A framework for data-driven adaptive GUI generation based on DICOM

Gambino, Orazio; Rundo, Leonardo; Cannella, Vincenzo; Vitabile, Salvatore; Pirrone, Roberto

doi:10.1016/j.jbi.2018.10.009

Cited by 24 publications

(14 citation statements)

References 41 publications

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“…It is for this reason that the combination of architecture plus agent system can be adapted and extended to other and more complicated clinical scenarios. For instance, specialized agents could support specialized physicians through intelligent software that uses a data-driven approach for building interfaces as in [55] or including techniques for data manipulation as in [56,57]. Moreover, everything related to monitoring can be deployed in various sensors to adopt IoT techniques for patient monitoring.…”

Section: Discussionmentioning

confidence: 99%

Agents and robots for collaborating and supporting physicians in healthcare scenarios

Lanza

Seidita

Chella

2020

Journal of Biomedical Informatics

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Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre-including this research content-immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

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Section: Discussionmentioning

confidence: 99%

Agents and robots for collaborating and supporting physicians in healthcare scenarios

Lanza

Seidita

Chella

2020

Journal of Biomedical Informatics

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“…In the future work, it will be an interesting direction to integrate multiple context information based on deep learning framework. It is also a should be adaptive on the basis of a results-driven approach in the interface [ 47 ].…”

Section: Discussionmentioning

confidence: 99%

Online Doctor Recommendation with Convolutional Neural Network and Sparse Inputs

Yan

2020

Computational Intelligence and Neuroscience

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The recommendation system in the online medical consultation website is a system to assist patients to find appropriate doctors. Based on the analysis of the current situation of the development of an online medical community (Haodf.com) in China, this paper puts forward recommendation suggestions of finding the right hospital and doctor to promote the rapid integration of Internet technology and traditional medical services. A new recommendation model called Probabilistic Matrix Factorization integrated with Convolutional Neural Network (PMF-CNN) is proposed in the paper. Doctors’ data in Haodf.com were used to evaluate the performance of our system. The model improves the performance of medical consultation recommendations by fusing review text and doctor information based on CNN (Convolutional Neural Network). Specifically, CNN is used to learn the feature representation of the review text and the doctors’ information. Furthermore, the extended matrix factorization model is exploited to fuse the review information feature and the initial value of the doctors’ information for recommendation. As is shown in the experimental results on Haodf.com datasets, the proposed PMF-CNN achieves better recommendation performances than the other state-of-the-art recommendation algorithms. And the recommendation system in an online medical website improves the utilization efficiency of doctors and the balance of public health resources allocation.

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“…Indeed, computer applications for diagnostic medical imaging generally provide a wide range of tools to support physicians in their daily diagnosis activities. Unfortunately, some functionalities are specialised for specific diseases or imaging modalities, while others are useless for the images under investigation [134]. In clinical environments, a Graphical User Interface (GUI) must represent a sequence of steps for image investigation following a well-defined workflow.…”

Section: Discussionmentioning

confidence: 99%

A Survey on Nature-Inspired Medical Image Analysis: A Step Further in Biomedical Data Integration

Rundo

Militello

Vitabile

et al. 2019

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Natural phenomena and mechanisms have always intrigued humans, inspiring the design of effective solutions for real-world problems. Indeed, fascinating processes occur in nature, giving rise to an ever-increasing scientific interest. In everyday life, the amount of heterogeneous biomedical data is increasing more and more thanks to the advances in image acquisition modalities and high-throughput technologies. The automated analysis of these large-scale datasets creates new compelling challenges for data-driven and model-based computational methods. The application of intelligent algorithms, which mimic natural phenomena, is emerging as an effective paradigm for tackling complex problems, by considering the unique challenges and opportunities pertaining to biomedical images. Therefore, the principal contribution of computer science research in life sciences concerns the proper combination of diverse and heterogeneous datasets-i.e., medical imaging modalities (considering also radiomics approaches), Electronic Health Record engines, multi-omics studies, and real-time monitoring-to provide a comprehensive clinical knowledge. In this paper, the state-of-the-art of nature-inspired medical image

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A framework for data-driven adaptive GUI generation based on DICOM

Cited by 24 publications

References 41 publications

Agents and robots for collaborating and supporting physicians in healthcare scenarios

Agents and robots for collaborating and supporting physicians in healthcare scenarios

Online Doctor Recommendation with Convolutional Neural Network and Sparse Inputs

A Survey on Nature-Inspired Medical Image Analysis: A Step Further in Biomedical Data Integration

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