Healthcare analytics (New York, N.Y.)最新文献

Tumors result from genetic mutations or environmental factors that prompt cells to divide uncontrollably. This study aims to examine the behavior of tumor-immune cell growth in the presence of chemotherapy drug diffusion at a Caputo fractional order. To accomplish this, we employed the modified homotopy perturbation method to solve a proposed system of nonlinear differential equations. We obtained the analytical solutions to study the spatiotemporal pathological response of tumor-immune cell growth. Our analysis also considered the impact of the Caputo-fractional order on the system's dynamics and compared the results with the classical integer-order scenario. Our findings demonstrated that the proposed method is an effective and precise technique for understanding the intricate interactions of tumor-immune cell growth. Additionally, we revealed that the Caputo-fractional order plays a significant role in the system's behavior and should not be overlooked in future analyses of such systems. In conclusion, this study holds important implications for cancer research by providing insights into the behavior of tumor-immune cell growth in the presence of time-fractional administration of chemotherapy drugs.

Fetal diseases often lead to the death of many babies during pregnancies. Machine learning and deep learning are promising technologies providing efficient and effective detection and treatment of various fetal diseases. We contribute to the medical field by addressing the critical challenge of fetal disease classification, a concern affecting females and infants. This study utilizes 22 features associated with fetal heart rate extracted from 2126 patient records within the Cardiotocography(CTG) datasets. Our classification system offers a cost-effective, efficient, and accurate solution. It classifies fetal diseases into three categories: Normal, Suspect, and Pathological, based on preprocessed data that underwent MinMax Scaling and employed dimensionality reduction techniques, including Principal Component Analysis(PCA) and Autoencoders. By incorporating dimensionality reduction techniques, the computation time has been reduced from 9 to 26 s to just 4 and 15 s, which is less than half of the original computation time. We assessed the performance of 11 standard machine learning algorithms and various performance metrics to identify the best classification model. We have applied the K-fold Cross-Validation technique to validate our model to improve machine learning models and identify the most effective algorithm. When the results are compared, it is observed that Extreme Gradient Boosting (XGBoost) gained the highest accuracy of 0.99% also highest precision 0.93% and outperformed all the other machine learning algorithms.

In this study, we propose a computationally-light and robust neural network for estimating heart rate in remote healthcare systems. We develop a model that can be trained on consumer-grade graphics processing units (GPUs), and can be deployed on edge devices for swift inference. We propose a hybrid model based on convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) architectures for estimating heart rate from Electrocardiogram (ECG) and Photoplethysmography (PPG) signals. Considering the sensitive nature of the ECG signals, we ensure a formal privacy guarantee, differential privacy, for the model training. We perform a tight accounting of the overall privacy budget of our training algorithm using the Rényi Differential Privacy technique. We demonstrate that our model outperforms state-of-the-art networks on a benchmark dataset for both ECG and PPG signals despite having a much smaller number of trainable parameters and, consequently, much smaller training and inference times. Our CNN-BiLSTM architecture can also provide excellent heart rate estimation performance even under strict privacy constraints. We develop a prototype Arduino-based data collection system that is low-cost, efficient, and useful for providing access to modern healthcare services to people living in remote areas.

Skin cancer can be detected through visual examination and confirmed through dermoscopic analysis and various diagnostic tests. This is because visual observation enables early detection of unique skin images by artificial intelligence. Promising outcomes are shown by several Convolution Neural Network (CNN)–based skin lesion classification systems that employ tagged skin images. This study suggests a practical approach for identifying skin cancers using dermoscopy pictures, improving specialists' ability to distinguish benign from malignant tumors. The Swarm Intelligence (SI) approach used dermoscopy photographs to locate lesions on the skin areas Region of interest (ROI). The Grasshopper Optimization technique produced the best segmentation outcomes. The Speed-Up Robust Features (SURF) approach is applied to extract features based on these findings. Two groups were created using the ISIC-2017, ISIC-2018, and PH-2 databases to categorize skin tumors. With an estimated accuracy in classification of 98.52%, preciseness of 96.73%, and Matthews Correlation Coefficient (MCC) of 97.04%, the suggested classification and segmentation methodologies have been evaluated for classification efficacy, specificity, sensitivity, F-measure, preciseness, the MCC, the dice coefficient, and Jaccard's index. In every performance indicator, the method we suggest outperformed state-of-the-art methods.

Cervical cancer is a significant public health concern among females worldwide. Despite being preventable, it remains a leading cause of mortality. Early detection is crucial for successful treatment and improved survival rates. This study proposes an ensemble Machine Learning (ML) classifier for efficient and accurate identification of cervical cancer using medical data. The proposed methodology involves preparing two datasets using effective preprocessing techniques, extracting essential features using the scikit-learn package, and developing an ensemble classifier based on Random Forest, Support Vector Machine, Gaussian Naïve Bayes, and Decision Tree classifier traits. Comparison with other state-of-the-art algorithms using several ML techniques, including support vector machine, decision tree, random forest, Naïve Bayes, logistic regression, CatBoost, and AdaBoost, demonstrates that the proposed ensemble classifier outperforms them significantly, achieving accuracies of 98.06% and 95.45% for Dataset 1 and Dataset 2, respectively. The proposed ensemble classifier outperforms current state-of-the-art algorithms by 1.50% and 6.67% for Dataset 1 and Dataset 2, respectively, highlighting its superior performance compared to existing methods. The study also utilizes a five-fold cross-validation technique to analyze the benefits and drawbacks of the proposed methodology for predicting cervical cancer using medical data. The Receiver Operating Characteristic (ROC) curves with corresponding Area Under the Curve (AUC) values are 0.95 for Dataset 1 and 0.97 for Dataset 2, indicating the overall performance of the classifiers in distinguishing between the classes. Additionally, we employed SHapley Additive exPlanations (SHAP) as an Explainable Artificial Intelligence (XAI) technique to visualize the classifier’s performance, providing insights into the important features contributing to cervical cancer identification. The results demonstrate that the proposed ensemble classifier can efficiently and accurately identify cervical cancer and potentially improve cervical cancer diagnosis and treatment.

The global rise of multi-strain epidemics has raised significant concerns in the field of public health. To address this, our research introduces a game-theoretic approach to predict the evolutionary dynamics of multi-strained pathogens. Our proposed model sheds light on the pivotal role of vaccination in controlling the growth of such infectious diseases. Here, we propose a modified Susceptible-Vaccinated-Infected-Recovered (SVIR) model featuring two strains and corresponding vaccines: one is the primary vaccine that is designed to target the original strain (effectiveness: $e_1$) and simultaneously exhibits some effectiveness against the mutant strain ($e_2$), another is the mutant vaccine that concentrates on the mutant strain (${\eta }_2$) while showing significant effectiveness against the primary strain (${\eta }_1$). Next, we present a comprehensive time series analysis to examine the fraction of the vaccinated population who adopted these two vaccines. This work elucidates that with a slight increase effectiveness- setting $e_1=0.5$, $e_2=0.3$, ${\eta }_1=0.6$, and ${\eta }_2=0.7$- the mutant vaccine works more proficiently under both imitation dynamics known as Individual-Based Risk Assessment (IB-RA) and Strategy-Based Risk Assessment (SB-RA). Furthermore, a detailed analysis comparing these two imitation dynamics is demonstrated and also to reconcile the matter that the Strategy-Based-Risk-Assessment process should be adopted to minimize epidemic size. Finally, considering individuals’ attitudes and behaviors towards vaccination, we introduce a replicator equation. Subsequently, a thorough examination of the relationship between imitation dynamics and behavioral dynamics is presented where imitation dynamics outstripped behavioral dynamics which is confirmed by the use of heat maps.

Brain tumors are life-threatening and are typically identified by experts using imaging modalities like Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET). However, any error due to human intervention in brain anomaly detection can have devastating consequences. This study proposes a tumor detection algorithm for brain MRI images. Previous research into tumor detection has drawbacks, paving the way for further investigations. A visual attention-based technique for tumor detection is proposed to overcome these drawbacks. Brain tumors have a wide range of intensity, varying from inner matter-alike intensity to skull-alike intensity, making them difficult to threshold. Thus, a unique approach to threshold using entropy has been utilized. An on-center saliency map accurately captures the biological visual attention-focused tumorous region from the original image. Later, a superpixel-based framework has been proposed and used to capture the true structure of the tumor. Finally, it was experimentally shown that the proposed algorithm outperforms the existing algorithms for brain tumor detection.

Myocardial infarction is one of the most common cardiovascular diseases in emergency departments. Early prevention of this dangerous condition significantly impacts public health and considerable socioeconomic outcomes. The emergence of electronic health records (EHR) and the availability of real-world clinical data have provided opportunities to improve the quality and efficiency of healthcare by using artificial intelligence tools. In this study, we focus on the early recognition of risk factors, which can provide valuable information for early prediction of myocardial infarction and promoting a healthy life. Based on a big clinical dataset, we develop a predictive analytics approach for myocardial infarction. A vital step in efficient prediction is assessing the significance of input features, their relationships and their contributions to the disease. Therefore, we adopted statistical techniques, principal component analysis (PCA) and feature engineering. To reveal patterns and insights on our dataset, we implemented machine learning (ML) models varying from classical to more sophisticated: decision trees (DT), random forests (RF), gradient boosting algorithms (GBoost, LightGBM, CatBoost, and XGBoost) and deep neural networks (DNN). The imbalance-data issue is tackled by employing random under-sampling technique. The light gradient boosting model (LightGBM) with feature engineering on the balanced dataset is the best prediction performance achieved in this study.

Timely identification of lung nodules, which are precursors to lung cancer, and their evaluation can significantly reduce the incidence rate. Computed Tomography (CT) is the primary technique used for lung cancer screening due to its high resolution. Identifying white, spherical shadows as lung nodules in CT images is essential for accurately detecting lung cancer. Convolutional Neural Network (CNN)-based methods have performed better than traditional techniques in various medical image applications. However, challenges still need to be addressed due to insufficient annotated datasets, significant intra-class variations, and substantial inter-class similarities, which hinder their practical use. Manually labeling the position of nodules on CT slices is critical for distinguishing between benign and malignant cases, but it is an unreliable and time-consuming process. Insufficient data and class imbalance are the primary factors that may result in overfitting and below-par performance. The paper presents a novel Deep Learning (DL) framework to detect and classify lung cancer in input CT images. It introduces a 3D-VNet architecture for accurate segmentation of pulmonary nodules and a 3D-ResNet architecture designed for their classification. The segmentation model achieves a Dice Similarity Coefficient (DSC) of 99.34% on the LUNA16 dataset while reducing false positives to 0.4%. The classification model shows performance metrics with accuracy, sensitivity, and specificity of 99.2%, 98.8%, and 99.6%, respectively. The 3D-VNet network outperforms previous segmentation methods by accurately calibrating lung nodules of various sizes and shapes with excellent robustness. The classification model’s metrics show that the suggested method outperforms current approaches regarding accuracy, specificity, sensitivity and F1-Score.

This study explores the intricacies of the COVID-19 pandemic by employing a four-compartment model with a fractal-fractional derivative based on Caputo concept. The analysis hinges on Schauder fixed point theorem, used to qualitatively examine the solutions and ascertain their existence and uniqueness within the model. The fundamental reproduction number is determined through the next-generation matrix approach. This study delves into the stability of equilibrium points and conducts a sensitivity analysis of model parameters. The equilibrium without infections is locally and globally stable when the basic reproduction number is less than 1. Also, this equilibrium becomes unstable when the basic reproduction number exceeds 1. Applying Lyapunov principles and the Routh–Hurwitz criteria, it is established that the endemic equilibrium point is globally stable for the basic reproduction number values greater than 1. The proposed model incorporates Ulam-Hyers stability through nonlinear functional analysis. Lagrange interpolation method estimates solutions for the fractal-fractional order COVID-19 model. Numerical simulations are performed using MATLAB software to exemplify the model behavior in the context of the Italian case study. Furthermore, fractal-fractional calculus techniques hold significant promise for comprehending and predicting the pandemic’s global dynamics in other countries.