HCloud, a Healthcare-Oriented Cloud System with Improved Efficiency in Biomedical Data Processing

2017 ◽  
pp. 163-192
Author(s):  
Ye Li ◽  
Chenguang He ◽  
Xiaomao Fan ◽  
Xucan Huang ◽  
Yunpeng Cai
Keyword(s):  
Data Processing ◽  
Cloud System ◽  
Biomedical Data

Correct problem statements in biomedical data processing

1976 ◽  
Author(s):  
N. I. Moiseeva ◽  
M. Yu. Simonov ◽  
V. M. Sysuev
Keyword(s):  
Data Processing ◽  
Biomedical Data ◽  
Correct Problem

scholarly journals Medical Informatics in Morocco: Casablanca Medical Informatics Laboratory

2007 ◽  
Vol 16 (01) ◽  
pp. 138-140
Author(s):  
S. Diouny ◽  
K. Balar ◽  
M. Bennani Othmani
Keyword(s):  
Data Processing ◽  
Medical Informatics ◽  
New Technologies ◽  
Research Unit ◽  
Processing Unit ◽  
Biomedical Data ◽  
Professional Activities ◽  
Research Activities ◽  
Medical Data Processing ◽  
International Projects

SummaryIn 2005, Medical Informatics Laboratory (CMIL) became an independent research unit within the Faculty of Medicine and Pharmacy of Casablanca. CMIL is currently run by three persons (a university professor, a data processing specialist and a pedagogical assistant). The objectives of CMIL are to promote research and develop quality in the field of biomedical data processing and health, and integrate new technologies into medical education and biostatistics. It has four units: Telehealth Unit, Network Unit, Biostatistics Unit, Medical data processing Unit.The present article seeks to give a comprehensive account of Casablanca Medical informatics laboratory (CMIL) activities. For ease of exposition, the article consists of four sections: Section I discusses the background of CMIL; section II is devoted to educational activities; section III addresses professional activities; and section IV lists projects that CMIL is involved in.Since its creation, CMIL has been involved in a number of national and international projects, which have a bearing on Telemedicine applications, E-learning skills and data management in medical studies in Morocco.It is our belief that the skills and knowledge gained in the past few years would certainly enrich our research activities, and improve the situation of research in Medical informatics in Morocco.

scholarly journals Architectural concepts for managing biomedical sensor data utilised for medical diagnosis and patient remote care.

2018 ◽  
Vol 210 ◽  
pp. 05016
Author(s):  
Mariusz Chmielewski ◽  
Damian Frąszczak ◽  
Dawid Bugajewski
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Large Scale ◽  
Data Representation ◽  
Medical Diagnostics ◽  
Mobile Technologies ◽  
Sensor Data ◽  
Biomedical Data ◽  
Support Tool ◽  
Decision Algorithms

This paper discusses experiences and architectural concepts developed and tested aimed at acquisition and processing of biomedical data in large scale system for elderly (patients) monitoring. Major assumptions for the research included utilisation of wearable and mobile technologies, supporting maximum number of inertial and biomedical data to support decision algorithms. Although medical diagnostics and decision algorithms have not been the main aim of the research, this preliminary phase was crucial to test capabilities of existing off-the-shelf technologies and functional responsibilities of system’s logic components. Architecture variants contained several schemes for data processing moving the responsibility for signal feature extraction, data classification and pattern recognition from wearable to mobile up to server facilities. Analysis of transmission and processing delays provided architecture variants pros and cons but most of all knowledge about applicability in medical, military and fitness domains. To evaluate and construct architecture, a set of alternative technology stacks and quantitative measures has been defined. The major architecture characteristics (high availability, scalability, reliability) have been defined imposing asynchronous processing of sensor data, efficient data representation, iterative reporting, event-driven processing, restricting pulling operations. Sensor data processing persist the original data on handhelds but is mainly aimed at extracting chosen set of signal features calculated for specific time windows – varying for analysed signals and the sensor data acquisition rates. Long term monitoring of patients requires also development of mechanisms, which probe the patient and in case of detecting anomalies or drastic characteristic changes tune the data acquisition process. This paper describes experiences connected with design of scalable decision support tool and evaluation techniques for architectural concepts implemented within the mobile and server software.

scholarly journals SIproc: an open-source biomedical data processing platform for large hyperspectral images

The Analyst ◽  
2017 ◽  
Vol 142 (8) ◽  
pp. 1350-1357 ◽  
Author(s):  
Sebastian Berisha ◽  
Shengyuan Chang ◽  
Sam Saki ◽  
Davar Daeinejad ◽  
Ziqi He ◽  
...  
Keyword(s):  
Data Processing ◽  
Open Source ◽  
Clinical Diagnosis ◽  
Vibrational Spectroscopy ◽  
Imaging Techniques ◽  
Spectroscopic Imaging ◽  
Hyperspectral Images ◽  
Biomedical Data ◽  
Significant Interest ◽  
Processing Platform

There has recently been significant interest within the vibrational spectroscopy community to apply quantitative spectroscopic imaging techniques to histology and clinical diagnosis.

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