Data mining and analysis of lung cancer data.

Lung cancer is the leading cause of cancer death in the United States and the world, with more than 1.3 million deaths worldwide per year. However, because of a lack of effective tools to diagnose Lung Cancer, more than half of all cases are diagnosed at an advanced stage, when surgical resection is unlikely to be feasible. The main purpose of this study is to examine the relationship between patient outcomes and conditions of the patients undergoing different treatments for lung cancer and to develop models to predict the mortality of lung cancer. This study will identify the demographic, finance, and clinical factors related to the diagnosis or mortality of Lung Cancer to help physicians and patients in their decision-making. We combined Text Miner and Cluster analysis to identify the claim data for Lung Cancer and to determine the category of diagnosis, treatment procedures and medication treatments for those patients. Moreover, the claims data were used to define severity level and treatment categories. Compared with using diagnosis codes directly, the combination of text mining and cluster analysis is more efficient and captures more useful information for further analysis. In order to analyze the mortality of Lung Cancer, we also found that survival analysis is appropriate to preprocess the data for the relationship between a predictor variable of interest and the time of an event. The proportional hazard model examined the effects of different treatment clusters using a hazard ratio and the proportional effect of a treatment cluster (treatment procedure or medication treatment) may vary with time. A decision tree was built to generate rules for identifying high risk lung cancer cases among the regular inpatient population. Two primary data sets have been used in this study, the Nationwide Inpatient Sample (NIS) and the Thomson MedStat MarketScan data. Kernel density estimation was used for NIS to examine the relationship between Age, Length of stay, Diagnosis Categories, Total Cost and Lung Cancer by visualization. The Kaplan-Meier method and Cox proportional hazard model are used for the Medstat data to discover the relationship between the factors and the target variable for more detail. Time series and predictive modeling are used to predict the total cost for hospital decision making, the mortality of Lung cancer based on the historical data and to generate rules to identify the diagnosis of Lung cancer. Older patients are more likely to have lung cancers that would lead to a higher probability of longer stay and higher costs for the treatment. Within 7 defined clusters of diagnosis for Lung Cancer, the malignant neoplasm of lobe, bronchus or lung is under higher risk. Age, length of stay, admit type, clusters of diagnosis, and clusters of treatment procedures and Major Diagnostic Categories (MDC) were identified as significant factors for the mortality of lung cancer

Analysis of Atrial Electrograms

This work provides methods to measure and analyze features of atrial electrograms - especially complex fractionated atrial electrograms (CFAEs) - mathematically. Automated classification of CFAEs into clinical meaningful classes is applied and the newly gained electrogram information is visualized on patient specific 3D models of the atria. Clinical applications of the presented methods showed that quantitative measures of CFAEs reveal beneficial information about the underlying arrhythmia

Hypotheses engine (HypE): exploring structured biomedical datasets in search for predictive patterns

Nowadays, healthcare facilities constantly collect an immense amount of data as part of their daily-management systems, which include diverse type of information, such as patient admission details, drugs administered or clinical examinations’ results. Even though medical research has been traditionally condition-oriented, researchers oftentimes use similar analysis methodologies, with very little context customization, making them computationally redundant. This project proposes an analysis pipeline capable of automatically mine big and diverse biomedical datasets, and identify potentially interesting patterns in the data, despite of the medical conditions the data might relate to. Such system is called an hypotheses engine, as its purpose is to output patterns that seem to be medically predictive, which we call hypotheses. HypE’s novelty is two-fold: on one hand, a tailored data processing method was developed for analyzing inconsistent and chaotic temporal data (i.e. a patient has laboratory measurements, that usually are only partially repeated over time); and on the other hand, the hypotheses found are to be outputted in a physician-friendly way, to allow fast understanding of the patterns found, in case medical intervention is recommended. Given HypE’s functionality, results cannot be straightforwardly classified as good or bad, as certain data subsets might actually not contain any patterns, at all. However, methodologically, it is to expect that some hypotheses found will be known medical patterns. Thus, HypE’s outputs are presented and discussed on a high level, considering no manual check for their medical validity was performed by medical experts. The prototype implemented was ran on MIMIC-III data and the results exceeded the initial expectations as they did include common medical scenarios

Medical geography in public health and tropical medicine: case studies from Brazil

Within the last few decades, the multitude of infrastructural and environmental changes associated with population growth, human migration, and economic development have catalyzed the emergence and re-emergence of many infectious diseases worldwide. The morbidity and mortality associated with these diseases have in turn led to an increased and renewed impetus to gain a better understanding of the etiology, epidemiology, prevention, and control of these diseases in order to achieve better health and well-being, especially for underprivileged populations. Two traditionally separate fields, medical geography and tropical medicine, have recently seen complex and radical paradigm shifts in response to this global situation: medical geography has been developing many new and sophisticated methods of data collection, data manipulation, and spatial analysis that make it more suited for the study of health-related problems; and tropical medicine has been revisiting the fundamental notion that disease is intimately linked to the physical and cultural geographic environments in which humans live. As a result, concepts of medical geography are being more readily employed within tropical disease research, and tropical medicine is embracing geographic methods as a central mainstay in the control, management, and prevention of tropical diseases. As the associations between these two fields continue to grow, a clearer understanding of how they compliment each other will be needed in order to better define their interrelated roles in augmenting human health. This dissertation examines the multifarious relationships that have developed between the fields of medical geography and tropical medicine in recent years by presenting the reader with a brief history of their common origins and a comprehensive review of the techniques and methodologies in medical geography that are frequently employed in tropical disease research. Following this background information, several case studies are investigated that provide examples of how geographic methods can be easily and effectively employed in the analysis of several tropical diseases, including tungiasis, intestinal helminthes, leprosy, and tuberculosis. These case studies demonstrate some of the advantages and disadvantages of current geographic methods employed in health research, and offer a framework for readers who are interested in applying basic geographic concepts to analyze questions of health

Communications and Methodologies in Crime Geography: Contemporary Approaches to Disseminating Criminal Incidence and Research

Many tools exist to assist law enforcement agencies in mitigating criminal activity. For centuries, academics used statistics in the study of crime and criminals, and more recently, police departments make use of spatial statistics and geographic information systems in that pursuit. Clustering and hot spot methods of analysis are popular in this application for their relative simplicity of interpretation and ease of process. With recent advancements in geospatial technology, it is easier than ever to publicly share data through visual communication tools like web applications and dashboards. Sharing data and results of analyses boosts transparency and the public image of police agencies, an image important to maintaining public trust in law enforcement and active participation in community safety

Statistical and Graph-Based Signal Processing: Fundamental Results and Application to Cardiac Electrophysiology

The goal of cardiac electrophysiology is to obtain information about the mechanism, function, and performance of the electrical activities of the heart, the identification of deviation from normal pattern and the design of treatments. Offering a better insight into cardiac arrhythmias comprehension and management, signal processing can help the physician to enhance the treatment strategies, in particular in case of atrial fibrillation (AF), a very common atrial arrhythmia which is associated to significant morbidities, such as increased risk of mortality, heart failure, and thromboembolic events. Catheter ablation of AF is a therapeutic technique which uses radiofrequency energy to destroy atrial tissue involved in the arrhythmia sustenance, typically aiming at the electrical disconnection of the of the pulmonary veins triggers. However, recurrence rate is still very high, showing that the very complex and heterogeneous nature of AF still represents a challenging problem. Leveraging the tools of non-stationary and statistical signal processing, the first part of our work has a twofold focus: firstly, we compare the performance of two different ablation technologies, based on contact force sensing or remote magnetic controlled, using signal-based criteria as surrogates for lesion assessment. Furthermore, we investigate the role of ablation parameters in lesion formation using the late-gadolinium enhanced magnetic resonance imaging. Secondly, we hypothesized that in human atria the frequency content of the bipolar signal is directly related to the local conduction velocity (CV), a key parameter characterizing the substrate abnormality and influencing atrial arrhythmias. Comparing the degree of spectral compression among signals recorded at different points of the endocardial surface in response to decreasing pacing rate, our experimental data demonstrate a significant correlation between CV and the corresponding spectral centroids. However, complex spatio-temporal propagation pattern characterizing AF spurred the need for new signals acquisition and processing methods. Multi-electrode catheters allow whole-chamber panoramic mapping of electrical activity but produce an amount of data which need to be preprocessed and analyzed to provide clinically relevant support to the physician. Graph signal processing has shown its potential on a variety of applications involving high-dimensional data on irregular domains and complex network. Nevertheless, though state-of-the-art graph-based methods have been successful for many tasks, so far they predominantly ignore the time-dimension of data. To address this shortcoming, in the second part of this dissertation, we put forth a Time-Vertex Signal Processing Framework, as a particular case of the multi-dimensional graph signal processing. Linking together the time-domain signal processing techniques with the tools of GSP, the Time-Vertex Signal Processing facilitates the analysis of graph structured data which also evolve in time. We motivate our framework leveraging the notion of partial differential equations on graphs. We introduce joint operators, such as time-vertex localization and we present a novel approach to significantly improve the accuracy of fast joint filtering. We also illustrate how to build time-vertex dictionaries, providing conditions for efficient invertibility and examples of constructions. The experimental results on a variety of datasets suggest that the proposed tools can bring significant benefits in various signal processing and learning tasks involving time-series on graphs. We close the gap between the two parts illustrating the application of graph and time-vertex signal processing to the challenging case of multi-channels intracardiac signals

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world