David Hazel

Dietary intake assessment using integrated sensors and software

The area of dietary assessment is becoming increasingly important as obesity rates soar, but valid measurement of the food intake in free-living persons is extraordinarily challenging. Traditional paper-based dietary assessment methods have limitations due to bias, user burden and cost, and therefore improved methods are needed to address important hypotheses related to diet and health. In this paper, we will describe the progress of our mobile Diet Data Recorder System (DDRS), where an electronic device is used for objective measurement on dietary intake in real time and at moderate cost. The DDRS consists of (1) a mobile device that integrates a smartphone and an integrated laser package, (2) software on the smartphone for data collection and laser control, (3) an algorithm to process acquired data for food volume estimation, which is the largest source of error in calculating dietary intake, and (4) database and interface for data storage and management. The estimated food volume, together with direct entries of food questionnaires and voice recordings, could provide dietitians and nutritional epidemiologists with more complete food description and more accurate food portion sizes. In this paper, we will describe the system design of DDRS and initial results of dietary assessment.

Dynamic Hierarchical Classification for Patient Risk-of-Readmission

Congestive Heart Failure (CHF) is a serious chronic condition often leading to 50% mortality within 5 years. Improper treatment and post-discharge care of CHF patients leads to repeat frequent hospitalizations (i.e., readmissions). Accurately predicting patients risk-of-readmission enables care-providers to plan resources, perform factor analysis, and improve patient quality of life. In this paper, we describe a supervised learning framework, Dynamic Hierarchical Classification (DHC) for patients risk-of-readmission prediction. Learning the hierarchy of classifiers is often the most challenging component of such classification schemes. The novelty of our approach is to algorithmically generate various layers and combine them to predict overall 30-day risk-of-readmission. While the components of DHC are generic, in this work, we focus on congestive heart failure (CHF), a pressing chronic condition. Since healthcare data is diverse and rich and each source and feature-subset provides different insights into a complex problem, our DHC based prediction approach intelligently leverages each source and feature-subset to optimize different objectives (such as, Recall or AUC) for CHF risk-of-readmission. DHCs algorithmic layering capability is trained and tested over two real world datasets and is currently integrated into the clinical decision support tools at MultiCare Health System (MHS), a major provider of healthcare services in the northwestern US. It is integrated into a QlikView App (with EMR integration planned for Q2) and currently scores patients everyday, helping to mitigate readmissions and improve quality of care, leading to healthier outcomes and cost savings.

Routing service with real world severe weather

Traditional routing services aim to save driving time by recommending the shortest path, in terms of distance or time, to travel from a start location to a given destination. However, these methods are relatively static and to a certain extent rely on traffic patterns under relatively normal conditions to calculate and recommend an appropriate route. As such, they do not necessarily translate effectively during severe weather events such as tornadoes. In these scenarios, the guiding principal is not, optimize for travel time, but rather, optimize for survivability of the event, i.e., can we recommend an evacuation route to those users inside the hazardous areas. In this demo, we present a framework for routing services for evacuating and avoiding real world severe weather threats that is able to: (1) Identify the users inside the dangerous region of a severe weather event (2) Recommend an evacuation route to guide the users out to a safe destination or shelter (3) Assure the recommended route to be one of the shortest paths after excluding the risky area (4) Maintain the flow of traffic by normalizing the evacuation on the possible safe routes. During the demo, attendees will be able to use the system interactively through its graphical user interface within a number of different scenarios. They will be able to locate the severe weather events on real time basis in any area in USA and examine detailed information about each event, to issue an evacuation query from an existing dangerous area by identifying a destination location and receiving the routing direction on their mobile devices, to issue an avoidance routing query to ask for a shortest path that avoids the dangerous region, to have an inside look into the internal system components and finally, to evaluate the overall system performance.

Work in Progress - In-Memory Analysis for Healthcare Big Data

Advances in healthcare data management and analytics have opened new horizons for healthcare providers such as cost effective treatments, ability to detect medical fraud, and diagnose diseases at an early stage. Central to these abilities is the need for fast ad-hoc query processing of large volumes of complex healthcare datasets. End users who work with healthcare databases spend enormous effort in data exploration since exploration is the first step to any subsequent predictive modeling to generate actionable insights for patients, providers and physicians. Unfortunately, unlike other domains the complexity and volumes of claims (ICD9 or 10) as well as clinical (HL7) healthcare datasets results in data exploration solutions being extremely slow and cumbersome when attempted using traditional disk resident data warehousing approaches. In this paper we describe the first ever attempt of real-time data exploration for healthcare datasets using in-memory databases. We benchmark and compare two such in-memory database systems to study responsiveness and ability to handle complexity of typical health data exploration tasks. We share our work in progress results and outline key issues that need to be addressed for forthcoming advances in this very important big data vertical.

Smart Personalized Routing for Smart Cities

In smart cities, commuters have the opportunities for smart routing that may enable selecting a route with less car accidents, or one that is more scenic, or perhaps a straight and flat route. Such smart personalization requires a data management framework that goes beyond a static road network graph. This paper introduces PreGo, a novel system developed to provide real time personalized routing. The recommended routes by PreGo are smart and personalized in the sense of being (1) adjustable to individual users preferences, (2) subjective to the trip start time, and (3) sensitive to changes of the road conditions. Extensive experimental evaluation using real and synthetic data demonstrates the efficiency of the PreGo system.

Dynamic and Personalized Routing in PreGo

Existing routing services calculate the best route from source to destination over a road network graph. Most commercial routing services offer the best route in terms of either the shortest travel distance or the shortest travel time (with or without considering current traffic conditions). While travel distance and travel time are crucial route preferences for the commuter, other preferences are equally, or even more, important. Examples of other route preferences include fuel consumption, gas emissions, road safety, points of interest along the route, construction activities, open shops and restaurants. While some route preferences are static (e.g., travel distance and points of interests), other route preferences are dynamic and vary according to the time of the day (e.g., traffic-dependent travel time and the number of open shops/restaurants). Volunteered Geographic information (VGI) has been proposed as an approach to collect massive amounts of route information and, more specifically, the time varying parameters. This demo presents PreGo, a time-dependent multi-preference routing engine. During the demo, audience would interact with the PreGo routing engine to (1) find the optimal route w.r.t. The users personal preferences for a given start time, (2) dynamically obtain the best start time for a trip given a set of preferences, (3) feed the system with VGI and examine their effect on the chosen route at real time, and (4) examine the correctness and efficiency of the PreGo selected routes compared to routes chosen by other commercial systems.

HealthSCOPE: An Interactive Distributed Data Mining Framework for Scalable Prediction of Healthcare Costs

In this demonstration proposal we describe Health-SCOPE (Healthcare Scalable COst Prediction Engine), a frame-work for exploring historical and present day healthcare costs as well as for predicting future costs. Health SCOPE can be used by individuals to estimate their healthcare costs in the coming year. In addition, Health SCOPE supports a population based view for actuaries and insurers who want to estimate the future costs of a population based on historical claims data, a typical scenario for accountable care organizations (ACOs). Using our interactive data mining framework, users can view claims (sample files will be provided), use Health SCOPE to predict costs for the upcoming year, interactively select from a set of possible medical conditions, understand the factors that contribute to the cost, and compare costs against historical averages. The back-end system contains cloud based prediction services hosted on the Microsoft Azure infrastructure that allow the easy deployment of models encoded in Predictive Model Markup Language (PMML) and trained using either Spark MLLib or various non-distributed environments.

Pathway-Finder: An Interactive Recommender System for Supporting Personalized Care Pathways

Clinical pathways define the essential component of the complex care process, with the objective to optimize patient outcomes and resource allocation. Clinical pathway analysis has gained increased attention in order to augment the patient treatment process. In this demonstration paper, we propose Pathway-Finder, an interactive recommender system to visually explore and discover clinical pathways. The interactive web service efficiently collects and displays patient information in a meaningful way to support an effective personalized treatment plan. Pathway-Finder implements a Bayesian Network to discover causal relationships among different factors. To support real-time recommendation and visualization, a key-value structure has been implemented to traverse the Bayesian Network faster. Additionally, Pathway-Finder is a cloud based web service hosted on Microsoft Azure which enables the health providers to access the system without the need to deploy analytics infrastructure. We demonstrate Pathway-Finder to interactively recommend personalized interventions to minimize 30-day readmission risk for Heart Failure (HF).