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

Automated segmentation methods can produce faster segmentation of tumors in medical images, aiding medical professionals in diagnosis and treatment plans. A 3D U-Net method excels in this task but has high computational costs due to large model parameters, which limits their application under resource constraints. This study targets an optimized triplanar (2.5D) model ensemble to generate accurate segmentation with fewer parameters. The proposed triplanar model uses spatial and channel attention mechanisms and information from multiple orthogonal planar views to predict segmentation labels. In particular, we studied the optimum filter size to improve the accuracy without increasing the network complexity. The model generated output is further post-processed to fine-tune the segmentation results. The Dice similarity coefficients (Dice-score) of the Brain Tumor Segmentation (BraTS) 2020 training set for enhancing tumor (ET), whole tumor (WT), and tumor core (TC) are 0.736, 0.896, and 0.841, whereas, for the validation set, they are 0.713, 0.873, and 0.778, respectively. The proposed base model has only $10.25M$ parameters, three times less than BraTS 2020’s best-performing model (ET 0.798, WT 0.912, TC 0.857) on the validation set. The proposed ensemble model has $93.5M$ parameters, 1.6 times less than the top-ranked model and two times less than the third-ranked model (ET 0.793, WT 0.911, TC 0.853 on validation set) of BraTS2020 challenge.

Healthcare professionals must provide their patients with the best possible service and be well-informed and expert at carrying out complex surgical procedures to fulfill this responsibility. The aim of the medical treatments is fewer complications, shorter hospital stays, and a better patient experience. Through continuous learning and training, medical practitioners trained in up-to-date and state-of-the-art surgical techniques and technologies make productive and effective healthcare systems possible. Healthcare systems often report on problems with surgical processes, skipped procedures, unusual activities during operations, and lengthy transition times. This event log data allows implementing process mining methods to deliver medical professionals with simple and understandable findings using Petri nets for process analysis and enhancement. This study identifies the parallels and discrepancies between the pre-and post-stages and their respective frequency on each typical Central Venous Catheter (CVC) installation activity. The Process Mining for Python (PM4Py) frameworks used four major mining algorithms to view the event log (i.e., alpha miner, directly-follows graph (DFG), heuristic miner, and inductive miner). This study's findings indicate that medical residents are more susceptible to error during pre-operative procedures.

This study addresses a tumor–macrophage interaction model to examine the role of the saturated response of M2 macrophages. We find the equilibrium point of the model and analyze local stability at each equilibrium. We show that tumor-free equilibrium is always stable, whereas, under certain conditions, the tumor-dominant and interior equilibrium are asymptotically stable. Moreover, stable and unstable limit cycles and period-doubling bifurcation have been observed at the interior equilibrium point. A remarkable result has been observed: in the presence of a saturated response of M2 macrophages, with a relatively higher activation rate of M2 macrophages due to tumor cells, the disease spreads more quickly in the body. Hence, M1 macrophages cannot stabilize the system, and aperiodic oscillations are observed. Furthermore, we show that a better immune response can reverse that system’s unstable nature. Numerical simulations verify the analytical results.

Oral cancer is a form of cancer that develops in the tissue of an oral cavity. Detection at an early stage is necessary to prevent the mortality rate in cancer patients. Artificial intelligence (AI) techniques play a significant role in assisting with diagnosing oral cancer. The AI techniques provide better detection accuracy and help automate oral cancer detection. The study shows that AI has a wide range of algorithms and provides outcomes in the most precise manner possible. We provide an overview of different input types and apply an appropriate algorithm to detect oral cancer. We aim to provide an overview of various AI techniques that can be used to automate oral cancer detection and to analyze these techniques to improve the efficiency and accuracy of oral cancer screening. We provide a summary of various methods available for oral cancer detection. We cover different input image formats, their processing, and the need for segmentation and feature extraction. We further include a list of other conventional strategies. We focus on various AI techniques for detecting oral cancer, including deep learning, machine learning, fuzzy computing, data mining, and genetic algorithms, and evaluates their benefits and drawbacks. The larger part of the articles focused on deep learning (37%) methods, followed by machine learning (32%), genetic algorithms (12%), data mining techniques (10%), and fuzzy computing (9%) for oral cancer detection.

Diabetic retinopathy (DR) involves retina damage due to diabetes, often leading to blindness. It is diagnosed via color fundus injections, but the manual analysis is cumbersome and error-prone. While computer vision techniques can predict DR stages, they are computationally intensive and struggle with complex data extraction. In this research, our prime objective was to automate the process of DR classification into its various stages using convolutional neural network (CNN) models. We employed the performance of fifteen pre-trained models with our novel proposed diabetic retinopathy network (DRNet13) model. We aimed to discern the most efficient model for accurate diabetic retinopathy (DR) staging based on fundus images from five DR classes. We preprocessed the image using a median filter for noise reduction and Gamma correction for image enhancement. We expanded our dataset from 3662 to 7500 images to create a more generalized training model through various augmentation techniques. We also evaluated multiple evaluation metrics, including accuracy, precision, F1-score, Sensitivity, Specificity, Area under the curve (AUC), Mean Squared Error (MSE), False Positive Rate (FPR), False Negative Rate (FNR), in addition to confusion matrices for an in-depth comparison of the performance of these models. Feature maps were employed to illuminate decision making areas in the DRNet13 model, which achieved a 97 % accuracy rate for DR detection, surpassing other CNN architectures in speed and efficiency. Despite a few misclassifications, the model's capability to identify critical features demonstrates its potential as an impactful diagnostic tool for timely and accurate identification of diabetic retinopathy.

This study introduces a novel fractional-order stochastic epidemic model to analyze the spread of conjunctivitis, a prevalent ocular infection, while accounting for the influence of media awareness on disease transmission. The model incorporates fractional derivatives to capture memory effects and non-local interactions inherent in epidemic processes, allowing for a more accurate representation of disease dynamics. The stability analysis of equilibrium points is carried out based on the basic reproduction number ${ℛ}_0$ and fractional-order $\alpha$. Further, the Hopf bifurcation phenomenon is discussed in this paper. Stochasticity accounts for the randomness in transmission events. The findings of this study provide insights into the complex interrelationship between disease dynamics and media influence, shedding light on the role of public awareness in mitigating or exacerbating conjunctivitis outbreaks. The implications of this work extend to public health policy formulation, highlighting the importance of targeted communication strategies in controlling and preventing the spread of conjunctivitis and similar infectious diseases.

This study introduces the first-ever self-explanatory interface for diagnosing diabetes patients using machine learning. We propose four classification models (Decision Tree (DT), K-nearest Neighbor (KNN), Support Vector Classification (SVC), and Extreme Gradient Boosting (XGB)) based on the publicly available diabetes dataset. To elucidate the inner workings of these models, we employed the machine learning interpretation method known as Shapley Additive Explanations (SHAP). All the models exhibited commendable accuracy in diagnosing patients with diabetes, with the XGB model showing a slight edge over the others. Utilising SHAP, we delved into the XGB model, providing in-depth insights into the reasoning behind its predictions at a granular level. Subsequently, we integrated the XGB model and SHAP’s local explanations into an interface to predict diabetes in patients. This interface serves a critical role as it diagnoses patients and offers transparent explanations for the decisions made, providing users with a heightened awareness of their current health conditions. Given the high-stakes nature of the medical field, this developed interface can be further enhanced by including more extensive clinical data, ultimately aiding medical professionals in their decision-making processes.

The coronavirus pandemic is a global health crisis creating an unprecedented socio-economic catastrophe. This pandemic is the biggest challenge the world has faced since World War II and is the main turning point in the history of humanity. Media coverage can change citizens’ attention to emerging infectious diseases and consequently change individual behaviors and attitudes. This study proposes and analyzes a seven-compartmental mathematical model to investigate the impact of media coverage on the spread and control of COVID-19. The threshold condition Ro for the initial transmission of infection is achieved by the next-generation approach. Stability analysis of the proposed model on disease-free and endemic equilibria is investigated in terms of basic reproduction numbers locally and globally. The sensitivity analysis of the reproduction number is visualized to distinguish the most sensitive parameters that can be regulated to control the transmission dynamics of coronavirus disease. Moreover, the theoretical results of the deterministic model are compared using numerical simulations. The outcomes of the analysis suggest that the disease prevalence can be terminated by suitable management of quarantine/medical care. We further extend the model to the optimal control framework. It is analyzed using Pontryagin’s maximum principle to characterize preventive control, testing facility, and treatment measures for managing COVID-19 transmission.

Autistic Spectrum Disorder (ASD) is a neurological disease characterized by difficulties with social interaction, communication, and repetitive activities. While its primary origin lies in genetics, early detection is crucial, and leveraging machine learning offers a promising avenue for a faster and more cost-effective diagnosis. This study employs diverse machine learning methods to identify crucial ASD traits, aiming to enhance and automate the diagnostic process. We study eight state-of-the-art classification models to determine their effectiveness in ASD detection. We evaluate the models using accuracy, precision, recall, specificity, F1-score, area under the curve (AUC), kappa, and log loss metrics to find the best classifier for these binary datasets. Among all the classification models, for the children dataset, the SVM and LR models achieve the highest accuracy of 100% and for the adult dataset, the LR model produces the highest accuracy of 97.14%. Our proposed ANN model provides the highest accuracy of 94.24% for the new combined dataset when hyperparameters are precisely tuned for each model. As almost all classification models achieve high accuracy which utilize true labels, we become interested in delving into five popular clustering algorithms to understand model behavior in scenarios without true labels. We calculate Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and Silhouette Coefficient (SC) metrics to select the best clustering models. Our evaluation finds that spectral clustering outperforms all other benchmarking clustering models in terms of NMI and ARI metrics while demonstrating comparability to the optimal SC achieved by k-means. The implemented code is available at GitHub.

Diabetes is a prevalent chronic condition that poses significant challenges to early diagnosis and identifying at-risk individuals. Machine learning plays a crucial role in diabetes detection by leveraging its ability to process large volumes of data and identify complex patterns. However, imbalanced data, where the number of diabetic cases is substantially smaller than non-diabetic cases, complicates the identification of individuals with diabetes using machine learning algorithms. This study focuses on predicting whether a person is at risk of diabetes, considering the individual’s health and socio-economic conditions while mitigating the challenges posed by imbalanced data. We employ several data augmentation techniques, such as oversampling (Synthetic Minority Over Sampling for Nominal Data, i.e.SMOTE-N), undersampling (Edited Nearest Neighbor, i.e. ENN), and hybrid sampling techniques (SMOTE-Tomek and SMOTE-ENN) on training data before applying machine learning algorithms to minimize the impact of imbalanced data. Our study sheds light on the significance of carefully utilizing data augmentation techniques without any data leakage to enhance the effectiveness of machine learning algorithms. Moreover, it offers a complete machine learning structure for healthcare practitioners, from data obtaining to machine learning prediction, enabling them to make informed decisions.