Integrative analysis of drug response and clinical outcome in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a dataset that has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort concordance and identify features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally affecting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers avenues for mechanistic exploration and drug development, and reveals tools for predicting outcome in AML.

Optimizing medication reminders using a decision-theoretic framework.

We discuss a new approach to patients' adherence to enhance to their medication-taking regimen by developing a context-aware alerting system that would optimize the expected utility of alerts. Each patient's instantaneous context is assessed using a real-time sensor network deploying a variety of sensors. The alerts are generated to optimize the expected value to the patient. This paper is focused on the initial assessment of the utility of alerts, including the tradeoff between effectiveness and annoyance.

Remote health coaching for interactive exercise with older adults in a home environment.

Optimal health coaching interventions are tailored to individuals' needs, preferences, motivations, barriers, timing, and readiness to change. Technology approaches are useful in both monitoring a user's adherence to their behavior change goals and also in providing just-in-time feedback and coaching messages. User models that incorporate dynamically varying behavior change variables with algorithms that trigger tailored messages provide a framework for making health interventions more effective. These principles are applied in the described system for assisting older adults in meeting their physical exercise goals with a tailored interactive video system with just-in-time feedback and encouragement.

Computer mouse movement patterns: A potential marker of mild cognitive impairment.

INTRODUCTION: Subtle changes in cognitively demanding activities occur in MCI but are difficult to assess with conventional methods. In an exploratory study, we examined whether patterns of computer mouse movements obtained from routine home computer use discriminated between older adults with and without MCI. METHODS: Participants were 42 cognitively intact and 20 older adults with MCI enrolled in a longitudinal study of in-home monitoring technologies. Mouse pointer movement variables were computed during one week of routine home computer use using algorithms that identified and characterized mouse movements within each computer use session. RESULTS: MCI was associated with making significantly fewer total mouse moves (p<.01), and making mouse movements that were more variable, less efficient, and with longer pauses between movements (p<.05). Mouse movement measures were significantly associated with several cognitive domains (p's<.01-.05). DISCUSSION: Remotely monitored computer mouse movement patterns are a potential early marker of real-world cognitive changes in MCI.

Assessing executive function using a computer game: computational modeling of cognitive processes.

Early and reliable detection of cognitive decline is one of the most important challenges of current healthcare. In this project, we developed an approach whereby a frequently played computer game can be used to assess a variety of cognitive processes and estimate the results of the pen-and-paper trail making test (TMT)--known to measure executive function, as well as visual pattern recognition, speed of processing, working memory, and set-switching ability. We developed a computational model of the TMT based on a decomposition of the test into several independent processes, each characterized by a set of parameters that can be estimated from play of a computer game designed to resemble the TMT. An empirical evaluation of the model suggests that it is possible to use the game data to estimate the parameters of the underlying cognitive processes and using the values of the parameters to estimate the TMT performance. Cognitive measures and trends in these measures can be used to identify individuals for further assessment, to provide a mechanism for improving the early detection of neurological problems, and to provide feedback and monitoring for cognitive interventions in the home.

Quantification of human movement for assessment in automated exercise coaching.

Quantification of human movement is a challenge in many areas, ranging from physical therapy to robotics. We quantify of human movement for the purpose of providing automated exercise coaching in the home. We developed a model-based assessment and inference process that combines biomechanical constraints with movement assessment based on the Microsoft Kinect camera. To illustrate the approach, we quantify the performance of a simple squatting exercise using two model-based metrics that are related to strength and endurance, and provide an estimate of the strength and energy-expenditure of each exercise session. We look at data for 5 subjects, and show that for some subjects the metrics indicate a trend consistent with improved exercise performance.

One walk a year to 1000 within a year: continuous in-home unobtrusive gait assessment of older adults.

Physical performance measures predict health and function in older populations. Walking speed in particular has consistently predicted morbidity and mortality. However, single brief walking measures may not reflect a person's typical ability. Using a system that unobtrusively and continuously measures walking activity in a person's home we examined walking speed metrics and their relation to function. In 76 persons living independently (mean age, 86) we measured every instance of walking past a line of passive infra-red motion sensors placed strategically in their home during a four-week period surrounding their annual clinical evaluation. Walking speeds and the variance in these measures were calculated and compared to conventional measures of gait, motor function and cognition. Median number of walks per day was 18±15. Overall mean walking speed was 61±17 cm/s. Characteristic fast walking speed was 96 cm/s. Men walked as frequently and fast as women. Those using a walking aid walked significantly slower and with greater variability. Morning speeds were significantly faster than afternoon/evening speeds. In-home walking speeds were significantly associated with several neuropsychological tests as well as tests of motor performance. Unobtrusive home walking assessments are ecologically valid measures of walking function. They provide previously unattainable metrics (periodicity, variability, range of minimum and maximum speeds) of everyday motor function.

Unobtrusive in-home assessment by means of everyday computer mouse usage.

An important component of future proactive healthcare is the detection of changes in the individual's physical or cognitive performance, especially for aging and for those with neurodegenerative diseases. For a variety of reasons, the current techniques for neuropsychological assessment are not suitable for continuous monitoring and assessment. This paper proposes a technique for continuous monitoring of behaviors that could potentially be used to complement the traditional assessment techniques. In particular we monitor the movements of a computer pointing device (mouse) to assess cognitive and sensory-motor functionality of human users unobtrusively. The focus of this paper is on an approach that can be used to identify moves so that they can later be used as the basis for constructing sensory-motor measures. Due to the nature of the data the distinction between moves and pauses between moves is not immediately apparent. The segmentation of the data into moves is done by constructing an estimated distribution of the mouse cursor velocity for the entire computer session and locating a particular minimum which indicates a likely point of division between active moves and inter-move intervals. We analyzed computer usage data for 113 elderly participants over a period of two years, and the technique applied to that data was able to divide data from a session of computer usage into a series of mouse moves in 98% of observed computer sessions with a physically sensible value for the cutoff dividing moves from stops.

Unobtrusive and ubiquitous in-home monitoring: a methodology for continuous assessment of gait velocity in elders.

Gait velocity has been shown to quantitatively estimate risk of future hospitalization, a predictor of disability, and has been shown to slow prior to cognitive decline. In this paper, we describe a system for continuous and unobtrusive in-home assessment of gait velocity, a critical metric of function. This system is based on estimating walking speed from noisy time and location data collected by a "sensor line" of restricted view passive infrared motion detectors. We demonstrate the validity of our system by comparing with measurements from the commercially available GAITRite walkway system gait mat. We present the data from 882 walks from 27 subjects walking at three different subject-paced speeds (encouraged to walk slowly, normal speed, or fast) in two directions through a sensor line. The experimental results show that the uncalibrated system accuracy (average error) of estimated velocity was 7.1 cm/s (SD = 11.3 cm/s), which improved to 1.1 cm/s (SD = 9.1 cm/s) after a simple calibration procedure. Based on the average measured walking speed of 102 cm/s, our system had an average error of less than 7% without calibration and 1.1% with calibration.

Models of cognitive performance based on home monitoring data.

Modeling cognitive performance using home monitoring data is a new approach to managing neurologic conditions and for monitoring the effects of cognitive exercise interventions. The data consists of activity monitoring from motion sensors and specific cognitive metrics embedded within our adaptive computer games. The frequency and continuity of data collection allows us to analyze within subject trends of cognitive performance and to assess day to day variability. This approach provides a framework for clinicians and care managers to have the potential of detecting patients' cognitive problems early and to have timely feedback on treatment interventions.

Unobtrusive assessment of walking speed in the home using inexpensive PIR sensors.

Walking speed and activity are important measures of functional ability in the elderly. Our earlier studies have suggested that continuous monitoring may allow us to detect changes in walking speed that are also predictive of cognitive changes. We evaluated the use of passive infrared (PIR) sensors for measuring walking speed in the home on an ongoing basis. In comparisons with gait mat estimates (ground truth) and the results of a timed walk test (the clinical gold standard) in 18 subjects, we found that the clinical measure overestimated typical walking speed, and the PIR sensor estimations of walking speed were highly correlated to actual gait speed. Examination of in-home walking patterns from more than 100,000 walking speed samples for these subjects suggested that we can accurately assess walking speed in the home. We discuss the potential of this approach for continuous assessment.

The exploration & forensic analysis of computer usage data in the elderly.

Unobtrusive in-home computer monitoring could one day be used to deliver cost-effective diagnostic information about the cognitive abilities of the elderly. This could allow for early detection of cognitive impairment and would additionally be coupled with the cost advantages that are associated with a semi-automated system. Before using the computer usage data to draw conclusions about the participants, we first needed to investigate the nature of the data that was collected. This paper represents a forensics style analysis of the computer usage data that is being collected as part of a larger study of cognitive decline, and focuses on the isolation and removal of non user-generated activities that were recorded by our computer monitoring software (CMS).