Population analysis of mortality risk: Predictive models from passive monitors using motion sensors for 100,000 UK Biobank participants.

Many studies have utilized physical activity for predicting mortality risk, using measures such as participant walk tests and self-reported walking pace. The rise of passive monitors to measure participant activity without requiring specific actions opens the possibility for population level analysis. We have developed novel technology for this predictive health monitoring, using limited sensor inputs.

Evaluation of Machine Learning Models for Classifying Upper Extremity Exercises Using Inertial Measurement Unit-Based Kinematic Data.

The amount of home-based exercise prescribed by a physical therapist is difficult to monitor. However, the integration of wearable inertial measurement unit (IMU) devices can aid in monitoring home exercise by analyzing exercise biomechanics. The objective of this study is to evaluate machine learning models for classifying nine different upper extremity exercises, based upon kinematic data captured from an IMU-based device.

Accelerometer-based predictive models of fall risk in older women: a pilot study.

Current clinical methods of screening older adults for fall risk have difficulties. We analyzed data on 67 women (mean age = 77.5 years) who participated in the Objective Physical Activity and Cardiovascular Health (OPACH) study within the Women's Health Initiative and in an accelerometer calibration substudy.

Predicting Pulmonary Function from Phone Sensors.

Introduction: Smartphones are ubiquitous, but it is unknown what physiological functions can be monitored at clinical quality. Pulmonary function is a standard measure of health status for cardiopulmonary patients. We have shown phone sensors can accurately measure walking patterns.

Classification Models for Pulmonary Function using Motion Analysis from Phone Sensors.

Smartphones are ubiquitous, but it is unknown what physiological functions can be monitored at clinical quality. Pulmonary function is a standard measure of health status for cardiopulmonary patients. We have shown phone sensors can accurately measure walking patterns.

Mining Discriminative Patterns to Predict Health Status for Cardiopulmonary Patients.

Smartphones are ubiquitous now, but it is still unclear what physiological functions they can monitor at clinical quality. Pulmonary function is a standard measure of health status for cardiopulmonary patients. We have shown that predictive models can accurately classify cardiopulmonary conditions from healthy status, as well as different severity levels within cardiopulmonary disease, the GOLD stages.

National Surveys of Population Health: Big Data Analytics for Mobile Health Monitors.

At the core of the healthcare crisis is fundamental lack of actionable data. Such data could stratify individuals within populations to predict which persons have which outcomes. If baselines existed for all variations of all conditions, then managing health could be improved by matching the measuring of individuals to their cohort in the population.

Predicting Transitions in Oxygen Saturation Using Phone Sensors.

Introduction: Widespread availability of mobile devices is revolutionizing health monitoring. Smartphones are ubiquitous, but it is unknown what vital signs can be monitored with medical quality. Oxygen saturation is a standard measure of health status.

A natural walking monitor for pulmonary patients using mobile phones.

Mobile devices have the potential to continuously monitor health by collecting movement data including walking speed during natural walking. Natural walking is walking without artificial speed constraints present in both treadmill and nurse-assisted walking. Fitness trackers have become popular which record steps taken and distance, typically using a fixed stride length.

Health monitors for chronic disease by gait analysis with mobile phones.

We have developed GaitTrack, a phone application to detect health status while the smartphone is carried normally. GaitTrack software monitors walking patterns, using only accelerometers embedded in phones to record spatiotemporal motion, without the need for sensors external to the phone. Our software transforms smartphones into health monitors, using eight parameters of phone motion transformed into body motion by the gait model.

Interventions to reduce rehospitalizations after chronic obstructive pulmonary disease exacerbations. A systematic review.

Rationale: Approximately 20% of patients hospitalized for COPD exacerbations in the United States will be readmitted within 30 days. The Centers for Medicare and Medicaid Services has recently proposed to revise the Hospital Readmissions Reduction Program to financially penalize hospitals with high all-cause 30-day rehospitalization rates after a hospitalization for COPD exacerbation on or after October 1, 2014.

Objectives: To report the results of a systematic review of randomized clinical trials evaluating interventions to reduce the rehospitalizations after COPD exacerbations.

Designing and evaluating a clustering system for organizing and integrating patient drug outcomes in personal health messages.

Patient outcomes to drugs vary, but physicians currently have little data about individual responses. We designed a comprehensive system to organize and integrate patient outcomes utilizing semantic analysis, which groups large collections of personal comments into a series of topics. A prototype implementation was built to extract situational evidences by filtering and digesting user comments provided by patients.

Predicting adverse drug events from personal health messages.

Adverse drug events (ADEs) remain a large problem in the United States, being the fourth leading cause of death, despite post market drug surveillance. Much post consumer drug surveillance relies on self-reported "spontaneous" patient data. Previous work has performed datamining over the FDA's Adverse Event Reporting System (AERS) and other spontaneous reporting systems to identify drug interactions and drugs correlated with high rates of serious adverse events.

Behavior-specific changes in transcriptional modules lead to distinct and predictable neurogenomic states.

Using brain transcriptomic profiles from 853 individual honey bees exhibiting 48 distinct behavioral phenotypes in naturalistic contexts, we report that behavior-specific neurogenomic states can be inferred from the coordinated action of transcription factors (TFs) and their predicted target genes. Unsupervised hierarchical clustering of these transcriptomic profiles showed three clusters that correspond to three ecologically important behavioral categories: aggression, maturation, and foraging. To explore the genetic influences potentially regulating these behavior-specific neurogenomic states, we reconstructed a brain transcriptional regulatory network (TRN) model.

BeeSpace Navigator: exploratory analysis of gene function using semantic indexing of biological literature.

With the rapid decrease in cost of genome sequencing, the classification of gene function is becoming a primary problem. Such classification has been performed by human curators who read biological literature to extract evidence. BeeSpace Navigator is a prototype software for exploratory analysis of gene function using biological literature.

Feasibility of mobile phone-based management of chronic illness.

According to the CDC, chronic conditions such as heart disease, cancer, and diabetes cause 75% of healthcare spending in the United States and contribute to nearly seven in ten American deaths. However, despite the prevalence and high-cost of chronic disease, they are also among the most preventable of health problems1. How can we use technology to improve self-care, reduce costs, and lessen the burden on medical professionals? Devices to help manage chronic illness have been marketed for years, but are these specialized devices really necessary? In this paper, the authors identify the aspects of the major chronic illnesses that most need to be controlled and monitored in the US today and explore the feasibility of using current mobile phone technology to improve the management of chronic illness.

Toward Dietary Assessment via Mobile Phone Video Cameras.

Reliable dietary assessment is a challenging yet essential task for determining general health. Existing efforts are manual, require considerable effort, and are prone to underestimation and misrepresentation of food intake. We propose leveraging mobile phones to make this process faster, easier and automatic.

BSQA: integrated text mining using entity relation semantics extracted from biological literature of insects.

Text mining is one promising way of extracting information automatically from the vast biological literature. To maximize its potential, the knowledge encoded in the text should be translated to some semantic representation such as entities and relations, which could be analyzed by machines. But large-scale practical systems for this purpose are rare.

Identifying overrepresented concepts in gene lists from literature: a statistical approach based on Poisson mixture model.

Background: Large-scale genomic studies often identify large gene lists, for example, the genes sharing the same expression patterns. The interpretation of these gene lists is generally achieved by extracting concepts overrepresented in the gene lists. This analysis often depends on manual annotation of genes based on controlled vocabularies, in particular, Gene Ontology (GO).

Measuring population health using personal health messages.

Personal health messages - inter patient communications within online communities; represent a new path towards providing continuous information about patient derived health status. We apply natural language processing techniques to personal health messages from online message boards to demonstrate the ability to track trends in people's positive or negative opinion (sentiment) regarding particular drugs over time. The significant changes in sentiment correspond to FDA announcements and other publicity.

Automatically generating gene summaries from biomedical literature.

Biologists often need to find information about genes whose function is not described in the genome databases. Currently they must try to search disparate biomedical literature to locate relevant articles, and spend considerable efforts reading the retrieved articles in order to locate the most relevant knowledge about the gene. We describe our software, the first that automatically generates gene summaries from biomedical literature.