCC). Methods: Clinical data of 882 early-stage HCC patients who were admitted to the First Affiliated Hospital of Nanjing Medical University from May 2009 to December 2019 and treated with curative surgical resection were retrospectively collected. There were 701 males and 181 females,with an age of (57.3±10.5)years(range:21 to 86 years). All patients were randomly assigned in a 2∶1 ratio, the training dataset consisted of 588 patients and the test dataset consisted of 294 patients. The construction of machine learning-based prediction models included random survival forest(RSF),gradient boosting machine,elastic net regression and Cox regression model. The prediction accuracy of the model was measured by the concordance index(C-index). The prediction error of the model was measured by the integrated Brier score. Model fit was assessed by the calibration plot. The performance of machine learning models with that of rival model and HCC staging systems was compared. All models were validated in the independent test dataset. Results: Median recurrence-free survival was 61.7 months in the training dataset while median recurrence-free survival was 61.9 months in the validation dataset, there was no significant difference between two datasets in terms of recurrence-free survival(χ²=0.029,P=0.865). The RSF model consisted of 5 commonly used clinicopathological characteristics, including albumin-bilirubin grade,serum alpha fetoprotein,tumor number,type of hepatectomy and microvascular invasion. In both training and test datasets,the RSF model provided the best prediction accuracy,with respective C-index of 0.758(95%CI:0.725 to 0.791) and 0.749(95%CI:0.700 to 0.797),and the lowest prediction error,with respective integrated Brier score of 0.171 and 0.151. The prediction accuracy of RSF model for recurrence after resection of early-stage HCC was superior to that of other machine learning models,rival model(ERASL model) as well as HCC staging systems(BCLC,CNLC and TNM staging),with statistically significant difference(P<0.01). Calibration curves demonstrated good agreement between RSF model-predicted probabilities and observed outcomes.All patients could be stratified into low-risk,intermediate-risk or high-risk group based on RSF model;statistically significant differences among three risk groups were observed in both training and test datasets(all P<0.01). The risk stratification of RSF model was superior to that of TNM staging. Conclusion: The proposed RSF model assembled with 5 commonly used clinicopathological characteristics in this study can predict the recurrence risk with favorable accuracy that may facilitate clinical decision-support for patients with early-stage HCC.

Objects This study aims to develop an Exploratory deep learning method to quantitatively predict hematoma progression (EXPEDITION in short) after intracerebral hemorrhage (ICH). Methods Patients with primary ICH in the basal ganglia or thalamus were retrospectively enrolled, and their baseline non-contrast CT (NCCT) image, CT perfusion (CTP) images, and subsequent re-examining NCCT images from the 2nd to the 8th day after baseline CTP were collected. The subjects who had received three or more re-examining scans were categorized into the test data set, and others were assigned to the training data set. Hematoma volume was estimated by manually outlining the lesion shown on each NCCT scan. Cerebral venous hemodynamic feature was extracted from CTP images. Then, EXPEDITION was trained. The Bland–Altman analysis was used to assess the prediction performance. Results A total of 126 patients were enrolled initially, and 73 patients were included in the final analysis. They were then categorized into the training data set (58 patients with 93 scans) and the test data set (15 patients with 50 scans). For the test set, the mean difference [mean ±1.96SD] of hematoma volume between the EXPEDITION prediction and the reference is −0.96 [−9.64, +7.71] mL. Specifically, in the test set, the consistency between the true and the predicted volume values was compared, indicating that the EXPEDITION achieved the needed accuracy for quantitative prediction of hematoma progression. Conclusions An Exploratory deep learning method, EXPEDITION, was proposed to quantitatively predict hematoma progression after primary ICH in basal ganglia or thalamus.

An important benchmark for closed-loop flow control is the suppression of the von Karman vortex street in the wake of a circular cylinder at a Reynolds number of 100. A low-dimensional Proper Orthogonal Decomposition (POD) is applied to the flow field and a four sensor configuration is placed based on the intensity of the resulting spatial Eigenfunctions. This effort focuses on the performance of a non-linear Artificial Neural Network Estimator (ANNE) used for real-time mapping of flow field sensors to POD states. This investigation examines the sensitivity of ANNE by varying the number of neurons in the hidden layer. The aim is to determine if there is an optimum number of neurons in the hidden layer. Three input data sets were studied, the first had no noise on the training and testing data, the second had 10% noise applied to the training and testing data, and the third had 25% noise applied to the training and testing data sets. In all cases 50 randomly selected training data sets were applied to the back-propagating network. A separate data set was used for testing the feed-forward network. During the validation process, network outputs were compared to desired outputs and the Root Mean Squared (RMS) error was calculated for each of the four output nodes. Results show that in general, the more nodes in the hidden layer the better the performance of the network. However, there appeared to be a consistently reduced error when eight (8) nodes were used in the hidden layer. Results from ANNE when compared to the state-of-the-art Linear Stochastic Estimator (LSE) shows significant benefits.

Purpose Continuous fiber-reinforced thermoplastic composites are a class of materials highly valuable for structural applications and modeling of heat transfer within them is critical to the design of their processing methods. However, the fiber reinforcement leads to highly anisotropic thermal conduction. Among a variety of methods to account for anisotropic thermal conductivity, continuum models with effective media approximation thermal conductivity are computationally efficient and require minimal data to begin modeling a specific composite material. The purpose of this study is to evalute the utility of these models. Design/methodology/approach In this work, six potential effective media approximation models are evaluated against experimental heating data. Thick (>25 mm) glass fiber-reinforced polyethylene terephthalate glycol (PET-G) specimens with 40% fiber volume fraction were heated with embedded resistance heating to produce validation and testing data sets. A two-dimensional finite-difference solver was implemented using each of the six effective media approximation models. The accuracy of each model is compared. Findings The model developed by Cheng and Vachon was found to predict the experimental results most accurately. Fit statistics were similar in the testing and validation data sets. This model is recommended for simulation of transient heating in continuous fiber-reinforced thermoplastic composites with low-to-moderate fiber volume fractions. Originality/value There are a wide variety of mathematical models for effective media approximation thermal conductivity, though very few have been applied to continuous fiber-reinforced thermoplastic composites. This work shows that the simplest methods based on rules of mixtures are well outperformed by more modern and complex models, and should be incorporated for accurate prediction of heating during thermal processing of fiber-reinforced thermoplastic composites.

High-dimensional data sets are often available in genome-enabled predictions. Such data sets include nonlinear relationships with complex dependence structures. For such situations, vine copula-based (quantile) regression is an important tool. However, the current vine copula-based regression approaches do not scale up to high and ultra-high dimensions. To perform high-dimensional sparse vine copula-based regression, we propose 2 methods. First, we show their superiority regarding computational complexity over the existing methods. Second, we define relevant, irrelevant, and redundant explanatory variables for quantile regression. Then, we show our method's power in selecting relevant variables and prediction accuracy in high-dimensional sparse data sets via simulation studies. Next, we apply the proposed methods to the high-dimensional real data, aiming at the genomic prediction of maize traits. Some data processing and feature extraction steps for the real data are further discussed. Finally, we show the advantage of our methods over linear models and quantile regression forests in simulation studies and real data applications.

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Predictive maintenance is crucial for Industry 4.0, and deep neural networks are a promising approach for predicting the capacity of electric batteries. However, few applications effectively utilize neural networks for this purpose with lithium-ion batteries. In this work, different deep learning models are developed, starting with simple neural networks, dense neural networks, convolutional networks, and recurrent networks. Using a public domain dataset, training, testing, and validation datasets were generated to predict battery capacity as a function of the number of cycles. Despite the limited number of samples in the dataset, deep learning techniques are employed to ensure robust prediction performance. The work presents the loss functions for each iteration of the algorithms and the average absolute error. The models made good generalizations over the test dataset within a short prediction time window. Finally, the work presents an average absolute error below 0.3, ensuring good convergence, avoiding data overfitting, and ensuring good generalization of the model to the test set. This work is a first estimate of the life cycle of electric vehicle batteries using small samples as inputs for estimating the remaining useful life of this electric vehicle component.&lt;/div&gt;&lt;/div&gt;

Background Deep learning (DL) algorithms could improve the classification of ovarian tumors assessed with multimodal US. Purpose To develop DL algorithms for the automated classification of benign versus malignant ovarian tumors assessed with US and to compare algorithm performance to Ovarian-Adnexal Reporting and Data System (O-RADS) and subjective expert assessment for malignancy. Materials and Methods This retrospective study included consecutive women with ovarian tumors undergoing gray scale and color Doppler US from January 2019 to November 2019. Histopathologic analysis was the reference standard. The data set was divided into training (70%), validation (10%), and test (20%) sets. Algorithms modified from residual network (ResNet) with two fusion strategies (feature fusion [hereafter, DLfeature] or decision fusion [hereafter, DLdecision]) were developed. DL prediction of malignancy was compared with O-RADS risk categorization and expert assessment by area under the receiver operating characteristic curve (AUC) analysis in the test set. Results A total of 422 women (mean age, 46.4 years ± 14.8 [SD]) with 304 benign and 118 malignant tumors were included; there were 337 women in the training and validation data set and 85 women in the test data set. DLfeature had an AUC of 0.93 (95% CI: 0.85, 0.97) for classifying malignant from benign ovarian tumors, comparable with O-RADS (AUC, 0.92; 95% CI: 0.85, 0.97; P = .88) and expert assessment (AUC, 0.97; 95% CI: 0.91, 0.99; P = .07), and similar to DLdecision (AUC, 0.90; 95% CI: 0.82, 0.96; P = .29). DLdecision, DLfeature, O-RADS, and expert assessment achieved sensitivities of 92%, 92%, 92%, and 96%, respectively, and specificities of 80%, 85%, 89%, and 87%, respectively, for malignancy. Conclusion Deep learning algorithms developed by using multimodal US images may distinguish malignant from benign ovarian tumors with diagnostic performance comparable to expert subjective and Ovarian-Adnexal Reporting and Data System assessment. © RSNA, 2022 Online supplemental material is available for this article.

P68. Machine learning algorithms for predicting patient-controlled analgesia use after minimally invasive spine surgery

This study aimed to develop a multiregional radiomics-based model using multisequence MRI to predict microvascular invasion in hepatocellular carcinoma. We enrolled 141 patients with hepatocellular carcinoma, including 61 with microvascular invasion, who were diagnosed between March 2017 and July 2022. Clinical data were compared using the Wilcoxon rank-sum test or χ 2 test. Patients were randomly divided into training (n=112, 80%) and test (n=29, 20%) data sets. Four MRI sequences-including T2-weighted imaging, T2-weighted imaging with fat suppression, arterial phase-contrast enhancement, and portal venous phase contrast enhancement-were used to build the radiomics model. The tumor volumes of interest were manually delineated, and the expand-5mm and expand-10mm volumes of interest were automatically generated. A total of 1409 radiomic features were extracted from each volume of interest. Feature selection was performed using the least absolute shrinkage and selection operator and Spearman correlation analysis. Three logistic regression models (Tumor, Tumor-Expand5, and Tumor-Expand10) were established based on the radiomic features. Model performance was assessed using receiver operating characteristic analysis and Delong's test. Maximum tumor diameter, hepatitis B virus DNA, and aspartate aminotransferase levels were significantly different between the groups. The Tumor-Expand5mm model exhibited the best performance among the 3 models, with areas under the curve of 0.90 and 0.84 in the training and test data sets. The Tumor-Expand5 model based on multisequence MRI shows great potential for predicting microvascular invasion in patients with hepatocellular carcinoma, and may further contribute to personal clinical decision-making.

SummaryAccurate prediction of the solubility of chemical substances in solvents remains a challenge. The sparsity of high-quality solubility data is recognized as the biggest hurdle in the development of robust data-driven methods for practical use. Nonetheless, the effects of the quality and quantity of data on aqueous solubility predictions have not yet been scrutinized. In this study, the roles of the size and the quality of data sets on the performances of the solubility prediction models are unraveled, and the concepts of actual and observed performances are introduced. In an effort to curtail the gap between actual and observed performances, a quality-oriented data selection method, which evaluates the quality of data and extracts the most accurate part of it through statistical validation, is designed. Applying this method on the largest publicly available solubility database and using a consensus machine learning approach, a top-performing solubility prediction model is achieved.

The aim of this study is to present a method of assessing eight types of mood that is optimized to every individual on the basis of the heart rate variability (HRV) data which, to eliminate the influence of the inter-individual variability, are measured in a long time period during daily life. Eight types of mood are happiness, tension, fatigue, anxiety, depression, anger, vigor, and confusion. HRV and body accelerations were recorded from nine normal subjects for two months of normal daily life. Fourteen HRV indices were calculated with the HRV data at 512 seconds prior to the time of every mood level report. Data to be analyzed were limited to those with body accelerations of 30 mG (0.294 m/s2) and lower. Further, the differences from the reference values in the same time zone were calculated with both the mood score (Δmood) and HRV index values (ΔHRVI). The multiple linear regression model that estimates Δmood from the scores for principal components of ΔHRVI were then constructed for each individual. The data were divided into training data set and test data set in accordance with the 2-fold cross validation method. Multiple linear regression coefficients were determined using the training data set, and with the optimized model its generalization capability was checked using the test data set. The model was most effective on estimating tension compared with other seven types of mood. The subjects' mean Pearson correlation coefficient was 0.52 with the training data set and 0.40 with the test data set. We proposed a method of assessing mood that is optimized to every individual based on HRV data measured over a long period of daily life.