Special Issue "Bioengineering—Selected Papers from ICBET 2020 (2020 10th International Conference on Biomedical Engineering and Technology)"

A special issue of Bioengineering (ISSN 2306-5354).

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 3485

Special Issue Editors

Special Issue Information

Dear Colleagues,

This Special Issue is cooperating with the ICBET 2020 conference (www.icbet.org). All speakers and registered participants at this conference are invited to submit a manuscript for publication.

The 2020 10th International Conference on Biomedical Engineering and Technology (ICBET 2020) will be held in Meiji University, Tokyo, Japan on 15-18 September 2020. The objective of the ICBET 2020 is to provide a platform for researchers, engineers, academicians, as well as industrial professionals from all over the world to present their research results and development activities in Biomedical Engineering and Technology. Topics of interest for submission include but are not limited to:

1. Bioinformatics and Computational Biology

Protein structure, function, and sequence analysis;
Protein interactions, docking, and function;
Computational proteomics;
DNA and RNA structure, function, and sequence analysis;
Gene regulation, expression, identification, and network;
Structural, functional, and comparative genomics;
Gene engineering and protein engineering;
Computational evolutionary biology;
Drug design and computer-aided diagnosis;
Data acquisition, normalization, analysis, and visualization;
Algorithms, models, software, and tools in bioinformatics;
Any novel approaches to bioinformatics problems.

2. Biomedical Engineering

Biomedical imaging, image processing, and visualization;
Bioelectrical and neural engineering;
Biomechanics and biotransport;
Methods and biology effects of NMR/CT/ECG technology;
Biomedical devices, sensors, and artificial organs;
Biochemical, cellular, molecular, and tissue engineering;
Biomedical robotics and mechanics;
Rehabilitation engineering and clinical engineering;
Health monitoring systems and wearable system;
Biosignal processing and analysis;
Biometric and biomeasurement;
Drug delivery;
Tissue engineering;
Other topics related to biomedical engineering.

Prof. Dr. Jyh-Ping Chen

Dr. Larbi Boubchir
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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17 pages, 3751 KiB  
Decision Trees for Predicting Mortality in Transcatheter Aortic Valve Implantation
Bioengineering 2021, 8(2), 22; https://doi.org/10.3390/bioengineering8020022 - 09 Feb 2021
Cited by 6 | Viewed by 2559
Current prognostic risk scores in cardiac surgery do not benefit yet from machine learning (ML). This research aims to create a machine learning model to predict one-year mortality of a patient after transcatheter aortic valve implantation (TAVI). We adopt a modern gradient boosting [...] Read more.
Current prognostic risk scores in cardiac surgery do not benefit yet from machine learning (ML). This research aims to create a machine learning model to predict one-year mortality of a patient after transcatheter aortic valve implantation (TAVI). We adopt a modern gradient boosting on decision trees classifier (GBDTs), specifically designed for categorical features. In combination with a recent technique for model interpretations, we developed a feature analysis and selection stage, enabling the identification of the most important features for the prediction. We base our prediction model on the most relevant features, after interpreting and discussing the feature analysis results with clinical experts. We validated our model on 270 consecutive TAVI cases, reaching a C-statistic of 0.83 with CI [0.82, 0.84]. The model has achieved a positive predictive value ranging from 57% to 64%, suggesting that the patient selection made by the heart team of professionals can be further improved by taking into consideration the clinical data we identified as important and by exploiting ML approaches in the development of clinical risk scores. Our approach has shown promising predictive potential also with respect to widespread prognostic risk scores, such as logistic European system for cardiac operative risk evaluation (EuroSCORE II) and the society of thoracic surgeons (STS) risk score, which are broadly adopted by cardiologists worldwide. Full article
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