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

An Intelligent Data Mining System to Detect Health Care Fraud

The chapter begins with an overview of the types of healthcare fraud. Next, there is a brief discussion of issues with the current fraud detection approaches. The chapter then develops information technology based approaches and illustrates how these technologies can improve current practice. Finally, there is a summary of the major findings and the implications for healthcare practice

Deepr: A Convolutional Net for Medical Records

Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive regular clinical motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-end deep learning system that learns to extract features from medical records and predicts future risk automatically. Deepr transforms a record into a sequence of discrete elements separated by coded time gaps and hospital transfers. On top of the sequence is a convolutional neural net that detects and combines predictive local clinical motifs to stratify the risk. Deepr permits transparent inspection and visualization of its inner working. We validate Deepr on hospital data to predict unplanned readmission after discharge. Deepr achieves superior accuracy compared to traditional techniques, detects meaningful clinical motifs, and uncovers the underlying structure of the disease and intervention space

Explanation-Based Auditing

To comply with emerging privacy laws and regulations, it has become common for applications like electronic health records systems (EHRs) to collect access logs, which record each time a user (e.g., a hospital employee) accesses a piece of sensitive data (e.g., a patient record). Using the access log, it is easy to answer simple queries (e.g., Who accessed Alice's medical record?), but this often does not provide enough information. In addition to learning who accessed their medical records, patients will likely want to understand why each access occurred. In this paper, we introduce the problem of generating explanations for individual records in an access log. The problem is motivated by user-centric auditing applications, and it also provides a novel approach to misuse detection. We develop a framework for modeling explanations which is based on a fundamental observation: For certain classes of databases, including EHRs, the reason for most data accesses can be inferred from data stored elsewhere in the database. For example, if Alice has an appointment with Dr. Dave, this information is stored in the database, and it explains why Dr. Dave looked at Alice's record. Large numbers of data accesses can be explained using general forms called explanation templates. Rather than requiring an administrator to manually specify explanation templates, we propose a set of algorithms for automatically discovering frequent templates from the database (i.e., those that explain a large number of accesses). We also propose techniques for inferring collaborative user groups, which can be used to enhance the quality of the discovered explanations. Finally, we have evaluated our proposed techniques using an access log and data from the University of Michigan Health System. Our results demonstrate that in practice we can provide explanations for over 94% of data accesses in the log.Comment: VLDB201

Protecting Patient Privacy: Strategies for Regulating Electronic Health Records Exchange

The report offers policymakers 10 recommendations to protect patient privacy as New York state develops a centralized system for sharing electronic medical records. Those recommendations include:Require that the electronic systems employed by HIEs have the capability to sort and segregate medical information in order to comply with guaranteed privacy protections of New York and federal law. Presently, they do not.Offer patients the right to opt-out of the system altogether. Currently, people's records can be uploaded to the system without their consent.Require that patient consent forms offer clear information-sharing options. The forms should give patients three options: to opt-in and allow providers access to their electronic medical records, to opt-out except in the event of a medical emergency, or to opt-out altogether.Prohibit and sanction the misuse of medical information. New York must protect patients from potential bad actors--that small minority of providers who may abuse information out of fear, prejudice or malice.Prohibit the health information-sharing networks from selling data. The State Legislature should pass legislation prohibiting the networks from selling patients' private health information