A cognitive technology based healthcare monitoring system and medical data transmission

Kumar, M. Arun; Vimala, R.; Britto, K. R. Aravind

doi:10.1016/j.measurement.2019.03.017

Cited by 29 publications

(10 citation statements)

References 25 publications

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“…An ever-increasing amount of medical data is being recorded by monitoring patient care devices, enabling big data analysis in health care [ 9 ]. This paves the way for the application of different ML techniques like deep learning [ 7 ], traditional shallow neural networks, support vector machines, and random forests, which are specific models for using AI in practice [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey

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Palm³

et al. 2021

J Med Internet Res

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Background The increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of health care. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians’ requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research have not been investigated widely in German university hospitals. Objective This study aimed to evaluate physicians’ requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research (eg, for the development of machine learning algorithms) in university hospitals in Germany. Methods A web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given using Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals. Results The online survey was completed by 303 physicians (female: 121/303, 39.9%; male: 173/303, 57.1%; no response: 9/303, 3.0%) from a wide range of medical disciplines and work experience levels. Most respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. There was a significant association between the personal rating of AI in medicine and the self-reported technical affinity level (H4=48.3, P<.001). A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). Of the respondents, 82.5% (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research. Conclusions Physicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism comes several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (eg, imaging procedures in radiology and pathology) or data are collected continuously (eg, cardiology and intensive care medicine), physicians’ expectations of AI to substantially improve future patient care are high. In the study, the greatest potential was seen in the application of AI for the identification of drug interactions, assumedly due to the rising complexity of drug administration to polymorbid, polypharmacy patients. However, for the practical usage of AI in health care, regulatory and organizational challenges still have to be mastered.

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Section: Introductionmentioning

confidence: 99%

Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey

Maaßen

Fritsch

Palm³

et al. 2021

J Med Internet Res

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“…Kumar et al have proposed a cognitive data transmission method that can effectively detect, record, and analyze patient's health data. During an emergency, the data of the patient, under critical condition, are transmitted with the utmost priority [ 55 ].…”

Section: Services and Application Of Hiotmentioning

confidence: 99%

IoT-Based Applications in Healthcare Devices

Pradhan

Bhattacharyya

Pal

2021

Journal of Healthcare Engineering

240

112

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The last decade has witnessed extensive research in the field of healthcare services and their technological upgradation. To be more specific, the Internet of Things (IoT) has shown potential application in connecting various medical devices, sensors, and healthcare professionals to provide quality medical services in a remote location. This has improved patient safety, reduced healthcare costs, enhanced the accessibility of healthcare services, and increased operational efficiency in the healthcare industry. The current study gives an up-to-date summary of the potential healthcare applications of IoT- (HIoT-) based technologies. Herein, the advancement of the application of the HIoT has been reported from the perspective of enabling technologies, healthcare services, and applications in solving various healthcare issues. Moreover, potential challenges and issues in the HIoT system are also discussed. In sum, the current study provides a comprehensive source of information regarding the different fields of application of HIoT intending to help future researchers, who have the interest to work and make advancements in the field to gain insight into the topic.

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“…Despite such a limit, we argue the importance of identifying the practical characteristics of edge-cloud data transmission at runtime, because the data transmission characteristics can vary considerably over both different workload regimes and different environmental conditions. For example, by intuitively assuming the network congestion due to big data volumes, some researchers propose to prioritize the health data traffic, which however would incur unnecessary processing delay and result in unwanted unfairness among patients in the context of our project [4,21,28].…”

Section: ) Application-specific Workload Regimementioning

confidence: 99%

Take one for the team: On the time efficiency of application-level buffer-aided data relaying in edge cloud communication

Millar-Bilbao

Rojas

et al. 2021

Preprint

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Background: Adding buffers to networks is part of the fundamental advance in data communication. Since edge cloud computing is based on the heterogeneous collaboration network model in a federated environment, it is natural to consider buffer-aided data communication for edge cloud applications. However, the existing studies generally pursue the beneficial features of buffering at a cost of time, not to mention that many investigations are focused on lower-layer data packets rather than application-level communication transactions.Aims: Driven by a counterintuitive argument against the claim that buffers "can introduce additional delay to the communication between the source and destination", this research aims to investigate whether or not (and if yes, to what extent) the application-level buffering mechanism can improve the time efficiency in edge-cloud data transmissions.Method: To collect empirical evidence for the theoretical discussion, we built up a testbed to simulate a remote health monitoring system, and conducted both experimental and modeling investigations into the first-in-first-served (FIFS) and buffer-aided data transmissions at a relay node in the system.Results: An empirical inequality system is established for revealing the time efficiency of buffer-aided edge cloud communication. For example, given the reference of transmitting the 11th data entity in the FIFS manner, the inequality system suggests buffering up to 50 data entities into one transmission transaction on our testbed.Conclusions: Despite the trade-off benefits (e.g., energy efficiency and fault tolerance) of buffering data, our investigation argues that the buffering mechanism can also speed up data transmission under certain circumstances, and thus it would be worth taking data buffering into account when designing and developing edge cloud applications even in the time-critical context.

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A cognitive technology based healthcare monitoring system and medical data transmission

Cited by 29 publications

References 25 publications

Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey

Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey

IoT-Based Applications in Healthcare Devices

Take one for the team: On the time efficiency of application-level buffer-aided data relaying in edge cloud communication

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