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Relevant bibliographies by topics / Patient data

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Journal articles

Academic literature on the topic 'Patient data'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 June 2025

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Journal articles on the topic "Patient data"

1

McCoy, Matthew S., Steven Joffe, and Ezekiel J. Emanuel. "Sharing Patient Data Without Exploiting Patients." JAMA 323, no. 6 (2020): 505. http://dx.doi.org/10.1001/jama.2019.22354.

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2

Jolly, Damian. "Recording patient data." Emergency Nurse 12, no. 8 (2004): 11. http://dx.doi.org/10.7748/en.12.8.11.s17.

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3

Goymer, Luke. "Recording patient data." Emergency Nurse 12, no. 9 (2005): 9. http://dx.doi.org/10.7748/en.12.9.9.s11.

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4

Straseski, Joely A., and Frederick G. Strathmann. "Patient Data Algorithms." Clinics in Laboratory Medicine 33, no. 1 (2013): 147–60. http://dx.doi.org/10.1016/j.cll.2012.11.009.

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5

Nieri, Michele, Carlo Clauser, Umberto Pagliaro, and Giovanpaolo PiniPrato. "Individual patient data." Journal of Evidence Based Dental Practice 3, no. 3 (2003): 122–26. http://dx.doi.org/10.1016/s1532-3382(03)00070-8.

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6

Contreras, Jorge L., John Rumbold, and Barbara Pierscionek. "Patient Data Ownership." JAMA 319, no. 9 (2018): 935. http://dx.doi.org/10.1001/jama.2017.21672.

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7

Sadan, Batami. "Patient data confidentiality and patient rights." International Journal of Medical Informatics 62, no. 1 (2001): 41–49. http://dx.doi.org/10.1016/s1386-5056(00)00135-0.

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8

Pearce, Christopher, Adam McCleod, and Jason Ferrigi. "From linked data to patient centred data: using health data to improve patient outcomes." International Journal of Integrated Care 20, no. 3 (2021): 39. http://dx.doi.org/10.5334/ijic.s4039.

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9

Menaka, Dr S. R., M. Gokul Raj, P. Elakiya Selvan, G. Tharani Kumar, and M. Yashika. "A Sensor based Data Analytics for Patient Monitoring Using Data Mining." International Academic Journal of Innovative Research 9, no. 1 (2022): 28–36. http://dx.doi.org/10.9756/iajir/v9i1/iajir0905.

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Abstract:

Remote sensor networks have been broadly utilized in medical care applications, like emergency clinic and home patient observing. A great deal of work has been done to get remote clinical sensor organizations. The current arrangements can safeguard the patient information during transmission, yet cant stop within assault where the manager of the patient data set uncovers the touchy patient information. In this paper, we propose a useful way to deal with forestall within assault by utilizing different information servers to store patient information. The fundamental commitment of this paper is safely disseminating the patient information in various information servers and utilizing the AES to perform measurement investigation on the patient information without compromising the patients security. Remote clinical sensor networks surely work on understandings nature of-care without upsetting their solace. Nonetheless, there exist numerous potential security dangers to the patient delicate physiological information communicated over the public diverts and put away in the back-end frameworks. Average security dangers to medical care applications with WSNs can be summed up as follows. Snoopping is a security danger to the patient information protection A busybody, having a strong beneficiary recieving wire, might have the option to catch the patient information from the clinical sensors and accordingly realizes the patients medical issue. He might even post the patients medical issue on interpersonal organization, which can represent a genuine danger to patient security. Pantomime is a security danger to the patient information credibility.

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10

AL-Mafrji, Ahmad Abdullah Mohammed, and Ahmed Burhan Mohammed. "Analysis of Patients Data Using Fuzzy Expert System." Webology 19, no. 1 (2022): 4027–34. http://dx.doi.org/10.14704/web/v19i1/web19265.

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Abstract:

Many problems are facing many developed and developing countries in the medical field, and the most important of these problems is the analysis and diagnosis of patient data for government and private hospitals. This is due to the lack of experience of medical staff, especially new ones, which affects the provision of correct medical services to patients. It is no secret that these countries are making great efforts to overcome these problems. The study focuses on the use of a fuzzy expert system to analyze patient data based on (age, type of review) to reach the result of the analysis (intensive care, medium care, no care) and this system helps to give advice and good analysis of patient data, which can increase the speed of gaining experience for new and inexperienced medical staff in this field.

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