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

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