Data Mining - Concepts and Techniques.

The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.

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Big Data Analytics in Healthcare

Process mining in healthcare

The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems.

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Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges

Rationale: Machine learning may be useful to characterize cardiovascular risk, predict outcomes, and identify biomarkers in population studies. Objective: To test the ability of random survival forests, a machine learning technique, to predict 6 cardiovascular outcomes in comparison to standard cardiovascular risk scores. Methods and Results: We included participants from the MESA (Multi-Ethnic Study of Atherosclerosis). Baseline measurements were used to predict cardiovascular outcomes over 12 years of follow-up. MESA was designed to study progression of subclinical disease to cardiovascular events where participants were initially free of cardiovascular disease. All 6814 participants from MESA, aged 45 to 84 years, from 4 ethnicities, and 6 centers across the United States were included. Seven-hundred thirty-five variables from imaging and noninvasive tests, questionnaires, and biomarker panels were obtained. We used the random survival forests technique to identify the top-20 predictors of each outcome. Imaging, electrocardiography, and serum biomarkers featured heavily on the top-20 lists as opposed to traditional cardiovascular risk factors. Age was the most important predictor for all-cause mortality. Fasting glucose levels and carotid ultrasonography measures were important predictors of stroke. Coronary Artery Calcium score was the most important predictor of coronary heart disease and all atherosclerotic cardiovascular disease combined outcomes. Left ventricular structure and function and cardiac troponin-T were among the top predictors for incident heart failure. Creatinine, age, and ankle-brachial index were among the top predictors of atrial fibrillation. TNF-α (tissue necrosis factor-α) and IL (interleukin)-2 soluble receptors and NT-proBNP (N-Terminal Pro-B-Type Natriuretic Peptide) levels were important across all outcomes. The random survival forests technique performed better than established risk scores with increased prediction accuracy (decreased Brier score by 10%–25%). Conclusions: Machine learning in conjunction with deep phenotyping improves prediction accuracy in cardiovascular event prediction in an initially asymptomatic population. These methods may lead to greater insights on subclinical disease markers without apriori assumptions of causality. Clinical Trial Registration: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00005487.

Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis.

As a new concept that emerged in the middle of 1990's, data mining can help researchers gain both novel and deep insights and can facilitate unprecedented understanding of large biomedical datasets. Data mining can uncover new biomedical and healthcare knowledge for clinical and administrative decision making as well as generate scientific hypotheses from large experimental data, clinical databases, and/or biomedical literature. This review first introduces data mining in general (e.g., the background, definition, and process of data mining), discusses the major differences between statistics and data mining and then speaks to the uniqueness of data mining in the biomedical and healthcare fields. A brief summarization of various data mining algorithms used for classification, clustering, and association as well as their respective advantages and drawbacks is also presented. Suggested guidelines on how to use data mining algorithms in each area of classification, clustering, and association are offered along with three examples of how data mining has been used in the healthcare industry. Given the successful application of data mining by health related organizations that has helped to predict health insurance fraud and under-diagnosed patients, and identify and classify at-risk people in terms of health with the goal of reducing healthcare cost, we introduce how data mining technologies (in each area of classification, clustering, and association) have been used for a multitude of purposes, including research in the biomedical and healthcare fields. A discussion of the technologies available to enable the prediction of healthcare costs (including length of hospital stay), disease diagnosis and prognosis, and the discovery of hidden biomedical and healthcare patterns from related databases is offered along with a discussion of the use of data mining to discover such relationships as those between health conditions and a disease, relationships among diseases, and relationships among drugs. The article concludes with a discussion of the problems that hamper the clinical use of data mining by health professionals.

Data Mining in Healthcare and Biomedicine: A Survey of the Literature

This study examined DNA methylation associated with acute lymphoblastic leukemia (ALL) and showed that selected molecular targets can be pharmacologically modulated to reverse gene silencing. A CpG island (CGI) microarray containing more than 3,400 unique clones that span all human chromosomes was used for large-scale discovery experiments and led to 262 unique CGI loci being statistically identified as methylated in ALL lymphoblasts. The methylation status of 10 clones encompassing 11 genes (DCC, DLC-1, DDX51, KCNK2, LRP1B, NKX6-1, NOPE, PCDHGA12, RPIB9, ABCB1, and SLC2A14) identified as differentially methylated between ALL patients and controls was independently verified. Consequently, the methylation status of DDX51 was found to differentiate patients with B- and T-ALL subtypes (P = 0.011, Fisher's exact test). Next, the relationship between methylation and expression of these genes was examined in ALL cell lines (NALM-6 and Jurkat) before and after treatments with 5-aza-2-deoxycytidine and trichostatin A. More than a 10-fold increase in mRNA expression was observed for two previously identified tumor suppressor genes (DLC-1 and DCC) and also for RPIB9 and PCDHGA12. Although the mechanisms that lead to the CGI methylation of these genes are unknown, bisulfite sequencing of the promoter of RPIB9 suggests that expression is inhibited by methylation within SP1 and AP2 transcription factor binding motifs. Finally, specific chromosomal methylation hotspots were found to be associated with ALL. This study sets the stage for acquiring a better biological understanding of ALL and for the identification of epigenetic biomarkers useful for differential diagnosis, therapeutic monitoring, and the detection of leukemic relapse.

https://cancerres.aacrjournals.org/content/canres/67/6/2617.full.pdf

Large-scale CpG methylation analysis identifies novel candidate genes and reveals methylation hotspots in acute lymphoblastic leukemia.

Epigenetic studies in cancer pathways have been essential in helping scientists understand the key players in cancer. Gene relationships reported in biomedical literature are valuable to understand the interaction network. Nevertheless, biomedical literature is growing rapidly and the scientific community needs a mechanism to have up-to-date pathways that reflect the new findings from the literature. In this work, we are developing informatics tools to extract gene relationship from literature using text mining and semantic understanding.

Keila E. Pena-Hernandez

Papers

Data Mining in Healthcare and Biomedicine: A Survey of the Literature

Large-scale CpG methylation analysis identifies novel candidate genes and reveals methylation hotspots in acute lymphoblastic leukemia.

Mapping biomedical literature with WNT signaling pathway.