DISMON

2011 ◽  
pp. 995-1007
Author(s):  
Ángel M. Lagares-Lemos ◽  
Miguel Lagares-Lemos ◽  
Ricardo Colomo-Palacios ◽  
Ángel García-Crespo ◽  
Juan Miguel Gómez-Berbís
Keyword(s):  
Medical Diagnosis ◽  
Medical Information ◽  
Clinical Symptoms ◽  
Improve Patient Care ◽  
Social Dimension ◽  
The Internet ◽  
Semantic Technologies ◽  
Medical Information Systems ◽  
Challenges And Opportunities ◽  
Improve Patient

Information technology and, more precisely, the internet represent challenges and opportunities for medicine. Technology-driven medicine has changed how practitioners perform their roles in and medical information systems have recently gained momentum as a proof-of-concept of the efficiency of new support-oriented technologies. Emerging applications combine sharing information with a social dimension. This paper presents DISMON (Disease Monitor), a system based on Semantic Technologies and Social Web (SW) to improve patient care for medical diagnosis in limited environments, namely, organizations. DISMON combines Web 2.0 capacities and SW to provide semantic descriptions of clinical symptoms, thereby facilitating diagnosis and helping to foresee diseases, giving useful information to the company and its employees to increase efficiency by means of the prevention of injuries and illnesses, resulting in a safety environment for workers.

DISMON

2013 ◽  
pp. 48-59
Author(s):  
Ángel M. Lagares-Lemos ◽  
Miguel Lagares-Lemos ◽  
Ricardo Colomo-Palacios ◽  
Ángel García-Crespo ◽  
Juan Miguel Gómez-Berbís
Keyword(s):  
Medical Diagnosis ◽  
Medical Information ◽  
Clinical Symptoms ◽  
Improve Patient Care ◽  
Social Dimension ◽  
The Internet ◽  
Semantic Technologies ◽  
Medical Information Systems ◽  
Challenges And Opportunities ◽  
Improve Patient

Information technology and, more precisely, the internet represent challenges and opportunities for medicine. Technology-driven medicine has changed how practitioners perform their roles in and medical information systems have recently gained momentum as a proof-of-concept of the efficiency of new support-oriented technologies. Emerging applications combine sharing information with a social dimension. This paper presents DISMON (Disease Monitor), a system based on Semantic Technologies and Social Web (SW) to improve patient care for medical diagnosis in limited environments, namely, organizations. DISMON combines Web 2.0 capacities and SW to provide semantic descriptions of clinical symptoms, thereby facilitating diagnosis and helping to foresee diseases, giving useful information to the company and its employees to increase efficiency by means of the prevention of injuries and illnesses, resulting in a safety environment for workers.

DISMON

2011 ◽  
Vol 4 (1) ◽  
pp. 48-59 ◽  
Author(s):  
Ángel M. Lagares-Lemos ◽  
Miguel Lagares-Lemos ◽  
Ricardo Colomo-Palacios ◽  
Ángel García-Crespo ◽  
Juan Miguel Gómez-Berbís
Keyword(s):  
Medical Information ◽  
Clinical Symptoms ◽  
Improve Patient Care ◽  
Social Dimension ◽  
Social Web ◽  
Semantic Technologies ◽  
Proof Of Concept ◽  
Medical Information Systems ◽  
Challenges And Opportunities ◽  
Improve Patient

Information technology and, more precisely, the internet represent challenges and opportunities for medicine. Technology-driven medicine has changed how practitioners perform their roles in and medical information systems have recently gained momentum as a proof-of-concept of the efficiency of new support-oriented technologies. Emerging applications combine sharing information with a social dimension. This paper presents DISMON (Disease Monitor), a system based on Semantic Technologies and Social Web (SW) to improve patient care for medical diagnosis in limited environments, namely, organizations. DISMON combines Web 2.0 capacities and SW to provide semantic descriptions of clinical symptoms, thereby facilitating diagnosis and helping to foresee diseases, giving useful information to the company and its employees to increase efficiency by means of the prevention of injuries and illnesses, resulting in a safety environment for workers.

Locating Doctors using Social and Semantic Web Technologies

2011 ◽  
pp. 94-106
Author(s):  
Alejandro Rodríguez-González ◽  
Ángel García-Crespo ◽  
Ricardo Colomo-Palacios ◽  
José Emilio Labra-Gayo ◽  
Juan Miguel Gómez Berbís
Keyword(s):  
Medical Diagnosis ◽  
Geographical Information Systems ◽  
Improve Patient Care ◽  
User Preferences ◽  
Geographical Information ◽  
Social Web ◽  
Semantic Technologies ◽  
Semantic Web Technologies ◽  
Knowledge Based ◽  
Promising Tool

The advent of the information age represents both a challenge and an opportunity for medicine. New forms of diagnosis, innovation-oriented supervision and expert location paths are deeply impacting medical sciences as we know it around the word. In this new scenario, semantic technologies can be seen as new and promising tool to support knowledge-based services, and particularly for the health domain, medical diagnosis. This chapter presents MedFinder, a system based on semantic technologies and social Web to improve patient care for medical diagnosis. The main breakthroughs of MedFinder are the follow-up once the diagnosis is performed, by using a medical ontology and formal reasoning together with rules, since it makes possible to locate the most appropriate doctor for a patient using Geographical Information Systems (GIS) and taking into account user preferences given via social Web feedback.

scholarly journals A Preliminary Scoping Study of Federated Learning for the Internet of Medical Things

2021 ◽  
Author(s):  
Arshad Farhad ◽  
Sandra I. Woolley ◽  
Peter Andras
Keyword(s):  
Patient Care ◽  
Scoping Review ◽  
Research Work ◽  
Improve Patient Care ◽  
The Internet ◽  
Scoping Study ◽  
Internet Of Medical Things ◽  
Improve Patient

This paper presents a scoping review of federated learning for the Internet of Medical Things (IoMT) and demonstrates the limited amount of research work in an area which has potential to improve patient care. Federated Learning and IoMT – as standalone technologies – have already proved to be highly disruptive but there is a need for further research to apply federated learning to the IoMT.

Why Is Everyone Giving up on Our Son?

2020 ◽  
pp. 7-12
Author(s):  
Lindsay B. Ragsdale
Keyword(s):  
Social Media ◽  
Information Exchange ◽  
Medical Information ◽  
Medical Training ◽  
Improve Patient Care ◽  
Multiple Routes ◽  
Clear Communication ◽  
Medical Teams ◽  
To Receive ◽  
Improve Patient

Understanding complex medical information can be challenging for patients and families. Especially with the large amount of information available online and in social media, maintaining clear communication and a concise plan of care can be challenging. Medical training teaches clinicians to ask questions pertaining to patients’ disease and symptoms, but this training commonly fails to prepare clinicians to assess how patients would choose to receive medical information. Medical teams should ask how patients and caregivers prefer to receive information and assess understanding of the information after tailoring the delivery to their preferences. They should allow for multiple routes of information exchange, which may vary between individuals in a family. Cultural and spiritual influences can impact understanding of medical information. Improved communication can clarify goals, improve patient care, and avoid conflicts.

A Comprehensive Model for Medical Information Processing

1983 ◽  
Vol 22 (03) ◽  
pp. 124-130 ◽  
Author(s):  
J. H. Bemmel
Keyword(s):  
Intensive Care ◽  
Information Processing ◽  
Medical Information ◽  
Computer Applications ◽  
Human Interaction ◽  
Comprehensive Model ◽  
Computers In Medicine ◽  
Medical Information Systems ◽  
Research And Education ◽  
The Many

At first sight, the many applications of computers in medicine—from payroll and registration systems to computerized tomography, intensive care and diagnostics—do make a rather chaotic impression. The purpose of this article is to propose a scheme or working model for putting medical information systems in order. The model comprises six »levels of complexity«, running parallel to dependence on human interaction. Several examples are treated to illustrate the scheme. The reason why certain computer applications are more frequently used than others is analyzed. It has to be strongly considered that the differences in complexity and dependence on human involvement are not accidental but fundamental. This has consequences for research and education which are also discussed.

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