Abstract: The Arterial Pressure Waveform (APW) can provide essential information about arterial wall integrity and arterial stiffness. Most of APW analysis frameworks individually process each hemodynamic parameter and do not evaluate inter-dependencies in the overall pulse morphology. The key contribution of this work is the use of machine learning algorithms to deal with vectorized features extracted from APW. With this purpose, we follow a five-step evaluation methodology: (1) a custom-designed, non-invasive, electromechanical device was used in the data collection from 50 subjects; (2) the acquired position and amplitude of onset, Systolic Peak (SP), Point of Inflection (Pi) and Dicrotic Wave (DW) were used for the computation of some morphological attributes; (3) pre-processing work on the datasets was performed in order to reduce the number of input features and increase the model accuracy by selecting the most relevant ones; (4) classification of the dataset was carried out using four different machine learning algorithms: Random Forest, BayesNet (probabilistic), J48 (decision tree) and RIPPER (rule-based induction); and (5) we evaluate the trained models, using the majority-voting system, comparatively to the respective calculated Augmentation Index (AIx). Classification algorithms have been proved to be efficient, in particular Random Forest has shown good accuracy (96.95%) and high area under the curve (AUC) of a Receiver Operating Characteristic (ROC) curve (0.961). Finally, during validation tests, a correlation between high risk labels, retrieved from the multi-parametric approach, and positive AIx values was verified. This approach gives allowance for designing new hemodynamic morphology vectors and techniques for multiple APW analysis, thus improving the arterial pulse understanding, especially when compared to traditional single-parameter analysis, where the failure in one parameter measurement component, such as Pi, can jeopardize the whole evaluation.
Abstract: The Oslo University Hospital (Norway), the K.G. Jebsen Centre for Breast Cancer Research (Norway), The Radiumhospital Foundation (Norway) and the Fritz-Bender-Foundation (Germany) designed under the conference chairmen (E. Mihich, K.S. Zänker, A.L. Borresen-Dale) and advisory committee (A. Borg, Z. Szallasi, O. Kallioniemi, H.P. Huber) a program at the cutting edge of “PERSONALIZED CANCER CARE: Risk prediction, early diagnosis, progression and therapy resistance.” The conference was held in Oslo from September 7 to 9, 2012 and the science-based presentations concerned six scientific areas: (1) Genetic profiling of patients, prediction of risk, late side effects; (2) Molecular profiling of tumors and metastases; (3) Tumor-host microenvironment interaction and metabolism; (4) Targeted therapy; (5) Translation and (6) Informed consent, ethical challenges and communication. Two satellite workshops on (i) Ion Ampliseq—a novel tool for large scale mutation detection; and (ii) Multiplex RNA ISH and tissue homogenate assays for cancer biomarker validation were additionally organized. The report concludes that individual risk prediction in carcinogenesis and/or metastatogenesis based on polygenic profiling may be useful for intervention strategies for health care and therapy planning in the future. To detect distinct and overlapping DNA sequence alterations in tumor samples and adjacent normal tissues, including point mutations, small insertions or deletions, copy number changes and chromosomal rearrangements will eventually make it possible to design personalized management plans for individualized patients. However, large individualized datasets need a new approach in bio-information technology to reduce this enormous data dimensionally to simply working hypotheses about health and disease for each individual.
Abstract: Rapid progress in genomics and nanotechnology continue to advance our approach to patient care, from diagnosis and prognosis, to targeting and personalization of therapeutics. However, the clinical application of molecular diagnostics in ophthalmology has been limited even though there have been demonstrations of disease risk and pharmacogenetic associations. There is a high clinical need for therapeutic personalization and dosage optimization in ophthalmology and may be the focus of individualized medicine in this specialty. In several retinal conditions, such as age-related macular degeneration, diabetic macular edema, retinal vein occlusion and pre-threshold retinopathy of prematurity, anti-vascular endothelial growth factor therapeutics have resulted in enhanced outcomes. In glaucoma, recent advances in cytoskeletal agents and prostaglandin molecules that affect outflow and remodel the trabecular meshwork have demonstrated improved intraocular pressure control. Application of recent developments in nanoemulsion and polymeric micelle for targeted delivery and drug release are models of dosage optimization, increasing efficacy and improving outcomes in these major eye diseases.
Abstract: Sensors have become ubiquitous in their reach and scope of application. They are a technological cornerstone for various modes of health surveillance and participatory medicine—such as quantifying oneself; they are also employed to track people with certain as impairments perceived ability differences. This paper presents quantitative and qualitative data of an exploratory, non-generalizable study into the perceptions, attitudes and concerns of staff of a disability service organization, that mostly serve people with intellectual disabilities, towards the use of various types of sensor technologies that might be used by and with their clients. In addition, perspectives of various types of privacy issues linked to sensors, as well data regarding the concept of quantified self were obtained. Our results highlight the need to involve disabled people and their support networks in sensor and quantified-self discourses, in order to prevent undue disadvantages.
Abstract: Personalized medicine can be seen as a continuously developing approach to tailoring treatments according to the individual characteristics of a patient. In some way, medicine has always been personalized. During the last decade, however, scientific and technological progress have made truly personalized healthcare increasingly become reality. Today’s personalized medicine involves targeted therapies and diagnostic tests. The development of targeted agents represents a major investment opportunity to pharmaceutical companies, which have been facing the need to diversify their business due to an increasingly challenging market place. By investing into the development of personalized therapies, pharmaceutical companies mitigate a major part of the risks posed by factors such as patent expiries or generic competition. Viewing upon personalized medicine from different perspectives points out the multi-causality of its emergence. Research efforts and business diversification have been two main driving forces; they do supplement each other, however, are not jointly exhaustive in explaining the emergence of this approach. Especially in the future, a number of further stakeholders will impact the evolution of personalized medicine.
Abstract: Cancer care is often inconsistently delivered with inadequate incorporation of patient values and objective evidence into decision-making. Utilization of time limited trials of care with predefined decision points that are based on iteratively updated best evidence, tools that inform providers about a patient’s experience and values, and known information about a patient’s disease will allow superior matched care to be delivered. Personalized medicine does not merely refer to the incorporation of genetic information into clinical care, it involves utilization of the wide array of data points relevant to care, many of which are readily available at the bedside today. By pushing uptake of personalized matching available today, clinicians can better address the triple aim of improved health, lowers costs, and enhanced patient experience, and we can prepare the health care landscape for the iterative inclusion of progressively more sophisticated information as newer tests and information become available to support the personalized medicine paradigm.