Decision-support-Systeme, Assistenzsysteme und Telemedizin in der Epileptologie

Zöllner, Johann Philipp; Wolking, Stefan; Weber, Yvonne G.; Rosenow, Felix

doi:10.1007/s00115-020-01031-7

Cited by 6 publications

(4 citation statements)

References 33 publications

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“…First, the development knowledge base of the CDSS mainly comes from the big data of clinical guidelines or case composition included in high-level clinical research [27,28]. Second, for the CDSS developed based on a guideline, convenience is improved at the cost of reducing its reliability [7,[29][30][31]. Third, the updating speed and the abundance of information sources of the knowledge base can affect the heterogeneity of conclusions between the CDSS and multi-disciplinary team (MDT) [32].…”

Section: Application Characteristics Of the Cdssmentioning

confidence: 99%

Artificial intelligence in clinical decision support systems for oncology

Wang¹,

Chen²,

Zhang³

et al. 2023

Int. J. Med. Sci.

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Artificial intelligence (AI) has been widely used in various medical fields, such as image diagnosis, pathological classification, selection of treatment schemes, and prognosis analysis. Especially in the image-aided diagnosis of tumors, the cooperation of human-computer interactions has become mature. However, the ethics of the application of AI as an emerging technology in clinical decision-making have not been fully supported, so the clinical decision support system (CDSS) based on AI technology has not fully realized human-computer interactions in clinical practice as the image-aided diagnosis system. The CDSS was currently used and promoted worldwide including Watson for Oncology, Chinese society of clinical oncology-artificial intelligence (CSCO AI) and so on. This paper summarized the applications and clarified the principle of AI in CDSS, analyzed the difficulties of AI in oncology decisions, and provided a reference scheme for the application of AI in oncology decisions in the future.

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Section: Application Characteristics Of the Cdssmentioning

confidence: 99%

Artificial intelligence in clinical decision support systems for oncology

Wang¹,

Chen²,

Zhang³

et al. 2023

Int. J. Med. Sci.

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“…This study, published in 2018, was conducted in 2016. In 2020, Zollner and coworkers [16] published a review on clinical decision support systems for epileptology, in which more apps and other solutions are described, but without the MARS evaluation. More recently, Mohammadzadeh and colleagues [17] performed a systematic review of M-health applications used in epilepsy.…”

Section: Mobile Health In Epilepsymentioning

confidence: 99%

Mobile Applications for Epilepsy: Where Are We? Where Should We Go? A Systematic Review

Abreu

Carmo

Franco

et al. 2022

Signals

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The development of mobile health for epilepsy has grown in the last years, bringing new applications (apps) to the market and improving already existing ones. In this systematic review, we analyse the scope of mobile apps for seizure detection and epilepsy self-management, with two research questions in mind: what are the characteristics of current solutions and do they meet users’ requirements? What should be considered when designing mobile health for epilepsy? We used PRISMA methodology to search within App Store and Google Play Store from February to April of 2021, reaching 55 potential apps. A more thorough analysis regarding particular features was performed on 26 of those apps. The content of these apps was evaluated in five categories, regarding if there was personalisable content; features related to medication management; what aspects of seizure log were present; what type of communication prevailed; and if there was any content related to seizure alarm or seizure action plans. Moreover, the 26 apps were evaluated through using MARS by six raters, including two neurologists. The analysis of MARS categories was performed for the top and bottom apps, to understand the core differences. Overall, the lowest MARS scores were related to engagement and information, which play a big part in long-term use, and previous studies raised the concern of assuring continuous use, especially in younger audiences. With that in mind, we identified conceptual improvement points, which were divided in three main topics: customisation, simplicity and healthcare connection. Moreover, we summarised some ideas to improve m-health apps catered around long-term adherence. We hope this work contributes to a better understanding of the current scope in mobile epilepsy management, endorsing healthcare professionals and developers to provide off-the-shelf solutions that engage patients and allows them to better manage their condition.

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“…Telemedicine • Wearable • Automated seizure detection • App • Digitalization sichtsarbeiten [13,15] dargestellt, liegen die Schwerpunkte mobiler Gesundheitstechnologien in der Epilepsieversorgung bei digitalen Anfallskalendern und automatisierter Anfallserkennung, dies zeigen auch die Ergebnisse unserer Umfrage. Für die Nutzung klinisch geprüfter Wearables zur automatisierten Detektion tonisch-klonischer Anfälle liegen mittleweile auch Stellungnahmen internationaler Fachgesellschaften vor [3].…”

Section: Introductionunclassified

Forschung und Entwicklung telemedizinischer Anwendungen und mobiler Gesundheitstechnologien bei Epilepsie

Mues

Surges

2021

Z. Epileptol.

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Decision-support-Systeme, Assistenzsysteme und Telemedizin in der Epileptologie

Cited by 6 publications

References 33 publications

Artificial intelligence in clinical decision support systems for oncology

Artificial intelligence in clinical decision support systems for oncology

Mobile Applications for Epilepsy: Where Are We? Where Should We Go? A Systematic Review

Forschung und Entwicklung telemedizinischer Anwendungen und mobiler Gesundheitstechnologien bei Epilepsie

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