Multivariate sequential contrast pattern mining and prediction models for critical care clinical informatics

University of Technology Sydney. Faculty of Engineering and Information Technology.Data mining and knowledge discovery involves efficient search and discovery of patterns in data that are able to describe the underlying complex structure and properties of the corresponding system. To be of practical use, the discovered patterns need to be novel, informative and interpretable. Large-scale unstructured biomedical databases such as electronic health records (EHRs) tend to exacerbate the problem of discovering interesting and useful patterns. Typically, patients in intensive care units (ICUs) require constant monitoring of vital signs. To this purpose, significant quantities of patient data, coupled with waveform signals are gathered from biosensors and clinical information systems. Subsequently, clinicians face an enormous challenge in the assimilation and interpretation of large volumes of unstructured, multidimensional, noisy and dynamically fluctuating patient data. The availability of de-identified ICU datasets like the MIMIC-II (Multiparameter Intelligent Monitoring in Intensive Care) databases provide an opportunity to advance medical care, by benchmarking algorithms that capture subtle patterns associated with specific medical conditions. Such patterns are able to provide fresh insights into disease dynamics over long time scales. In this research, we focus on the extraction of computational physiological markers, in the form of relevant medical episodes, event sequences and distinguishing sequential patterns. These interesting patterns known as sequential contrast patterns are combined with patient clinical features to develop powerful clinical prediction models. Later, the clinical models are used to predict critical ICU events, pertaining to numerous forms of hemodynamic instabilities causing acute hypotension, multiple organ failures, and septic shock events. In the process, we employ novel sequential pattern mining methodologies for the structured analysis of large-scale ICU datasets. The reported algorithms use a discretised representation such as symbolic aggregate approximation for the analysis of physiological time series data. Thus, symbolic sequences are used to abstract physiological signals, facilitating the development of efficient sequential contrast mining algorithms to extract high risk patterns and then risk stratify patient populations, based on specific clinical inclusion criteria. Chapter 2 thoroughly reviews the pattern mining research literature relating to frequent sequential patterns, emerging and contrast patterns, and temporal patterns along with their applications in clinical informatics. In Chapter 3, we incorporate a contrast pattern mining algorithm to extract informative sequential contrast patterns from hemodynamic data, for the prediction of critical care events like Acute Hypotension Episodes (AHEs). The proposed technique extracts a set of distinguishing sequential patterns to predict the occurrence of an AHE in a future time window, following the passage of a user-defined gap interval. The method demonstrates that sequential contrast patterns are useful as potential physiological biomarkers for building optimal patient risk stratification systems and for further clinical investigation of interesting patterns in critical care patients. Chapter 4 reports a generic two stage sequential patterns based classification framework, which is used to classify critical patient events including hypotension and patient mortality, using contrast patterns. Here, extracted sequential patterns undergo transformation to construct binary valued and frequency based feature vectors for developing critical care classification models. Chapter 5 proposes a novel machine learning approach using sequential contrast patterns for the early prediction of septic shock. The approach combines highly informative sequential patterns extracted from multiple physiological variables and captures the interactions among these patterns via Coupled Hidden Markov Models (CHMM). Our results demonstrate a strong competitive accuracy in the predictions, especially when the interactions between the multiple physiological variables are accounted for using multivariate coupled sequential models. The novelty of the approach stems from the integration of sequence-based physiological pattern markers with the sequential CHMM to learn dynamic physiological behavior as well as from the coupling of such patterns to build powerful risk stratification models for septic shock patients. All of the described methods have been tested and bench-marked using numerous real world critical care datasets from the MIMIC-II database. The results from these experiments show that multivariate sequential contrast patterns based coupled models are highly effective and are able to improve the state-of-the-art in the design of patient risk prediction systems in critical care settings

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

QnQ: Quality and Quantity based Unified Approach for Secure and Trustworthy Mobile Crowdsensing

A major challenge in mobile crowdsensing applications is the generation of false (or spam) contributions resulting from selfish and malicious behaviors of users, or wrong perception of an event. Such false contributions induce loss of revenue owing to undue incentivization, and also affect the operational reliability of the applications. To counter these, we propose an event-trust and user-reputation model, called QnQ, to segregate different user classes such as honest, selfish, or malicious. The resultant user reputation scores, are based on both ‘quality’ (accuracy of contribution) and ‘quantity’ (degree of participation) of their contributions. Specifically, QnQ exploits a rating feedback mechanism for evaluating an event-specific expected truthfulness, which is then transformed into a robust quality of information (QoI) metric to weaken various effects of selfish and malicious user behaviors. Eventually, the QoIs of various events in which a user has participated are aggregated to compute his reputation score, which is then used to judiciously disburse incentives with a goal to reduce the incentive losses of the CS application provider. Subsequently, inspired by cumulative prospect theory (CPT), we propose a risk tolerance and reputation aware decision scheme to determine whether an event should be published or not, thus improving the operational reliability of the application. To evaluate QnQ experimentally, we consider a vehicular crowdsensing application as a proof-of-concept. We compare QoI performance achieved by our model with Jøsang\u27s belief model, reputation scoring with Dempster-Shafer based reputation model, and operational (decision) accuracy with expected utility theory Experimental results demonstrate that QnQ is able to better capture subtle differences in user behaviors based on both quality and quantity, reduces incentive losses and significantly improves operational accuracy in presence of rogue contributions

W2Q: A Dual Weighted QoI Scoring Mechanism in Social Sensing using Community Confidence

A significant vulnerability in social sensing based services is false notifications from sensing agents, thereby resulting in inaccurate published information that induces loss of revenue and business goodwill. Existing popular schemes utilize rating feedbacks (over the published information) to quantify the perceived usefulness (quality) of the information. However, these schemes do not reward the confidence of the feedback community and lacks provision to regulate the impact of uncertain feedbacks (ratings), and hence can be easily manipulated. In this paper, we propose a model, called W2Q, to mathematically evaluate the Quality of Information (QoI) as a function of the proportion of positive ratings, total number of ratings, and amortized proportion of uncertain ratings. The proposed model exploits Bayesian inference, and a dual weighted regression model to compute the QoI of any published information. We evaluate the proposed model through an experimental study assuming a crowd sourced-urban application as a proof of concept. Experimental results show that compared with the state-of-the-art Josang\u27s belief model, the resultant QoI score is less susceptible to rogue ratings and captures subtle differences between true and false information

QnQ: A Reputation Model to Secure Mobile Crowdsourcing Applications from Incentive Losses

A major limitation of mobile Crowd Sourcing (CS) applications is the generation of false (or spam) contributions due to selfish and malicious behaviors of users, or wrong perception of an event. Such false contributions induce loss of revenue through disbursement of undue incentives and also negatively affects the application\u27s operational reliability. In this work, we propose a reputation model, called QnQ, to segregate different user classes such as honest, selfish, or malicious based on their reputation scores. The resultant score is then used as an indicator to decide an incentive for a user. Unlike existing works, QnQ ensures fairness to different user behaviors by unifying \u27quantity\u27 (degree of participation) and \u27quality\u27 (accuracy of contribution). Specifically, QnQ utilizes evidences from a rating feedback mechanism to propose an event-specific expected truthfulness metric by considering total feedback volume, probability mass for positive evidence, and the discounted probability mass of uncertain evidence. To classify an event as true or not, a generalized linear model is used to transform its truthfulness into quality of information (QoI). Finally, the QoIs of various events in which a user participates, are aggregated to compute a user\u27s reputation score. For evaluation of QnQ through experimental study, we consider a vehicular crowdsourcing application. QoI performance of our model is compared with Jøsang\u27s belief model, while reputation and incentive leakage is compared with Dempster-Shafer based reputation model. Experimental results demonstrate that QnQ is able to better capture subtle differences in user behaviors by unifying both quality and quantity, and significantly reduces undue incentives in presence of rogue contributions

Ontology-Guided Data Augmentation for Medical Document Classification

Extracting meaningful features from unstructured text is one of the most challenging tasks in medical document classification. The various domain specific expressions and synonyms in the clinical discharge notes make it more challenging to analyse them. The case becomes worse for short texts such as abstract documents. These challenges can lead to poor classification accuracy. As the medical input data is often not enough in the real world, in this work a novel ontology-guided method is proposed for data augmentation to enrich input data. Then, three different deep learning methods are employed to analyse the performance of the suggested approach for classification. The experimental results show that the suggested approach achieved substantial improvement in the targeted medical documents classification

De (semester?), IPRO 342: Hybrid Electric Vehicles - Simulation Design Implementation IPRO 342 IPRO Day Presentation Sp06

IPRO 342 aims to complete the conversion of a CTA bus and a school bus from conventional vehicles to hybrid. There will be one retrofit parallel design for the CTA bus, while for the school bus there will be a new and a retrofit parallel design. All vehicle simulations and structured testing will be performed using ADVISOR, as well as other software packages available in the Power Electronics and Motor Drives Laboratory at IIT. Designed heavy-duty vehicles will be simulated and their performance as well as fuel economy and emissions under different conditions will be studied.Deliverables for IPRO 342: Hybrid Electric Vehicles: Simulation, Design, and Implementation for the Spring 2006 semeste

De (semester?), IPRO 342: Hybrid Electric Vehicles - Simulation Design Implementation IPRO 342 Midterm Report Sp06

IPRO 342 aims to complete the conversion of a CTA bus and a school bus from conventional vehicles to hybrid. There will be one retrofit parallel design for the CTA bus, while for the school bus there will be a new and a retrofit parallel design. All vehicle simulations and structured testing will be performed using ADVISOR, as well as other software packages available in the Power Electronics and Motor Drives Laboratory at IIT. Designed heavy-duty vehicles will be simulated and their performance as well as fuel economy and emissions under different conditions will be studied.Deliverables for IPRO 342: Hybrid Electric Vehicles: Simulation, Design, and Implementation for the Spring 2006 semeste

De (semester?), IPRO 342: Hybrid Electric Vehicles - Simulation Design Implementation IPRO 342 Project Plan Sp06

IPRO 342 aims to complete the conversion of a CTA bus and a school bus from conventional vehicles to hybrid. There will be one retrofit parallel design for the CTA bus, while for the school bus there will be a new and a retrofit parallel design. All vehicle simulations and structured testing will be performed using ADVISOR, as well as other software packages available in the Power Electronics and Motor Drives Laboratory at IIT. Designed heavy-duty vehicles will be simulated and their performance as well as fuel economy and emissions under different conditions will be studied.Deliverables for IPRO 342: Hybrid Electric Vehicles: Simulation, Design, and Implementation for the Spring 2006 semeste