Procedure of Medical Diagnosis

Drawing flow diagrams is an effective strategy to extract rules for designing an intelligent system. Physicians diagnose the diseases based on flow diagrams. In this chapter, the procedure and required steps for medical diagnosis are explained and the reader can learn the way to find the knowledge from the medical experts to extract the data for the intelligent system. Examples of some flowcharts from the California Department of Health Service are provided to show how the designer should work with the medical data. Medical data and types of patient information are described.

Epidemiology ◽  
1992 ◽  
Vol 3 (2) ◽  
pp. 83-93 ◽  
Author(s):  
Shanna H. Swan ◽  
Raymond R. Neutra ◽  
Margaret Wrensch ◽  
Irva Hertz-Picciotto ◽  
Gayle C. Windham ◽  
...  

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
I Mircheva ◽  
M Mirchev

Abstract Background Ownership of patient information in the context of Big Data is a relatively new problem, apparently not yet fully understood. There are not enough publications on the subject. Since the topic is interdisciplinary, incorporating legal, ethical, medical and aspects of information and communication technologies, a slightly more sophisticated analysis of the issue is needed. Aim To determine how the medical academic community perceives the issue of ownership of patient information in the context of Big Data. Methods Literature search for full text publications, indexed in PubMed, Springer, ScienceDirect and Scopus identified only 27 appropriate articles authored by academicians and corresponding to three focus areas: problem (ownership); area (healthcare); context (Big Data). Three major aspects were studied: scientific area of publications, aspects and academicians' perception of ownership in the context of Big Data. Results Publications are in the period 2014 - 2019, 37% published in health and medical informatics journals, 30% in medicine and public health, 19% in law and ethics; 78% authored by American and British academicians, highly cited. The majority (63%) are in the area of scientific research - clinical studies, access and use of patient data for medical research, secondary use of medical data, ethical challenges to Big data in healthcare. The majority (70%) of the publications discuss ownership in ethical and legal aspects and 67% see ownership as a challenge mostly to medical research, access control, ethics, politics and business. Conclusions Ownership of medical data is seen first and foremost as a challenge. Addressing this challenge requires the combined efforts of politicians, lawyers, ethicists, computer and medical professionals, as well as academicians, sharing these efforts, experiences and suggestions. However, this issue is neglected in the scientific literature. Publishing may help in open debates and adequate policy solutions. Key messages Ownership of patient information in the context of Big Data is a problem that should not be marginalized but needs a comprehensive attitude, consideration and combined efforts from all stakeholders. Overcoming the challenge of ownership may help in improving healthcare services, medical and public health research and the health of the population as a whole.


2020 ◽  
Vol 6 (2) ◽  
pp. 90-97
Author(s):  
Sagir Masanawa ◽  
Hamza Abubakar

In this paper, a hybrid intelligent system that consists of the sparse matrix approach incorporated in neural network learning model as a decision support tool for medical data classification is presented. The main objective of this research is to develop an effective intelligent system that can be used by medical practitioners to accelerate diagnosis and treatment processes. The sparse matrix approach incorporated in neural network learning algorithm for scalability, minimize higher memory storage capacity usage, enhancing implementation time and speed up the analysis of the medical data classification problem. The hybrid intelligent system aims to exploit the advantages of the constituent models and, at the same time, alleviate their limitations. The proposed intelligent classification system maximizes the intelligently classification of medical data and minimizes the number of trends inaccurately identified. To evaluate the effectiveness of the hybrid intelligent system, three benchmark medical data sets, viz., Hepatitis, SPECT Heart and Cleveland Heart from the UCI Repository of Machine Learning, are used for evaluation. A number of useful performance metrics in medical applications which include accuracy, sensitivity, specificity. The results were analyzed and compared with those from other methods published in the literature. The experimental outcomes positively demonstrate that the hybrid intelligent system was effective in undertaking medical data classification tasks.


PEDIATRICS ◽  
1993 ◽  
Vol 91 (4) ◽  
pp. 854-855
Author(s):  
LYNN R. GOLDMAN

To the Editor.— Dr Edgar J. Schoen does not like lead screening.1 In a letter to Pediatrics, "Lead Toxicity in the 21st Century: Will We Still Be Treating It?" he refers to a study my colleagues at the California Department of Health Services and I conducted in Oakland, California as "a vivid example of how poor methodology and biased selection of subjects can lead to greatly exaggerated prevalence rates," accusing me of having a "serious omission" in a submission to Morbidity and Mortality Weekly Report.


1976 ◽  
Vol 4 (2) ◽  
pp. 137-144
Author(s):  
M L Bissett

This paper reports on the serological and biochemical characteristics of 24 human isolates of Yersinia enterocolitica submitted to the California Department of Health from 1968 through 1975. Nine different serotypes were represented. The majority of strains were serotype O:8 (six strains) and serotype O:5 (five strains). Sources of the isolates included feces (12 cases), blood (3), sputum or throat (3), bile or bowel drainage (2), wounds (2), breast abscess (1), and skin abscess (1). Clinical histories indicated a number of different syndromes. Underlying medical conditions existed in 13 cases. Results of selected biochemical tests and antimicrobial susceptibility tests on the strains indicated grouping compatible with the O serotypes of the organisms.


2010 ◽  
Vol 92 (8) ◽  
pp. 266-268
Author(s):  
Matthew Worrall

Enhanced recovery (ER) is one of the current buzz terms in the health service but it seems to mean a different thing depending on to whom you speak. The Department of Health (DH) invited applications from acute trusts across England to become 'innovation sites' for the enhanced recovery programme. These sites are supported by DH as they implement a defined programme that aims to improve patient experience through shorter hospital stays. The Bulletin spent a day at one of them, West Hertfordshire Hospitals NHS Trust, to witness the changes made.


BMJ ◽  
1980 ◽  
Vol 280 (6224) ◽  
pp. 1180-1182
Author(s):  
M Ryan ◽  
G D Forwell
Keyword(s):  


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