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

Breast cancer is the second-leading cause of cancer death in women. Breast cells develop into malignant, cancerous lumps, the first signs of breast cancer. Breast cancer can be discovered by the automated diagnostic system when it is still too little to be found by conventional medical methods. Early breast cancers identified with automated screening and diagnosis technologies are generally treatable. This study proposes an enhanced multi-scale deep Convolutional Capsule Neural Network (CapsNet) optimized with Orchard Optimization Algorithm for breast cancer detection. The proposed system consists of preprocessing, feature extraction, segmentation, and classification process. Two input images are taken initially: the Breast Cancer Histopathology Images dataset and the Infrared Thermal Images dataset. The quality of the collected data is improved, and unwanted noises are removed. The features are extracted to segment the image to derive a Region of Interest for effectively segmenting the affected region. Finally, the images are classified as benign/malignant for histopathology images and healthy/cancer for thermal images. The proposed CapsNet is implemented in Python, run for 200 epochs, and compared with existing methods in terms of evaluation metrics. The result shows that the proposed CapsNet attained 99.74 % accuracy, 0.0482 binary entropy loss on the Breast Cancer Histopathology Image dataset and 97 % accuracy, 0.2081 binary entropy loss on the Infrared Thermal Images dataset while incrementing the epochs at each level.

Electromyogram (EMG) signal monitoring is an effective method for controlling the movements of a prosthetic limb. The classification of the EMG pattern of various finger motions in upper-arm amputees has drawn much attention in recent years to develop algorithms that provide adequate accuracy. However, due to the complexity of EMG data, movement detection is a challenging task. Therefore, an effective model is needed that can accurately process, analyze, and classify various hand and finger movements. This paper proposes a novel algorithm for processing and classifying 15 finger movements from surface EMG signals based on Welch power estimation from frequency analysis. Five time-domain features are extracted and trained with a machine learning classifier to classify 15 single fingers and combined finger gestures from eight healthy subjects. The experimental results show 92.30 % classification accuracy considering data from eight channels which was improved to 94.15 % after selecting two channels as dominating. For performance evaluation, 10-fold cross-validation is used during classification. We demonstrate an average accuracy of 92.35 % with 25 % test data.

Care providers in intellectual disability care use various health information systems (HIS) to document the care they provide. This generates a substantial amount of structured and unstructured data with significant potential for reuse, which is currently underexploited. To enhance data reuse, it is important to understand the architecture of health information systems in intellectual disability care, including their commonalities and variabilities (differences), as well as to identify related privacy and security issues. Our study adopts a multiple-case study approach, examining the architectures of four health information systems in the Netherlands. We conducted interviews with seven stakeholders from four HISs and reviewed multiple documents concerning system infrastructure. We identified commonalities and differences between these systems and outlined the primary challenges regarding privacy and security for data reuse. For each HIS, four architectural views were developed: a context diagram, decomposition view, layered view, and deployment view. The study discusses crucial security and privacy aspects for data reuse in intellectual disability care and highlights several challenges that must be addressed to unlock the full potential of this data. This research provides initial guidelines for overcoming these challenges.

Intuitionistic fuzzy sets cannot consider the degree of indeterminacy (i.e., the degree of hesitation). This study presents an intuitionistic fuzzy differential equation approach for the lake water and sediment phosphorus model. We examine the proposed model by assuming generalized trapezoidal intuitionistic fuzzy numbers for the initial condition. Feasible equilibrium points, along with their stability criteria, are evaluated. We describe the characteristics of intuitionistic fuzzy solutions and clarify the difference between strong and weak intuitionistic fuzzy solutions. Numerical simulations are performed in MATLAB to validate the model results.

Reducing hospital readmission rate is a significant challenge in the healthcare industry for managers and policymakers seeking to improve healthcare and lower costs. This study integrates data mining and meta-heuristic techniques to predict the early readmission probability of diabetic patients within 30 days of discharge. The research dataset was obtained from the UC Irvine Machine Learning Repository, including 101765 instances with 50 features representing patient and hospital outcomes, collected from 130 US hospitals. After data preprocessing, including cleansing, sampling, and normalization, a Chi-square analysis is done to confirm and rank the 20 identified factors affecting the readmission risk. As the algorithms' performance could vary based on the features’ characteristics, several classification algorithms, including a Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM), are employed. Moreover, the Genetic Algorithm (GA) is integrated into the SVM algorithm, called GA-SVM, for hyper-parameter tuning and increasing the prediction accuracy. The performance of the models was evaluated using accuracy, recall, precision, and f-measure metrics. The results indicate that the accuracy of RF, GA-SVM, SVM, and NN are calculated respectively as 74.04 %, 73.52 %, 72.40 %, and 70.44 %. Using GA to adjust c and gamma hyper-parameters led to a 1.12 % increase in SVM prediction accuracy. In response to increasing demand and considering poor hospital conditions, particularly during epidemics, these findings point out the potential benefits of a more tailored methodology in managing diabetic patients.

Dengue is a common viral disease in tropical and subtropical countries. The clinical manifestation of dengue has a wide spectrum, from asymptomatic seroconversion to severe dengue infection. Severe dengue is defined as dengue with the presence of specific symptoms, including severe plasma leakage leading to shock or the accumulation of fluids with respiratory distress, severe bleeding, and severe organ impairment. Examining the progression of shock with the integration of patients’ physiological information and biochemical parameters would help in understanding the progression of the disease and early detection of shock. In this study, physiological patient data diagnosed with dengue are collected from a University Malaya Medical Centre’s electronic record. A prediction model learned from the measurement of a patient’s physiological data is the basis for effective treatment and prevention of shock development in critically ill patients. Hence, this study presents the predictive performance of machine learning algorithms to estimate the risk of shock development among dengue patients. Logistic regression, decision trees, support vector machines and neural networks are evaluated. Lastly, ensemble learnings of bagging and boosting are also applied to the weak learner to optimize performance. The experimental results show that the bagging algorithm outperforms other competing methods with a 14.5% improvement from the individual decision tree. The full blood count (FBC) specifically haemoglobin (Hb) on day 2 is found to be a strong predictor for severe dengue occurrence.

Despite its side effects on the global environment, the pandemic has created business opportunities for healthcare. This work utilizes LinkedIn data to examine the features of U.S. healthcare companies that operate within a COVID-19 framework. Data from 304 companies in May 2022 and 333 companies in June 2023 from COVID-19-related companies with LinkedIn presence in the U.S. has been collected and analyzed. This study investigates the distinct characteristics of these companies through statistical measures and analysis at the state level. Some of these companies were established long before the pandemic but shifted their orientation toward COVID-19 in response to the crisis, while many others emerged explicitly due to the pandemic. These companies are primarily active in “Health, wellness and fitness,” “Hospital and healthcare,” Nonprofit organization and management,” “Medical practice,” and “Civic and Social organizations.” We show most companies and employees are located in California, and most followers are in the companies in Washington in the first and California in the second data mining.

Retinal imaging is crucial for observing the retina and accurately diagnosing pathological problems. Optical Coherence Tomography (OCT) has been a transformative breakthrough for developing high-resolution cross-sectional images. It is imperative to delineate the multiple layers of the retina for a proper diagnosis. A novel segmentation-based approach is introduced in this study to identify seven distinct layers of the retina using OCT images. The developed approach presents SliceOCTNet, a customized U-shaped Convolutional Neural Network (CNN) that introduces group normalization and intricate skip connections. Paired alongside a hybrid loss function, the SliceOCTNet outperformed most state-of-the-art approaches. The introduction of Group Normalization in SliceOCTNet stabilized the model and improved layer identification even when working with small datasets. The use of skip connections also contributed to an improvement in the spatial outlook of the model. Implementing a hybrid loss function addresses the class imbalance problem in the dataset. Duke University’s spectral-domain optical coherence tomography (SD-OCT) B-scan dataset of healthy and Diabetic Macular Edema (DME) afflicted patients was utilized to train and evaluate the SliceOCTNet. The model accurately recognizes the seven layers of the retina. It can achieve a high dice coefficient value of 0.941 and refine the segmentation process to a higher level of precision.

Optimization of medication therapy depends on maximizing benefits and minimizing side effects of medications. This research showed how a joint approach using text mining, natural language processing, and machine learning can provide information for personalized and optimized medication therapy. Reviews on the benefits and side effects of prescription and over-the-counter medications were used to determine how well an integrated supervised and unsupervised learning could learn medication satisfaction. Supervised learning with naïve-Bayes, non-linear support vector machine with radial basis function kernels, and random forests with CART decision trees was measured by a micro-aggregated Matthews correlation coefficient and a macro-averaged F1 measure. Random forests outperformed support vector machines by almost 250% and naive-Bayes by 600% on the two evaluation metrics. All models did better with three rating levels, instead of five. Topic modeling and stacked cluster analysis were coupled with parts-of-speech tagging and text mining operations to establish a robust data preprocessing procedure to eliminate noisy features from the data. Unsupervised topic modeling and clustering represented an exploratory validation of how easy supervised classification would be. Well-defined latent topics were discovered including topics on “sleep quality”, “the opportunity to get back to work”, and “weight gain”. Overlapping clusters revealed that incorporating more information on social, demographic, or medical history variables could improve classifier performance. This research provided evidence that medication satisfaction can be learned with carefully designed joint supervised, unsupervised, and natural language learning techniques.

Mind-wandering (MW) is when an individual’s concentration drifts away from the task or activity. Researchers found a greater variability in electroencephalogram (EEG) signals due to MW. Collecting more nuanced information from raw EEG data to examine the harmful effects of MW is time-consuming. This study proposes a multi-resolution assessment of EEG signals using the flexible analytic wavelet transform (FAWT). The FAWT algorithm decomposes raw EEG data into more representative sub-bands (SBs). Several statistical characteristics are derived from the obtained SBs, and the effects of MW during meditation on the EEG signals are investigated. A set of significant characteristics is chosen and fed into the machine learning modules using a 10-fold validation approach to detect MW subjects automatically. Our proposed framework attained the highest classification accuracy of 92.41%, the highest sensitivity of 93.56%, and the highest specificity of 91.97%. The proposed framework can be used to design a suitable brain-computer interface (BCI) system to reduce MW and increase meditation depth for holistic and long-term health in society.