This paper explores inequity in neighborhood walkability at the micro-scale level by qualitatively examining six streetscapes in Pittsburgh, Pennsylvania. A photographic survey is used to highlight differences in the quality and design of the built environment among pairs of streetscapes with high or low social vulnerability but approximately equal quantitative Walk Scores®. The survey revealed discernable differences in the quality and maintenance of the built environment among those in more and less disadvantaged neighborhoods. This was true of several characteristics expected to affect walkability, including enclosure, transparency, complexity, and tidiness. Streetscapes in neighborhoods with high social vulnerability exhibited less contiguous street walls, fewer windows and less transparent storefronts, less well maintained infrastructure, fewer street cafes, and overall less complexity than those in neighborhoods with low social vulnerability. Implications for planning and policy are discussed.

Equity in Microscale Urban Design and Walkability: A Photographic Survey of Six Pittsburgh Streetscapes

Abstract Exploiting existing longitudinal data cohorts can bring enormous benefits to the medical field, as many diseases have a complex and multi-factorial time-course, and start to develop long before symptoms appear. With the increasing healthcare digitisation, the application of machine learning techniques for longitudinal biomedical data may enable the development of new tools for assisting clinicians in their day-to-day medical practice, such as for early diagnosis, risk prediction, treatment planning and prognosis estimation. However, due to the heterogeneity and complexity of time-varying data sets, the development of suitable machine learning models introduces major challenges for data scientists as well as for clinical researchers. This paper provides a comprehensive and critical review of recent developments and applications in machine learning for longitudinal biomedical data. Although the paper provides a discussion of clustering methods, its primary focus is on the prediction of static outcomes, defined as the value of the event of interest at a given instant in time, using longitudinal features, which has emerged as the most commonly employed approach in healthcare applications. First, the main approaches and algorithms for building longitudinal machine learning models are presented in detail, including their technical implementations, strengths and limitations. Subsequently, most recent biomedical and clinical applications are reviewed and discussed, showing promising results in a wide range of medical specialties. Lastly, we discuss current challenges and consider future directions in the field to enhance the development of machine learning tools from longitudinal biomedical data. 

Machine and deep learning for longitudinal biomedical data: a review of methods and applications

Abstract The increase in eye disorders among older individuals has raised concerns, necessitating early detection through regular eye examinations. Age-related macular degeneration (AMD), a prevalent condition in individuals over 45, is a leading cause of vision impairment in the elderly. This paper presents a comprehensive computer-aided diagnosis (CAD) framework to categorize fundus images into geographic atrophy (GA), intermediate AMD, normal, and wet AMD categories. This is crucial for early detection and precise diagnosis of age-related macular degeneration (AMD), enabling timely intervention and personalized treatment strategies. We have developed a novel system that extracts both local and global appearance markers from fundus images. These markers are obtained from the entire retina and iso-regions aligned with the optical disc. Applying weighted majority voting on the best classifiers improves performance, resulting in an accuracy of 96.85%, sensitivity of 93.72%, specificity of 97.89%, precision of 93.86%, F1 of 93.72%, ROC of 95.85%, balanced accuracy of 95.81%, and weighted sum of 95.38%. This system not only achieves high accuracy but also provides a detailed assessment of the severity of each retinal region. This approach ensures that the final diagnosis aligns with the physician’s understanding of AMD, aiding them in ongoing treatment and follow-up for AMD patients. 

pdf/a-concentrated-machine-learning-based-classification-system-czwabaokxe.pdf

A concentrated machine learning-based classification system for age-related macular degeneration (AMD) diagnosis using fundus images

Retinal hemorrhages in pediatric patients can be a diagnostic challenge for ophthalmologists. These hemorrhages can occur due to various underlying etiologies, including abusive head trauma, accidental trauma, and medical conditions. Accurate identification of the etiology is crucial for appropriate management and legal considerations. In recent years, deep learning techniques have shown promise in assisting healthcare professionals in making more accurate and timely diagnosis of a variety of disorders. We explore the potential of deep learning approaches for differentiating etiologies of pediatric retinal hemorrhages. Our study, which spanned multiple centers, analyzed 898 images, resulting in a final dataset of 597 retinal hemorrhage fundus photos categorized into medical (49.9%) and trauma (50.1%) etiologies. Deep learning models, specifically those based on ResNet and transformer architectures, were applied; FastViT-SA12, a hybrid transformer model, achieved the highest accuracy (90.55%) and area under the receiver operating characteristic curve (AUC) of 90.55%, while ResNet18 secured the highest sensitivity value (96.77%) on an independent test dataset. The study highlighted areas for optimization in artificial intelligence (AI) models specifically for pediatric retinal hemorrhages. While AI proves valuable in diagnosing these hemorrhages, the expertise of medical professionals remains irreplaceable. Collaborative efforts between AI specialists and pediatric ophthalmologists are crucial to fully harness AI’s potential in diagnosing etiologies of pediatric retinal hemorrhages.

Deep Learning Approach for Differentiating Etiologies of Pediatric Retinal Hemorrhages: A Multicenter Study

Devices developed for health applications are being driven by technologies such as the Internet of Things (IoT) due to the evolution of hardware and software. In addition, the large amount of information that is generated, in data source devices, is not used properly, using only its computational power to transmit raw data. For this reason, this paper describes how the implementation of an Edge device, together with IoT technologies, allows the processing of vital signs avoiding intensive transmissions to cloud services. This research describes the design and implementation of adaptation, smoothing and filtering processes of the ECG signal to obtain the heart and respiratory rate without the need to use other types of sensors. The results show the need to consider the characteristics of the hardware during the validation stage of the processing algorithms. Due to the limitations of the Broker MQTT server, the transmission is made using the Json format of the breathing rate, heart rate and temperature values to avoid saturating the Web service. 

IoT Edge Device to Estimate Breathing Rate from ECG Signal for Continuous Monitoring

Blood oxygen speed (SpO2) is an indicator of the normal presence or absence of the respiratory function. This is attracting the attention of researchers since it could monitor the patient conditions of the chronic pulmonary diseases and covid-19. Covid-19 patients have the symptom of the significant SpO2 drop. This study tries to develop an early and easy checkup system of the continuous SpO2 using an RGB camera. Unlike the contact SpO2 measurement using the conventional optical sensor on a fingertip, the remote SpO2 sensor system is proposed using the facial video stream. The facial images are trained for the convolutional neural networks to implement the non-contact SpO2 estimation model, which is designed based on the architecture of the conventional remote photoplethysmography model.

Remote SpO2 Estimation using End-to-End CNN Model

Detecting an abrupt distributional shift of the data stream, known as change-point detection, is a fundamental problem in statistics and signal processing. We present a new approach for online change-point detection by training neural networks (NN), and sequentially cumulating the detection statistics by evaluating the trained discriminating function on test samples by a CUSUM recursion. The idea is based on the observation that training neural networks through logistic loss may lead to the log-likelihood function. We demonstrated the good performance of NN-CUSUM in the detection of high-dimensional data using both synthetic and real-world data

Training Neural Networks for Sequential Change-point Detection

Tailoring treatment for individual patients is crucial yet challenging in order to achieve optimal healthcare outcomes. Recent advances in reinforcement learning offer promising personalized treatment recommendations; however, they rely solely on current patient observations (vital signs, demographics) as the patient's state, which may not accurately represent the true health status of the patient. This limitation hampers policy learning and evaluation, ultimately limiting treatment effectiveness. In this study, we propose the Deep Attention Q-Network for personalized treatment recommendations, utilizing the Transformer architecture within a deep reinforcement learning framework to efficiently incorporate all past patient observations. We evaluated the model on real-world sepsis and acute hypotension cohorts, demonstrating its superiority to state-of-the-art models. The source code for our model is available at https://github.com/stevenmsm/RL-ICU-DAQN.

Deep Attention Q-Network for Personalized Treatment Recommendation

Deep neural network (DNN) architectures have been extensively studied for estimating the state-of-health (SOH) of Li-ion batteries (LIBs), but the effects of hyperparameters on their performance have not been thoroughly analyzed. As a result, it is difficult to compare the effectiveness of different DNN architectures and to extend or adapt previous works. We investigate the impact of hyperparameters on the performance of feed forward neural networks for LIB SOH estimation. Specifically, we propose two feed-forward neural networks: an accuracy model optimized for predicting the SOH of a single cell with high accuracy and a generalized model optimized for predicting the SOH of various cells. We demonstrate the effects of hyperparameters on various models using the National Aeronautics and Space Administration (NASA) prognostic battery dataset, which we cleanse to eliminate anomalous data and analyze its features. Our experiments show that our accuracy model achieves a root mean square error of 0.33%, demonstrating its efficacy for predicting the health state of individual cells. We also compare our results with those of other studies, establishing a benchmark for future research. Our open resources, including code, cleaned data, feature analysis, and experimental reports, are publicly available on GitHub (https://github.com/jhrrlee/batterystates).

Li-ion Battery Health State Estimation with Two Feed Forward Neural Networks

Change-point detection, detecting an abrupt change in the data distribution from sequential data, is a fundamental problem in statistics and machine learning. CUSUM is a popular statistical method for online change-point detection due to its efficiency from recursive computation and constant memory requirement, and it enjoys statistical optimality. CUSUM requires knowing the precise pre- and post-change distribution. However, post-change distribution is usually unknown a priori since it represents anomaly and novelty. When there is a model mismatch with actual data, classic CUSUM can perform poorly. While likelihood ratio-based methods encounter challenges in high dimensions, neural networks have become an emerging tool for change-point detection with computational efficiency and scalability. In this paper, we introduce a neural network CUSUM (NN-CUSUM) for online change-point detection. We also present a general theoretical condition when the trained neural networks can perform change-point detection and what losses can achieve our goal. We further extend our analysis by combining it with the Neural Tangent Kernel theory to establish learning guarantees for the standard performance metrics, including the average run length (ARL) and expected detection delay (EDD). The strong performance of NN-CUSUM is demonstrated in detecting change-point in high-dimensional data using both synthetic and real-world data.

Neural Network-based CUSUM for Online Change-point Detection

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