Analysing Temporal Relations – Beyond Windows, Frames and Predicates

This article proposes an approach to rely on the standard operators of relational algebra (including grouping and ag- gregation) for processing complex event without requiring window specifications. In this way the approach can pro- cess complex event queries of the kind encountered in appli- cations such as emergency management in metro networks. This article presents Temporal Stream Algebra (TSA) which combines the operators of relational algebra with an analy- sis of temporal relations at compile time. This analysis de- termines which relational algebra queries can be evaluated against data streams, i. e. the analysis is able to distinguish valid from invalid stream queries. Furthermore the analysis derives functions similar to the pass, propagation and keep invariants in Tucker's et al. \Exploiting Punctuation Seman- tics in Continuous Data Streams". These functions enable the incremental evaluation of TSA queries, the propagation of punctuations, and garbage collection. The evaluation of TSA queries combines bulk-wise and out-of-order processing which makes it tolerant to workload bursts as they typically occur in emergency management. The approach has been conceived for efficiently processing complex event queries on top of a relational database system. It has been deployed and tested on MonetDB

Evolving classification of intensive care patients from event data

Objective: This work aims at predicting the patient discharge outcome on each hospitalization day by introducing a new paradigm—evolving classification of event data streams. Most classification algorithms implicitly assume the values of all predictive features to be available at the time of making the prediction. This assumption does not necessarily hold in the evolving classification setting (such as intensive care patient monitoring), where we may be interested in classifying the monitored entities as early as possible, based on the attributes initially available to the classifier, and then keep refining our classification model at each time step (e.g., on daily basis) with the arrival of additional attributes. / Materials and methods: An oblivious read-once decision-tree algorithm, called information network (IN), is extended to deal with evolving classification. The new algorithm, named incremental information network (IIN), restricts the order of selected features by the temporal order of feature arrival. The IIN algorithm is compared to six other evolving classification approaches on an 8-year dataset of adult patients admitted to two Intensive Care Units (ICUs) in the United Kingdom. / Results: Retrospective study of 3452 episodes of adult patients (≥ 16 years of age) admitted to the ICUs of Guy’s and St. Thomas’ hospitals in London between 2002 and 2009. Random partition (66:34) into a development (training) set n = 2287 and validation set n = 1165. Episode-related time steps: Day 0—time of ICU admission, Day x—end of the x-th day at ICU. The most accurate decision-tree models, based on the area under curve (AUC): Day 0: IN (AUC = 0.652), Day 1: IIN (AUC = 0.660), Day 2: J48 decision-tree algorithm (AUC = 0.678), Days 3–7: regenerative IN (AUC = 0.717–0.772). Logistic regression AUC: 0.582 (Day 0)—0.827 (Day 7). / Conclusions: Our experimental results have not identified a single optimal approach for evolving classification of ICU episodes. On Days 0 and 1, the IIN algorithm has produced the simplest and the most accurate models, which incorporate the temporal order of feature arrival. However, starting with Day 2, regenerative approaches have reached better performance in terms of predictive accuracy

Modeling Interdependent and Periodic Real-World Action Sequences

Mobile health applications, including those that track activities such as exercise, sleep, and diet, are becoming widely used. Accurately predicting human actions is essential for targeted recommendations that could improve our health and for personalization of these applications. However, making such predictions is extremely difficult due to the complexities of human behavior, which consists of a large number of potential actions that vary over time, depend on each other, and are periodic. Previous work has not jointly modeled these dynamics and has largely focused on item consumption patterns instead of broader types of behaviors such as eating, commuting or exercising. In this work, we develop a novel statistical model for Time-varying, Interdependent, and Periodic Action Sequences. Our approach is based on personalized, multivariate temporal point processes that model time-varying action propensities through a mixture of Gaussian intensities. Our model captures short-term and long-term periodic interdependencies between actions through Hawkes process-based self-excitations. We evaluate our approach on two activity logging datasets comprising 12 million actions taken by 20 thousand users over 17 months. We demonstrate that our approach allows us to make successful predictions of future user actions and their timing. Specifically, our model improves predictions of actions, and their timing, over existing methods across multiple datasets by up to 156%, and up to 37%, respectively. Performance improvements are particularly large for relatively rare and periodic actions such as walking and biking, improving over baselines by up to 256%. This demonstrates that explicit modeling of dependencies and periodicities in real-world behavior enables successful predictions of future actions, with implications for modeling human behavior, app personalization, and targeting of health interventions.Comment: Accepted at WWW 201

Continuous Estimation of Smoking Lapse Risk from Noisy Wrist Sensor Data Using Sparse and Positive-Only Labels

Estimating the imminent risk of adverse health behaviors provides opportunities for developing effective behavioral intervention mechanisms to prevent the occurrence of the target behavior. One of the key goals is to find opportune moments for intervention by passively detecting the rising risk of an imminent adverse behavior. Significant progress in mobile health research and the ability to continuously sense internal and external states of individual health and behavior has paved the way for detecting diverse risk factors from mobile sensor data. The next frontier in this research is to account for the combined effects of these risk factors to produce a composite risk score of adverse behaviors using wearable sensors convenient for daily use. Developing a machine learning-based model for assessing the risk of smoking lapse in the natural environment faces significant outstanding challenges requiring the development of novel and unique methodologies for each of them. The first challenge is coming up with an accurate representation of noisy and incomplete sensor data to encode the present and historical influence of behavioral cues, mental states, and the interactions of individuals with their ever-changing environment. The next noteworthy challenge is the absence of confirmed negative labels of low-risk states and adequate precise annotations of high-risk states. Finally, the model should work on convenient wearable devices to facilitate widespread adoption in research and practice. In this dissertation, we develop methods that account for the multi-faceted nature of smoking lapse behavior to train and evaluate a machine learning model capable of estimating composite risk scores in the natural environment. We first develop mRisk, which combines the effects of various mHealth biomarkers such as stress, physical activity, and location history in producing the risk of smoking lapse using sequential deep neural networks. 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 smoking lapse. To circumvent the lack of confirmed negative labels (i.e., annotated low-risk moments) and only a few positive labels (i.e., sensor-based detection of smoking lapse corroborated by self-reports), we propose a new loss function to accurately optimize the models. We build the mRisk models using biomarker (stress, physical activity) streams derived from chest-worn sensors. Adapting the models to work with less invasive and more convenient wrist-based sensors requires adapting the biomarker detection models to work with wrist-worn sensor data. To that end, we develop robust stress and activity inference methodologies from noisy wrist-sensor data. We first propose CQP, which quantifies wrist-sensor collected PPG data quality. Next, we show that integrating CQP within the inference pipeline improves accuracy-yield trade-offs associated with stress detection from wrist-worn PPG sensors in the natural environment. mRisk also requires sensor-based precise detection of smoking events and confirmation through self-reports to extract positive labels. Hence, we develop rSmoke, an orientation-invariant smoking detection model that is robust to the variations in sensor data resulting from orientation switches in the field. We train the proposed mRisk risk estimation models using the wrist-based inferences of lapse risk factors. To evaluate the utility of the risk models, we simulate the delivery of intelligent smoking interventions to at-risk participants as informed by the composite risk scores. Our results demonstrate the envisaged impact of machine learning-based models operating on wrist-worn wearable sensor data to output continuous smoking lapse risk scores. The novel methodologies we propose throughout this dissertation help instigate a new frontier in smoking research that can potentially improve the smoking abstinence rate in participants willing to quit

The automatic detection of patterns in people's movements

Bibliography: leaves 102-105

Influencer events in episode rules: a way to impact the occurrence of events

International audienceEpisode rules are event patterns mined from a single event sequence. They are mainly used to predict the occurrence of events (the consequent of the rule), once the antecedent has occurred. The occurrence of the consequent of a rule may however be disturbed by the occurrence of another event in the sequence (that does not belong to the antecedent). We refer such an event to as an influencer event. To the best of our knowledge, the identification of such events in the context of episode rules has never been studied. However, identifying influencer events is of the highest importance as these events can be viewed as a way to act to impact the occurrence of events, here the consequent of rules. We propose to identify three types of influencer events: distance influencer events, confidence influencer events and disappearance events. To identify these influencer events, we propose to rely on the set of episode rules discovered by mining algorithms. The proposed approach for discovering influencer events is evaluated on an event sequence of social networks messages. Experiments measure the execution time efficiency according to the adopted episode rules mining algorithm. In addition, they show that some events do actually highly influence the consequent of some rules, that influencer events may not only influence several consequents, but also influence several characteristics of rules

Open challenges for Machine Learning based Early Decision-Making research

More and more applications require early decisions, i.e. taken as soon as possible from partially observed data. However, the later a decision is made, the more its accuracy tends to improve, since the description of the problem to hand is enriched over time. Such a compromise between the earliness and the accuracy of decisions has been particularly studied in the field of Early Time Series Classification. This paper introduces a more general problem, called Machine Learning based Early Decision Making (ML-EDM), which consists in optimizing the decision times of models in a wide range of settings where data is collected over time. After defining the ML-EDM problem, ten challenges are identified and proposed to the scientific community to further research in this area. These challenges open important application perspectives, discussed in this paper