Machine Learning Models For Predicting the Imminent Risk of Impulsive Behaviors Using mHealth Sensors

Researchers have developed machine learning models for detecting behaviors (e.g., smoking, eating, and drinking) and health states (e.g., stress) from wearable and mobile sensors. However, these models help detect events after they occur. The next frontier is the prediction of imminent risk of adverse health events.For mobile sensor-based prediction of imminent risk of impulsive behaviors, passive and continuous detection of risk factors of such behaviors is necessary. But, for human thoughts, perceptions, and contexts (e.g., suicidal ideation, craving/urge, desire for immediate gratification, and exposure to risky environmental cues), that are potential antecedents and precipitants of impulsive behaviors (e.g., suicide attempt, overeating, binge-drinking, and smoking lapse), it is challenging to obtain temporally-precise annotations. Furthermore, their manifestations in the underlying mobile sensor data are not yet known. Finally, due to their rapid variability and lack of clear separation of extreme values from well-established population norm, risk estimations of the target impulsive behavior becomes non-trivial.In this dissertation, we propose machine learning methods and models for predicting the imminent risk of impulsive behaviors. Specifically, we propose three key innovations. First, we propose temporal label propagation to address the mismatch between the temporal resolution of labels and the dynamism of the human state and context, determining specific `moments\u27 when they are expected to change. Second, we develop context-specific temporally-precise features to address the lack of known correspondence between the target state and the underlying sensor data in the relevant scientific domains.Finally, we introduce spatio-behavioro-temporal precision features and events-of-interest encodings to represent the person-specific spatio-temporal dynamics of multiple risk factors. We demonstrate the utility of these innovations by developing a risk prediction model for a smoking lapse in newly abstinent smokers.Specifically, we first develop mCrave to estimate the state of cigarette craving during smoking abstinence. Second, we develop SmokingOpp to detect the exposure to environmental or situational cues conducive to a smoking lapse. Finally, we develop mRisk to predict the imminent risk of a smoking lapse. These prediction models can be used to deliver temporally precise sensor-triggered interventions that are both personalized and contextualized to each individual and their current context

puffMarker: A Multi-Sensor Approach for Pinpointing the Timing of First Lapse in Smoking Cessation

Recent researches have demonstrated the feasibility of detecting smoking from wearable sensors, but their performance on real-life smoking lapse detection is unknown. In this paper, we propose a new model and evaluate its performance on 61 newly abstinent smokers for detecting a first lapse. We use two wearable sensors - breathing pattern from respiration and arm movements from 6-axis inertial sensors worn on wrists. In 10-fold cross-validation on 40 hours of training data from 6 daily smokers, our model achieves a recall rate of 96.9%, for a false positive rate of 1.1%. When our model is applied to 3 days of post-quit data from 32 lapsers, it correctly pinpoints the timing of first lapse in 28 participants. Only 2 false episodes are detected on 20 abstinent days of these participants. When tested on 84 abstinent days from 28 abstainers, the false episode per day is limited to 1/6

Using novel mobile sensors to assess stress and smoking lapse

Mobile sensors can now provide unobtrusive measurement of both stress and cigarette smoking behavior. We describe, here, the first field tests of two such methods, cStress and puffMarker, that were used to examine relationships between stress and smoking behavior and lapse from a sample of 76 smokers motivated to quit smoking. Participants wore a mobile sensors suite, called AutoSense, which collected continuous physiological data for 4 days (24-hours pre-quit and 72-hours post-quit) in the field. Algorithms were applied to the physiological data to create indices of stress (cStress) and first lapse smoking episodes (puffMarker). We used mixed effects interrupted autoregressive time series models to assess changes in heart rate (HR), cStress, and nicotine craving across the 4-day period. Self-report assessments using ecological momentary assessment (EMA) of mood, withdrawal symptoms, and smoking behavior were also used. Results indicated that HR and cStress, respectively, predicted smoking lapse. These results suggest that measures of traditional psychophysiology, such as HR, are not redundant with cStress; both provide important information. Results are consistent with existing literature and provide clear support for cStress and puffMarker in ambulatory clinical research. This research lays groundwork for sensor-based markers in developing and delivering sensor-triggered, just-in-time interventions that are sensitive to stress-related lapser risk factors

mRisk: Continuous Risk Estimation for Smoking Lapse from Noisy Sensor Data with Incomplete and Positive-Only Labels

Passive detection of risk factors (that may influence unhealthy or adverse behaviors) via wearable and mobile sensors has created new opportunities to improve the effectiveness of behavioral interventions. A key goal is to find opportune moments for intervention by passively detecting rising risk of an imminent adverse behavior. But, it has been difficult due to substantial noise in the data collected by sensors in the natural environment and a lack of reliable label assignment of low-and high-risk states to the continuous stream of sensor data. In this paper, we propose an event-based encoding of sensor data to reduce the effect of noises and then present an approach to efficiently model the historical influence of recent and past sensor-derived contexts on the likelihood of an adverse behavior. Next, to circumvent the lack of any confirmed negative labels (i.e., time periods with no high-risk moment), and only a few positive labels (i.e., detected adverse behavior), we propose a new loss function. We use 1,012 days of sensor and self-report data collected from 92 participants in a smoking cessation field study to train deep learning models to produce a continuous risk estimate for the likelihood of an impending smoking lapse. The risk dynamics produced by the model show that risk peaks an average of 44 minutes before a lapse. Simulations on field study data show that using our model can create intervention opportunities for 85% of lapses with 5.5 interventions per day

SmokingOpp: Detecting the smoking \u27opportunity\u27 context using mobile sensors

Context plays a key role in impulsive adverse behaviors such as fights, suicide attempts, binge-drinking, and smoking lapse. Several contexts dissuade such behaviors, but some may trigger adverse impulsive behaviors. We define these latter contexts as \u27opportunity\u27 contexts, as their passive detection from sensors can be used to deliver context-sensitive interventions. In this paper, we define the general concept of \u27opportunity\u27 contexts and apply it to the case of smoking cessation. We operationalize the smoking \u27opportunity\u27 context, using self-reported smoking allowance and cigarette availability. We show its clinical utility by establishing its association with smoking occurrences using Granger causality. Next, we mine several informative features from GPS traces, including the novel location context of smoking spots, to develop the SmokingOpp model for automatically detecting the smoking \u27opportunity\u27 context. Finally, we train and evaluate the SmokingOpp model using 15 million GPS points and 3,432 self-reports from 90 newly abstinent smokers in a smoking cessation study

mCrave

Craving usually precedes a lapse for impulsive behaviors such as overeating, drinking, smoking, and drug use. Passive estimation of craving from sensor data in the natural environment can be used to assist users in coping with craving. In this paper, we take the first steps towards developing a computational model to estimate cigarette craving (during smoking abstinence) at the minute-level using mobile sensor data. We use 2,012 hours of sensor data and 1,812 craving self-reports from 61 participants in a smoking cessation study. To estimate craving, we first obtain a continuous measure of stress from sensor data. We find that during hours of day when craving is high, stress associated with self-reported high craving is greater than stress associated with low craving. We use this and other insights to develop feature functions, and encode them as pattern detectors in a Conditional Random Field (CRF) based model to infer craving probabilities

A draft map of the human proteome

The availability of human genome sequence has transformed biomedical research over the past decade. However, an equivalent map for the human proteome with direct measurements of proteins and peptides does not exist yet. Here we present a draft map of the human proteome using high-resolution Fourier-transform mass spectrometry. In-depth proteomic profiling of 30 histologically normal human samples, including 17 adult tissues, 7 fetal tissues and 6 purified primary haematopoietic cells, resulted in identification of proteins encoded by 17,294 genes accounting for approximately 84% of the total annotated protein-coding genes in humans. A unique and comprehensive strategy for proteogenomic analysis enabled us to discover a number of novel protein-coding regions, which includes translated pseudogenes, non-coding RNAs and upstream open reading frames. This large human proteome catalogue (available as an interactive web-based resource at http://www.humanproteomemap.org) will complement available human genome and transcriptome data to accelerate biomedical research in health and disease. © 2014 Macmillan Publishers Limited