Special Issue on Novel Applications of Artificial Intelligence in Medicine and Health

Article Chemistry, Multidisciplinary

Rough Set Based Classification and Feature Selection Using Improved Harmony Search for Peptide Analysis and Prediction of Anti-HIV-1 Activities

Bagyamathi Mathiyazhagan et al.

Summary: In this study, a rough set-based framework with sequence-based numeric features is used to classify anti-HIV-1 peptides. The results show that the proposed framework achieves high predictive accuracy and improves the classification precision of the peptides.

APPLIED SCIENCES-BASEL (2022)

添加到收藏夹

Article Chemistry, Multidisciplinary

An Efficient Greedy Randomized Heuristic for the Maximum Coverage Facility Location Problem with Drones in Healthcare

Sumayah Al-Rabiaah et al.

Summary: This study focuses on the application of drones in healthcare services, proposing a maximum coverage greedy randomized heuristic algorithm. By randomly selecting some facilities to open and assigning patients to the closest facility, it efficiently covers over 80% of patients in a short amount of time. Extensive testing showed that the algorithm performs comparably to other methods in terms of efficiency and patient coverage.

APPLIED SCIENCES-BASEL (2022)

添加到收藏夹

Article Chemistry, Multidisciplinary

Lung Segmentation in CT Images: A Residual U-Net Approach on a Cross-Cohort Dataset

Joana Sousa et al.

Summary: Lung cancer is a leading cause of cancer-related mortality, but early detection through CT screening can improve survival rates. This study developed a deep learning model for lung segmentation, which achieved high accuracy across different pathological cases. The model can be a valuable tool for clinical evaluation and analysis of lung CT images.

APPLIED SCIENCES-BASEL (2022)

添加到收藏夹

Article Chemistry, Multidisciplinary

Quantification of Coronary Artery Atherosclerotic Burden and Muscle Mass: Exploratory Comparison of Two Freely Available Software Programs

Carmela Nappi et al.

Summary: Coronary artery calcification and sarcopenia have prognostic impact in patients with cancer and non-cancer conditions. The use of freeware software for quantitative evaluation of these parameters after whole-body PET/CT is promising for risk stratification without additional radiation or cost. The study compared two software tools and found good agreement in assessing muscle mass and coronary artery calcium score. The results suggest that freeware software can provide comprehensive evaluation of oncological patients.

APPLIED SCIENCES-BASEL (2022)

添加到收藏夹

Article Chemistry, Multidisciplinary

Machine Learning and Feature Selection Methods for EGFR Mutation Status Prediction in Lung Cancer

Joana Morgado et al.

Summary: The evolution of personalized medicine has shifted therapeutic strategies to genetic modification targeted therapy from classical chemotherapy and radiotherapy. Utilizing nodule image features extracted from CT scans can predict gene mutation status in a noninvasive, fast, and easy-to-use manner. Recent studies suggest that radiomic features extracted from extended regions beyond the tumor are more relevant for predicting mutation status in lung cancer, potentially reducing mortality rates.

APPLIED SCIENCES-BASEL (2021)

添加到收藏夹

Special Issue on Novel Applications of Artificial Intelligence in Medicine and Health

相关参考文献

Rough Set Based Classification and Feature Selection Using Improved Harmony Search for Peptide Analysis and Prediction of Anti-HIV-1 Activities

An Efficient Greedy Randomized Heuristic for the Maximum Coverage Facility Location Problem with Drones in Healthcare

Lung Segmentation in CT Images: A Residual U-Net Approach on a Cross-Cohort Dataset

Quantification of Coronary Artery Atherosclerotic Burden and Muscle Mass: Exploratory Comparison of Two Freely Available Software Programs

Machine Learning and Feature Selection Methods for EGFR Mutation Status Prediction in Lung Cancer

导出引文

分享论文