Medical Information Representation Framework for Mobile Healthcare

In mobile healthcare, medical information are often expressed in different formats due to the local policies and regulations and the heterogeneity of the applications, systems, and the adopted Information and communication technology. This chapter describes a framework which enables medical information, in particular clinical vital signs and professional annotations, be processed, exchanged, stored and managed modularly and flexibly in a mobile, distributed and heterogeneous environment despite the diversity of the formats used to represent the information. To deal with medical information represented in multiple formats the authors adopt techniques and constructs similar to the ones used on the Internet, in particular, the authors are inspired by the constructs used in multi-media e-mail and audio-visual data streaming standards. They additionally make a distinction of the syntax for data transfer and store from the syntax for expressing medical domain concepts. In this way, they separate the concerns of what to process, exchange and store from how the information can be encoded or transcoded for transfer over the internet. The authors use an object oriented information model to express the domain concepts and their relations while briefly illustrate how framework tools can be used to encode vital sign data for exchange and store in a distributed and heterogeneous environment

Sensing stress: stress detection from physiological variables in controlled and uncontrolled conditions

Recently, general concern about work-related stress is increasing. Chronic stress induces a number of mental and physical health problems that impact personal life, organizations and society. Timely detection and reduction of stress could prevent these health problems and their negative effects. Stress causes rapid activation of the autonomic nervous system and this activation can be measured in a number of physiological variables. The goal of this thesis is:\ud \ud to assess the feasibility of constructing personal models for the relation between mental stress and physiological variables, for use in ambulatory stress management systems.\ud \ud Four studies were performed in which physiological variable were measured, as well as self-reported stress measures and context variables. Stress induced reactions in the physiological variables, but the pattern of the reactions varies from person to person.\ud The main conclusions of this thesis are that using physiological variables for mental stress detection is feasible, personalization is necessary due to large variations among persons, and that ambulatory measurements are feasible if an unobtrusive and low-power sensor is available. The most common features used in stress estimation are blood pressure, heart rate and skin conductance. Other features such as heart rate variability, EMG and temperature are relevant for some subjects, but not for others. Respiration rate could be a useful feature but is heavily influenced by speech.\ud The main difficulty in the research field that needs attention in future work is that there is no recognized reference measure available that is known to resemble actual mental stress level accurately

Case of seasonal reassortant A(H1N2) influenza virus infection, the Netherlands, March 2018.

A seasonal reassortant A(H1N2) influenza virus harbouring genome segments from seasonal influenza viruses A(H1N1)pdm09 (HA and NS) and A(H3N2) (PB2, PB1, PA, NP, NA and M) was identified in March 2018 in a 19-months-old patient with influenza-like illness (ILI) who presented to a general practitioner participating in the routine sentinel surveillance of ILI in the Netherlands. The patient recovered fully. Further epidemiological and virological investigation did not reveal additional cases

Transportability and Implementation Challenges of Early Warning Scores for Septic Shock in the ICU: A Perspective on the TREWScore

The increased use of electronic health records (EHRs) has improved the availability of routine care data for medical research. Combined with machine learning techniques this has spurred the development of early warning scores (EWSs) in hospitals worldwide. EWSs are commonly used in the hospital where they have been developed, yet few have been transported to external settings and/or internationally. In this perspective, we describe our experiences in implementing the TREWScore, a septic shock EWS, and the transportability challenges regarding domain, predictors, and clinical outcome we faced. We used data of 53,330 ICU stays from Medical Information Mart for Intensive Care-III (MIMIC-III) and 18,013 ICU stays from the University Medical Center (UMC) Utrecht, including 17,023 (31.9%) and 2,557 (14.2%) cases of sepsis, respectively. The MIMIC-III and UMC populations differed significantly regarding the length of stay (6.9 vs. 9.0 days) and hospital mortality (11.6% vs. 13.6%). We mapped all 54 TREWScore predictors to the UMC database: 31 were readily available, seven required unit conversion, 14 had to be engineered, one predictor required text mining, and one predictor could not be mapped. Lastly, we classified sepsis cases for septic shock using the sepsis-2 criteria. Septic shock populations (UMC 31.3% and MIMIC-III 23.3%) and time to shock events showed significant differences between the two cohorts. In conclusion, we identified challenges to transportability and implementation regarding domain, predictors, and clinical outcome when transporting EWS between hospitals across two continents. These challenges need to be systematically addressed to improve model transportability between centers and unlock the potential clinical utility of EWS

Case of seasonal reassortant a(H1N2) influenza virus infection, the Netherlands, March 2018

A seasonal reassortant A(H1N2) influenza virus harbouring genome segments from seasonal influenza viruses A(H1N1)pdm09 (HA and NS) and A(H3N2) (PB2, PB1, PA, NP, NA and M) was identified in March 2018 in a 19-months-old patient with influenza-like illness (ILI) who presented to a general practitioner participating in the routine sentinel surveillance of ILI in the Netherlands. The patient recovered fully. Further epidemiological and virological investigation did not reveal additional cases

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

La reducción de los costes de transporte en España (1800-1936)

This paper describes the improvement that took place in the Spanish transport system between 1800 and 1936. The text points out that, despite the investment efforts that were carried out between 1840 and 1855, the process of transport cost reduction only experienced substantial progress after 1855. The largest transport cost decrease of the period under consideration took place during the three decades between 1855 and the great depression of the late nineteenth century, through the substitution of the railroad for the traditional transport means in the main routes of the country, as well as through the gradual reduction of the price of railway transport. The process went on more slowly later on, thanks to the construction of additional raillway lines (until 1895) and the enlargement of the secondary road network. The process of transport cost reduction accelerated again from the 1920s onwards, thanks to the diffusion of the automobile technology

Wearable physiological sensors reflect mental stress state in office-like situations

Timely mental stress detection can help to prevent stress-related health problems. The aim of this study was to identify those physiological signals and features suitable for detecting mental stress in office-like situations. Electrocardiogram (ECG), respiration, skin conductance and surface electromyogram (sEMG) of the upper trapezius muscle were measured with a wearable system during three distinctive stress tests. The protocol contained stress tests that were designed to represent office-like situations. Generalized Estimating Equations were used to classify the data into rest and stress conditions. We reached an average classification rate of 74.5%. This approach may be used for continuous stress measurement in daily office life to detect mental stress at an early stage

Low-power wearable sensing for preventive healthcare

Low-power wearable sensing will soon allow the quantitative and continuous measurement of health parameters. In this paper we illustrate how wearable sensors can be used to track activity and energy expenditure, and measure stress. Soon such information may empower people in managing their own health, and provide the necessary data to enable a preventive approach to healthcare

Towards ambulatory mental stress measurement from physiological parameters

Ambulatory mental stress monitoring requires longterm physiological measurements. This paper presents a data collection protocol for ambulatory recording of physiological parameters for stress measurement purposes. We present a wearable sensor system for ambulatory recording of ECG, EMG, respiration and skin conductance. The system also records various context parameters: acceleration, temperature and relative humidity. We show that the sensor system is capable of long-term, noninvasive, nonobtrusive, wireless physiological monitoring. We also show some preliminary results of a stress estimation method. These results reveal already a number of context-related issues we will have to take into account in future work. The presented sensor system enables physiological and context data collection and further development of personalized real-time stress detection algorithms