Classical Logistic Regression Model Research Articles

Performance of Radiomics Based Multi-Parameter Models Derived from Lung CT in Differentiating Benign Non-Inflammatory, Inflammatory and Malignant Pulmonary Nodule

Pulmonary nodules are a significant clinical issue that require accurate and efficient diagnosis. This study constructed a machine learning model, combining radiomics features of chest CT sequences with three types of microvascular density (MVD) values, to differentiate benign, inflammatory and malignant pulmonary nodules. A total of 100 patients with lung nodules on CT images and corresponding pathological results were retrospectively included in the study. The MVD values and radiomics features were calculated and extracted based on the segmented nodules. Univariate correlation analysis and principal component analysis were performed to select radiomics features. Combined MVD values and selected radiomics features, we conducted a logistic regression classification model. The area under the curve (AUC) was applied to show model performance. Our model reached an AUC value of 0.867 when tested on independent datasets. The performance of the model to differentiate benign, malignant and inflammatory nodules reached AUC values of only 0.908, 0.833, and 0.730, respectively. We conducted a prediction model that shows promising results in distinguishing three different types of lung nodules.

Anomaly prediction of CT equipment based on IoMT data

BackgroundLarge-scale medical equipment, which is extensively implemented in medical services, is of vital importance for diagnosis but vulnerable to various anomalies and failures. Most hospitals that conduct regular maintenance have been suffering from medical equipment-related incidents for years. Currently, the Internet of Medical Things (IoMT) has emerged as a crucial tool in monitoring the real-time status of the medical equipment. In this paper, we develop an IoMT system of Computed Tomography (CT) equipment in the West China Hospital, Sichuan University and collected the system status time-series data. Novel multivariate time-series classification models and frameworks are proposed to predict the anomalies of CT equipment. The important features that are closely related to the equipment anomalies are identified with the model.MethodsWe extracted the real-time CT equipment status time-series data of 11 equipment between May 19, 2020 and May 19, 2021 from the IoMT, which includes the equipment oil temperature, anode voltage, etc. The arcs are identified as labels of anomalies due to their relationship with decreased imaging quality and CT equipment failures. To improve prediction accuracy, the statistics and transformations of the raw historical time-series data segment in the sliding time window are used to construct new features. Due to the particularity of time-series data, two frameworks are proposed for splitting the training and test sets. Then the Decision Tree, Support Vector Machine, Logistic Regression, Naive Bayesian, and K-Nearest Neighbor classification models are used to classify the system status. We also compare our model to state-of-the-art models.ResultsThe results show that the anomaly prediction accuracy and recall of our method are 79% and 77%, respectively. The oil temperature and anode voltage are identified as the decisive features that may lead to anomalies. The proposed model outperforms the others when predicting the anomalies of the CT equipment based on our dataset.ConclusionsThe proposed method could predict the state of CT equipment and be used as a reference for practical maintenance, where unexpected anomalies of medical equipment could be reduced. It also brings new insights into how to handle non-uniform and imbalanced time series data in practical cases.

UPLC-QDA and machine learning for distinguishing different commodity specifications of Fritillariae Cirrhosae Bulbus and application of data augmentation technology

This study aimed to establish a method based on machine learning technology for accurately predicting the commodity specifications of Fritillariae Cirrhosae Bulbus and explore the application of data augmentation technology in the field of drug analysis. The correlation optimized warping(COW) algorithm was used to perform peak calibration on the UPLC-QDA multi-channel superimposed data of 30 batches of samples, and the data were normalized. Through unsupervised learning methods such as clustering analysis, principal component analysis(PCA), and correlation analysis, the general characteristics of the data were understood. Then, the logistic regression algorithm was used for supervised learning on the data, and the condition tabular generative adversarial networks(CTGAN) was used to generate a large amount of data. Logistic regression classification models were trained separately using the real data and the data generated by CTGAN, and these models were evaluated. The logistic regression model trained with real data achieved cross-validation and test set accuracies of 0.95 and 1.00, respectively, while the logistic regression model trained with both real and CTGAN-generated data achieved cross-validation and test set accuracies of 0.99 and 1.00, respectively. The results indicate that machine learning can accurately predict the classification of Songbei, Qingbei, and Lubeibased on UPLC-QDA detection data. CTGAN-generated data can partially compensate for the lack of data in drug analysis, improving the accuracy and predictive ability of machine learning models.

Regularized Machine Learning Models for Prediction of Metabolic Syndrome Using GCKR, APOA5, and BUD13 Gene Variants: Tehran Cardiometabolic Genetic Study.

Metabolic syndrome (MetS) is a complex multifactorial disorder that considerably burdens healthcare systems. We aim to classify MetS using regularized machine learning models in the presence of the risk variants of GCKR, BUD13 and APOA5, and environmental risk factors. A cohort study was conducted on 2,346 cases and 2,203 controls from eligible Tehran Cardiometabolic Genetic Study (TCGS) participants whose data were collected from 1999 to 2017. We used different regularization approaches [least absolute shrinkage and selection operator (LASSO), ridge regression (RR), elasticnet (ENET), adaptive LASSO (aLASSO), and adaptive ENET (aENET)] and a classical logistic regression (LR) model to classify MetS and select influential variables that predict MetS. Demographics, clinical features, and common polymorphisms in the GCKR, BUD13 and APOA5 genes of eligible participants were assessed to classify TCGS participant status in MetS development. The models' performance was evaluated by 10-repeated 10-fold crossvalidation. Various assessment measures of sensitivity, specificity, classification accuracy, and area under the receiver operating characteristic curve (AUC-ROC) and AUC-precision-recall (AUC-PR) curves were used to compare the models. During the follow-up period, 50.38% of participants developed MetS. The groups were not similar in terms of baseline characteristics and risk variants. MetS was significantly associated with age, gender, schooling years, body mass index (BMI), and alternate alleles in all the risk variants, as indicated by LR. A comparison of accuracy, AUCROC, and AUC-PR metrics indicated that the regularization models outperformed LR. Regularized machine learning models provided comparable classification performances, whereas the aLASSO model was more parsimonious and selected fewer predictors. Regularized machine learning models provided more accurate and parsimonious MetS classifying models. These high-performing diagnostic models can lay the foundation for clinical decision support tools that use genetic and demographical variables to locate individuals at high risk for MetS.

Establishment risk of invasive golden mussel in a water diversion project: An assessment framework

Inter-basin water diversion projects have led to accelerated colonization of aquatic organisms, including the freshwater golden mussel (Limnoperna fortunei), exacerbating global biofouling concerns. While the influence of environmental factors on the mussel's invasion and biofouling impact has been studied, quantitative correlations and underlying mechanisms remain unclear, particularly in large-scale inter-basin water diversion projects with diverse hydrodynamic and environmental conditions. Here, we examine the comprehensive impact of environmental variables on the establishment risk of the golden mussel in China's 1432-km-long Middle Route of the South-to-North Water Diversion Project. Logistic regression and multiclass classification models were used to investigate the environmental influence on the occurrence probability and reproductive density of the golden mussel. Total nitrogen, ammonia nitrogen, water temperature, pH, and velocity were identified as crucial environmental variables affecting the biofouling risk in the project. Logistic regression analysis revealed a negative correlation between the occurrence probability of all larval stages and levels of total nitrogen and ammonia nitrogen. The multiclass classification model showed that elevated levels of total nitrogen hindered mussel reproduction, while optimal water temperature enhanced their reproductive capacity. Appropriate velocity and pH levels were crucial in maintaining moderate larval density. This research presents a quantitative analytical framework for assessing establishment risks associated with invasive mussels, and the framework is expected to enhance invasion management and mitigate biofouling issues in water diversion projects worldwide.

Neuronally enriched microvesicle RNAs are differentially expressed in the serums of Parkinson's patients.

Circulating small RNAs (smRNAs) originate from diverse tissues and organs. Previous studies investigating smRNAs as potential biomarkers for Parkinson's disease (PD) have yielded inconsistent results. We investigated whether smRNA profiles from neuronally-enriched serum exosomes and microvesicles are altered in PD patients and discriminate PD subjects from controls. Demographic, clinical, and serum samples were obtained from 60 PD subjects and 40 age- and sex-matched controls. Exosomes and microvesicles were extracted and isolated using a validated neuronal membrane marker (CD171). Sequencing and bioinformatics analyses were used to identify differentially expressed smRNAs in PD and control samples. SmRNAs also were tested for association with clinical metrics. Logistic regression and random forest classification models evaluated the discriminative value of the smRNAs. In serum CD171 enriched exosomes and microvesicles, a panel of 29 smRNAs was expressed differentially between PD and controls (false discovery rate (FDR) < 0.05). Among the smRNAs, 23 were upregulated and 6 were downregulated in PD patients. Pathway analysis revealed links to cellular proliferation regulation and signaling. Least absolute shrinkage and selection operator adjusted for the multicollinearity of these smRNAs and association tests to clinical parameters via linear regression did not yield significant results. Univariate logistic regression models showed that four smRNAs achieved an AUC ≥ 0.74 to discriminate PD subjects from controls. The random forest model had an AUC of 0.942 for the 29 smRNA panel. CD171-enriched exosomes and microvesicles contain the differential expression of smRNAs between PD and controls. Future studies are warranted to follow up on the findings and understand the scientific and clinical relevance.

Effect of insulin resistance on left ventricular remodelling in essential hypertensives: a cross-sectional study.

In clinical practice, left ventricular hypertrophy (LVH) is defined by physical findings and electrocardiographic criteria, which are useful but imperfect tools, echocardiographic criteria and cardiac magnetic resonance imaging. In echocardiography, LVH is defined not by left ventricular wall thicknesses but by left ventricular mass. The latter is calculated according to Devereux's formula, and is increased by insulin resistance/hyperinsulinaemia. It is however unclear whether insulin resistance, hyperinsulinaemia, or both, is actually causative and what their collective or individual influence is on the components of Devereux's formula and parameters of left ventricular diastolic function. This study evaluated the associations of the homeostatic model assessment for insulin resistance (HOMAIR) and fasting plasma insulin levels with components of Devereux's formula and parameters of left ventricular diastolic function. Relevant clinical data were collected from 220 hypertensive patients recruited between January and December 2019. The associations of components of Devereux's formula and parameters of diastolic function with insulin resistance were tested using binary ordinal, conditional and classical logistic regression models. Thirty-two (14.5%) patients (43.9 ± 9.1 years), 99 (45%) patients (52.4 ± 8.7 years) and 89 (40.5%) patients (53.1 ± 9.8 years) had normal left ventricular geometry, concentric left ventricular remodelling and concentric left ventricular hypertrophy, respectively. In multivariable adjusted analysis, 46.8% of variation in interventricular septum diameter (R² = 0.468; overall p = 0.001) and 30.9% of E-wave deceleration time (R² = 0.309; overall p = 0.003) were explained by insulin level and HOMAIR, 30.1% of variation in left ventricular end-diastolic diameter (R² = 0.301; p = 0.013) by HOMAIR alone, and 46.3% of posterior wall thickness (R² = 0.463; p = 0.002) and 29.4% of relative wall thickness (R² = 0.294; p = 0.007) by insulin level alone. Insulin resistance and hyperinsulinaemia did not have the same influence on the components of Devereux's formula. Insulin resistance appeared to act on left ventricular end-diastolic diameter, while hyperinsulinaemia affected the posterior wall thickness. Both abnormalities acted on the interventricular septum and contributed to diastolic dysfunction via the E-wave deceleration time.

Efficient e-mail spam filtering approach combining Logistic Regression model and Orthogonal Atomic Orbital Search algorithm

Phishing emails called spam have created a need for reliable and intelligent spam filters. Machine-learning techniques are effective, but current methods such as Logistic Regression (LR), Support Vector Machine (SVM), Decision Trees (DT), and Naive Bayes (NB) sometimes produce low detection rates and struggle with large amounts of data. Motivated by these concerns, we propose an efficient spam-filtering approach, OAOS-LR, combining an improved Atomic Orbital Search (AOS) algorithm with an LR classification model. To remove the deficiency of low detection rate produced by the standard LR method due to the utilization of the gradient descent technique, we train it with our proposed OAOS approach, which uses AOS and Orthogonal learning to enhance the search capabilities of the conventional algorithm. In the experimental study, we first evaluated the performance of OAOS over the IEEE Congress on Evolutionary Computation (CEC’20) benchmarks against five different metaheuristics to prove its effectiveness in improving its convergence rate and reducing the probability of falling in local optima. After that, the proposed technique LR-OAOS was applied to the spam filtering problem using CSDMC2010 and Enron datasets and tested against the most recent machine learning and metaheuristic approaches using standard statistical measures and plots. OAOS-LR significantly outperformed other methods with an average F1-score success rate of 95.45% and 96.30% on CSDMC2010, and 74.80% and 78.33% on Enron, respectively with the number of feature spaces equal to 500 and 1000.

A machine learning approach that measures pH using acidoCEST MRI of iopamidol.

Tumor acidosis is an important biomarker for aggressive tumors, and extracellular pH (pHe) of the tumor microenvironment can be used to predict and evaluate tumor responses to chemotherapy and immunotherapy. AcidoCEST MRI measures tumor pHe by exploiting the pH-dependent chemical exchange saturation transfer (CEST) effect of iopamidol, an exogenous CT agent repurposed for CEST MRI. However, all pH fitting methodologies for acidoCEST MRI data have limitations. Herein we present results of the application of machine learning for extracting pH values from CEST Z-spectra of iopamidol. We acquired 36,000 experimental CEST spectra from 200 phantoms of iopamidol prepared at five concentrations, five T1 values, and eight pH values at five temperatures, acquired at six saturation powers and six saturation times. We also acquired T1 , T2 , B1 RF power, and B0 magnetic field strength supplementary MR information. These MR images were used to train and validate machine learning models for the tasks of pH classification and pH regression. Specifically, we tested the L1-penalized logistic regression classification (LRC) model and the random forest classification (RFC) model for classifying the CEST Z-spectra for thresholds at pH 6.5 and 7.0. Our results showed that both RFC and LRC were effective for pH classification, although the RFC model achieved higher predictive value, and improved the accuracy of classification accuracy with CEST Z-spectra with a more limited set of saturation frequencies. Furthermore, we used LASSO and random forest regression (RFR) models to explore pH regression, which showed that the RFR model achieved higher accuracy and precision for estimating pH across the entire pH range of 6.2-7.3, especially when using a more limited set of features. Based on these results, machine learning for analysis of acidoCEST MRI is promising for eventual in vivo determination of tumor pHe.

Quantification of vascular morphology in optical coherence tomography angiography in primary open angle glaucoma

PurposeTo quantitatively measure and compare the vascular morphology in healthy eyes and eyes with primary open-angle glaucoma (POAG) using optical coherence tomography angiography (OCTA) scans. MethodsThis is a retrospective and cross-sectional study which include healthy individuals and individuals with POAG that underwent OCTA imaging at an academic center's glaucoma clinic. We analyzed OCTA scans of the macula and optic nerve head (ONH) of one eye from each subject to quantitatively measure vessel density (VD), vessel length density (VLD), and branchpoint density (BPD). We compared these 3 parameters between the healthy and POAG groups and used logistic regression classification models to determine their diagnostic value in differentiating healthy and glaucomatous eyes. ResultsWe included 49 healthy subjects and 49 subjects with POAG. After age-adjusted analysis, the parameters of VD, VLD, and BPD were significantly reduced in eyes with POAG (P ​< ​0.001) in all scan layers and most significantly around the ONH. The parameter with the best performances were radial peripapillary capillary (RPC) VD [AUC (areas under the curve): 0.939 (0.891, 0.987)] which had statistically higher performances (P ​< ​0.05) than parameters in the superficial or deep layers. All 3 parameters in the RPC layer had statistically similar performances. ConclusionsWe found that VD, VLD, and BPD were reduced in glaucomatous eyes. The morphologic parameters of VLD and BPD had similar performances to the traditional parameter of VD in RPC layers. Our results suggest that vascular morphology parameters may provide additional value in the diagnosis and evaluation of glaucoma.

Double burden of malnutrition among women of reproductive age in Bangladesh: A comparative study of classical and Bayesian logistic regression approach.

Although the prevalence of undernutrition among women of reproductive age has declined in Bangladesh, the increase in the prevalence of overnutrition remains a major challenge. To achieve Sustainable Development Goal 2.2, it is important to identify the drivers of the double burden of malnutrition on women in Bangladesh. The Bangladesh Demographic and Health Survey, 2017-2018 was used to model the relationship between the double burden of malnutrition among women and the risk factors using a logistic regression model under the classical and Bayesian frameworks and performed the comparison between the regression models based on the narrowest confidence interval. Regarding the Bayesian application, the Metropolis-Hastings algorithm with two types of prior information (historical and noninformative prior) was used to simulate parameter estimates from the posterior distributions. The Boruta algorithm was used to determine the significant predictors. Almost half of reproductive aged women experienced a form of malnutrition (12% were underweight, 26.1% were overweight, and 6.8% were obese). In terms of the narrowest interval estimate, it was found that Bayesian logistic regression with informative priors performs better than the noninformative priors and the classical logistic regression model. Women who were older, highly educated, from rich families, unemployed, and from urban residences were more likely to experience the double burden of malnutrition. This study recommended using the historical prior as the informative prior rather than the flat/noninformative prior to estimating the parameter uncertainty if historical data are available. The double burden of malnutrition among women is a major public health challenge in Bangladesh. This study was to determine the impact of effective risk factors on the double burden of malnutrition among women by applying the Bayesian framework. Using both informative and noninformative priors, "historical prior" was proposed as informative prior information. The main strength is that the proposed prior (historical prior) provided improved estimation as compared to the flat prior distribution.

Online Chatter Identification for Thin-Walled Parts Machining Based on Improved Multisensor Signal Fusion and Multiscale Entropy

Thin-walled workpieces frequently appear to chatter during milling, causing the creation of vibration patterns on the surface of the workpiece, which affects the machining quality and workpiece performance. Identification of online chatter is important for thin-walled parts. The vibration signal associated with chatter changes dynamically throughout the milling process due to changes in tool position, removal of workpiece material, and transmission-related vibration signal attenuation characteristics, making reliable chatter identification difficult. This research proposes a unique online chatter identification approach for the machining of thin-walled parts based on improved multi-sensor signal fusion and multi-scale entropy. In order to acquire the fused vibration signals, the vibration signals produced during the machining of thin-walled parts are first acquired by sensors at various test sites and adaptively given weight coefficients by the improved Hausdorff distance and distance factor. Second, using the multi-scale sample entropy (MSSE) and multi-scale weighted permutation entropy (MSWPE), the eigenvalues of the fused vibration signals are obtained. Finally, the trained Logistic regression (LR) classification model is used to determine the vibration status of thin-walled parts. The analysis’s results demonstrated that the technique can properly identify the vibration state of thin-walled parts, and that the fusion signal can reflect the vibration state at the tool contact site more accurately.

Machine-learning-based risk stratification for probability of dying in patients with basal ganglia hemorrhage

To confirm whether machine learning algorithms (MLA) can achieve an effective risk stratification of dying within 7 days after basal ganglia hemorrhage (BGH). We collected patients with BGH admitted to Sichuan Provincial People’s Hospital between August 2005 and August 2021. We developed standard ML-supervised models and fusion models to assess the prognostic risk of patients with BGH and compared them with the classical logistic regression model. We also use the SHAP algorithm to provide clinical interpretability. 1383 patients with BGH were included and divided into the conservative treatment group (CTG) and surgical treatment group (STG). In CTG, the Stack model has the highest sensitivity (78.5%). In STG, Weight-Stack model achieves 58.6% sensitivity and 85.1% specificity, and XGBoost achieves 61.4% sensitivity and 82.4% specificity. The SHAP algorithm shows that the predicted preferred characteristics of the CTG are consciousness, hemorrhage volume, prehospital time, break into ventricles, brain herniation, intraoperative blood loss, and hsCRP were also added to the STG. XGBoost, Stack, and Weight-Stack models combined with easily available clinical data enable risk stratification of BGH patients with high performance. These ML classifiers could assist clinicians and families to identify risk states timely when emergency admission and offer medical care and nursing information.

Radiomics-based machine learning approach in differentiating fibro-adipose vascular anomaly from venous malformation.

As a complex vascular malformation, fibro-adipose vascular anomaly was first proposed in 2014. Its overlap with other vascular malformations regarding imaging and clinical features often leads to misdiagnosis and improper management. To construct a radiomics-based machine learning model to help radiologists differentiate fibro-adipose vascular anomaly from common venous malformations. We retrospectively analyzed 178 children, adolescents and young adults with vascular malformations (41 fibro-adipose vascular anomaly and 137 common vascular malformation cases) who underwent MRI before surgery between May 2012 to January 2021. We extracted radiomics features from T1-weighted images and fat-saturated (FS) T2-weighted images and further selected features through least absolute shrinkage and selection operator (LASSO) and Boruta methods. We established eight weighted logistic regression classification models based on various combinations of feature-selection strategies (LASSO or Boruta) and sequence types (single- or multi-sequence). Finally, we evaluated the performance of each model by the mean area under the receiver operating characteristics curve (ROC-AUC), sensitivity and specificity in 10 runs of repeated k-fold (k = 10) cross-validation. Two multi-sequence models based on axial FS T2-W, coronal FS T2-W and axial T1-W images showed promising performance. The LASSO-based multi-sequence model achieved an AUC of 97%±3.8, a sensitivity of 94%±12.4 and a specificity of 89%±9.0. The Boruta-based multi-sequence model achieved an AUC of 97%±3.7, a sensitivity of 95%±10.5 and a specificity of 87%±9.0. The radiomics-based machine learning model can provide a promising tool to help distinguish fibro-adipose vascular anomaly from common venous malformations.

Predicting Health Risks of Adult Asthmatics Susceptible to Indoor Air Quality Using Improved Logistic and Quantile Regression Models.

The increasing global patterns for asthma disease and its associated fiscal burden to healthcare systems demand a change to healthcare processes and the way asthma risks are managed. Patient-centered health care systems equipped with advanced sensing technologies can empower patients to participate actively in their health risk control, which results in improving health outcomes. Despite having data analytics gradually emerging in health care, the path to well established and successful data driven health care services exhibit some limitations. Low accuracy of existing predictive models causes misclassification and needs improvement. In addition, lack of guidance and explanation of the reasons of a prediction leads to unsuccessful interventions. This paper proposes a modeling framework for an asthma risk management system in which the contributions are three fold: First, the framework uses a deep learning technique to improve the performance of logistic regression classification models. Second, it implements a variable sliding window method considering spatio-temporal properties of the data, which improves the quality of quantile regression models. Lastly, it provides a guidance on how to use the outcomes of the two predictive models in practice. To promote the application of predictive modeling, we present a use case that illustrates the life cycle of the proposed framework. The performance of our proposed framework was extensively evaluated using real datasets in which results showed improvement in the model classification accuracy, approximately 11.5–18.4% in the improved logistic regression classification model and confirmed low relative errors ranging from 0.018 to 0.160 in quantile regression model.

Liver Fibrosis Assessment Using Radiomics of Ultrasound Homodyned-K imaging Based on the Artificial Neural Network Estimator.

The homodyned-K distribution is an important ultrasound backscatter envelope statistics model of physical meaning, and the parametric imaging of the model parameters has been explored for quantitative ultrasound tissue characterization. In this paper, we proposed a new method for liver fibrosis characterization by using radiomics of ultrasound backscatter homodyned-K imaging based on an improved artificial neural network (iANN) estimator. The iANN estimator was used to estimate the ultrasound homodyned-K distribution parameters k and α from the backscattered radiofrequency (RF) signals of clinical liver fibrosis (n = 237), collected with a 3-MHz convex array transducer. The RF data were divided into two groups: Group I corresponded to liver fibrosis with no hepatic steatosis (n = 94), and Group II corresponded to liver fibrosis with mild to severe hepatic steatosis (n = 143). The estimated homodyned-K parameter values were then used to construct k and α parametric images using the sliding window technique. Radiomics features of k and α parametric images were extracted, and feature selection was conducted. Logistic regression classification models based on the selected radiomics features were built for staging liver fibrosis. Experimental results showed that the proposed method is overall superior to the radiomics method of uncompressed envelope images when assessing liver fibrosis. Regardless of hepatic steatosis, the proposed method achieved the best performance in staging liver fibrosis ≥F1, ≥F4, and the area under the receiver operating characteristic curve was 0.88, 0.85 (Group I), and 0.85, 0.86 (Group II), respectively. Radiomics has improved the ability of ultrasound backscatter statistical parametric imaging to assess liver fibrosis, and is expected to become a new quantitative ultrasound method for liver fibrosis characterization.

Development of a clinical prediction model for recurrence and mortality outcomes after Clostridioides difficile infection using a machine learning approach

ObjectivesClostridioides difficile infection (CDI) is associated with a large burden of morbidity and mortality worldwide. Previous studies have developed models for predicting recurrence and mortality following CDI, but no machine learning predictive models have been developed specifically using data from Japanese patients. MethodsUsing a database of records from acute care hospitals in Japan, we extracted records from January 2012 to September 2016 (plus a 60-day lookback window). A total of 19,159 patients were included. We used a machine learning approach, XGBoost, and compared it to a traditional unregularized logistic regression model. The first 80% of the dataset (by patient index date) was used to optimize model hyperparameters and train the final models, and evaluation was performed on the remaining 20%. We measured model performance by the area under the receiver operator curve and assessed feature importance using Shapley additive explanations. ResultsPerformance was similar between the machine learning approach and the classical logistic regression model. Logistic regression performed slightly better than XGBoost for predicting mortality. ConclusionXGBoost performed slightly better than logistic regression for predicting recurrence, but it was not competitive with existing published models. Despite this, a future machine learning-based application provided in a bedside setting at low cost might be a clinically useful tool.

Leveraging Regio- and Stereoselective C(sp3)-H Functionalization of Silyl Ethers to Train a Logistic Regression Classification Model for Predicting Site-Selectivity Bias.

The C-H functionalization of silyl ethers via carbene-induced C-H insertion represents an efficient synthetic disconnection strategy. In this work, site- and stereoselective C(sp3)-H functionalization at α, γ, δ, and even more distal positions to the siloxy group has been achieved using donor/acceptor carbene intermediates. By exploiting the predilections of Rh2(R-TCPTAD)4 and Rh2(S-2-Cl-5-BrTPCP)4 catalysts to target either more electronically activated or more spatially accessible C-H sites, respectively, divergent desired products can be formed with good diastereocontrol and enantiocontrol. Notably, the reaction can also be extended to enable desymmetrization of meso silyl ethers. Leveraging the broad substrate scope examined in this study, we have trained a machine learning classification model using logistic regression to predict the major C-H functionalization site based on intrinsic substrate reactivity and catalyst propensity for overriding it. This model enables prediction of the major product when applying these C-H functionalization methods to a new substrate of interest. Applying this model broadly, we have demonstrated its utility for guiding late-stage functionalization in complex settings and developed an intuitive visualization tool to assist synthetic chemists in such endeavors.

Ultrasound Radiomics-Based Logistic Regression Model to Differentiate Between Benign and Malignant Breast Nodules.

To explore the potential value of ultrasound radiomics in differentiating between benign and malignant breast nodules by extracting the radiomic features of two-dimensional (2D) grayscale ultrasound images and establishing a logistic regression model. The clinical and ultrasound data of 1000 female patients (500 pathologically benign patients, 500 pathologically malignant patients) who underwent breast ultrasound examinations at our hospital were retrospectively analyzed. The cases were randomly divided into training and validation sets at a ratio of 7:3. Once the region of interest (ROI) of the lesion was manually contoured, Spearman's rank correlation, least absolute shrinkage and selection operator (LASSO) regression, and the Boruta algorithm were adopted to determine optimal features and establish a logistic regression classification model. The performance of the model was assessed using the area under the receiver operating characteristic curve (AUC), and calibration and decision curves (DCA). Eight ultrasound radiomic features were selected to establish the model. The AUC values of the model were 0.979 and 0.977 in the training and validation sets, respectively (P=.0029), indicating good discriminative ability in both datasets. Additionally, the calibration and DCAsuggested that the model's calibration efficiency and clinical application value were both superior. The proposed logistic regression model based on 2D grayscale ultrasound images could facilitate differential diagnosis of benign and malignant breast nodules. The model, which was constructed using ultrasound radiomic features identified in this study, demonstrated good diagnostic performance and could be useful in helping clinicians formulate individualized treatment plans for patients.

Using an Interpretable Machine Learning Framework to Understand the Relationship of Mobility and Reliability Indices on Truck Drivers’ Route Choices

This paper explores the relationships between travel time index and planning time index, two proven indices used to measure real-time congestion and travel time reliability, and their impacts on truck drivers’ route choices through training of a predictive model based on a machine learning algorithm—eXtreme Gradient Boost (XGBoost). Moreover, this study adopts an interpretable machine learning framework called SHapley Additive ExPlanation (SHAP) in the predictive model to reveal the insights usually hidden inside the machine learning “black box.” The predictive model is trained through a truck trajectory dataset provided by the Maryland Department of Transportation State Highway Administration and INRIX. The classical logistic regression model is adopted as the baseline model. The results show the XGBoost model can better handle nonlinearity and provide more reliable predictions. Through the SHAP framework, the results indicate that mobility and reliability indices and total trip time nonlinearly influence route choices. Truck drivers are more sensitive to real-time congestion information and reliability information when the differences in mobility and reliability indices on candidate routes reach certain thresholds. Moreover, the interaction study on trip time and mobility index shows that truck drivers are more sensitive to real-time congestion information if the candidate routes’ travel time is a larger portion of the total trip time.