Predicting PM2.5 atmospheric air pollution using deep learning with meteorological data and ground-based observations and remote-sensing satellite big data.

Air pollution is one of the world's leading factors for early deaths. Every 5 s, someone around the world dies from the adverse health effects of air pollution. In order to mitigate the effects of air pollution, we must first understand it, find its patterns and correlations, and predict it in advance.

Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making.

In the wake of COVID-19 disease, caused by the SARS-CoV-2 virus, we designed and developed a predictive model based on Artificial Intelligence (AI) and Machine Learning algorithms to determine the health risk and predict the mortality risk of patients with COVID-19. In this study, we used a dataset of more than 2,670,000 laboratory-confirmed COVID-19 patients from 146 countries around the world including 307,382 labeled samples. This study proposes an AI model to help hospitals and medical facilities decide who needs to get attention first, who has higher priority to be hospitalized, triage patients when the system is overwhelmed by overcrowding, and eliminate delays in providing the necessary care.

Local Macular Thickness Relationships between 2 OCT Devices.

Purpose: To compare local ganglion cell-inner plexiform layer (GCIPL) thickness measurements between 2 OCT devices and to explore factors that may influence the difference in measurements.

Design: Cross-sectional study.

Participants: Sixty-nine glaucoma eyes (63 patients) with evidence of central damage or mean deviation (MD) of -6.

Feasibility study of an EHR-integrated mobile shared decision making application.

Introduction: Integrating mobile applications (apps) into users' standard electronic health record (EHR) workflows may be valuable, especially for apps that both read and write data. This report details the lessons learned during the integration of a patient decision aid - prostate specific antigen (PSA) testing for prostate cancer screening - into our users' standard EHR workflow for a small usability assessment.

Materials And Methods: This feasibility study included two steps.

A flexible data-driven comorbidity feature extraction framework.

Disease and symptom diagnostic codes are a valuable resource for classifying and predicting patient outcomes. In this paper, we propose a novel methodology for utilizing disease diagnostic information in a predictive machine learning framework. Our methodology relies on a novel, clustering-based feature extraction framework using disease diagnostic information.

Remote Health Monitoring Outcome Success Prediction Using Baseline and First Month Intervention Data.

Remote health monitoring (RHM) systems are becoming more widely adopted by clinicians and hospitals to remotely monitor and communicate with patients while optimizing clinician time, decreasing hospital costs, and improving quality of care. In the Women's heart health study (WHHS), we developed Wanda-cardiovascular disease (CVD), where participants received healthy lifestyle education followed by six months of technology support and reinforcement. Wanda-CVD is a smartphone-based RHM system designed to assist participants in reducing identified CVD risk factors through wireless coaching using feedback and prompts as social support.

A data-driven feature extraction framework for predicting the severity of condition of congestive heart failure patients.

In this paper, we propose a novel methodology for utilizing disease diagnostic information to predict severity of condition for Congestive Heart Failure (CHF) patients. Our methodology relies on a novel, clustering-based, feature extraction framework using disease diagnostic information. To reduce the dimensionality we identify disease clusters using cooccurence frequencies.

Remote health monitoring: predicting outcome success based on contextual features for cardiovascular disease.

Current studies have produced a plethora of remote health monitoring (RHM) systems designed to enhance the care of patients with chronic diseases. Many RHM systems are designed to improve patient risk factors for cardiovascular disease, including physiological parameters such as body mass index (BMI) and waist circumference, and lipid profiles such as low density lipoprotein (LDL) and high density lipoprotein (HDL). There are several patient characteristics that could be determining factors for a patient's RHM outcome success, but these characteristics have been largely unidentified.

Improving compliance in remote healthcare systems through smartphone battery optimization.

Remote health monitoring (RHM) has emerged as a solution to help reduce the cost burden of unhealthy lifestyles and aging populations. Enhancing compliance to prescribed medical regimens is an essential challenge to many systems, even those using smartphone technology. In this paper, we provide a technique to improve smartphone battery consumption and examine the effects of smartphone battery lifetime on compliance, in an attempt to enhance users' adherence to remote monitoring systems.

Accurate tumor localization and tracking in radiation therapy using wireless body sensor networks.

Radiation therapy is an effective method to combat cancerous tumors by killing the malignant cells or controlling their growth. Knowing the exact position of the tumor is a very critical prerequisite in radiation therapy. Since the position of the tumor changes during the process of radiation therapy due to the patient׳s movements and respiration, a real-time tumor tracking method is highly desirable in order to deliver a sufficient dose of radiation to the tumor region without damaging the surrounding healthy tissues.

Accurate localization of in-body medical implants based on spatial sparsity.

Wearable and implantable wireless communication devices have in recent years gained increasing attention for medical diagnostics and therapeutics. In particular, wireless capsule endoscopy has become a popular method to visualize and diagnose the human gastrointestinal tract. Estimating the exact position of the capsule when each image is taken is a very critical issue in capsule endoscopy.

A novel method for medical implant in-body localization.

Wireless communication medical implants are gaining an important role in healthcare systems by controlling and transmitting the vital information of the patients. Recently, Wireless Capsule Endoscopy (WCE) has become a popular method to visualize and diagnose the human gastrointestinal (GI) tract. Estimating the exact location of the capsule when each image is taken is a very critical issue in capsule endoscopy.

Spatial sparsity based indoor localization in wireless sensor network for assistive healthcare.

Indoor localization is one of the key topics in the area of wireless networks with increasing applications in assistive healthcare, where tracking the position and actions of the patient or elderly are required for medical observation or accident prevention. Most of the common indoor localization methods are based on estimating one or more location-dependent signal parameters like TOA, AOA or RSS. However, some difficulties and challenges caused by the complex scenarios within a closed space significantly limit the applicability of those existing approaches in an indoor assistive environment, such as the well-known multipath effect.