Guest Editors
Ziheng Wang
University of Macau
My research mainly focuses on bioinformatics analysis and the use of machine learning methods to solve clinical problems. At the same time, I interested in developing more sophisticated protocols of single-cell multiple-omics, and studying major diseases, especially oncology using single-cell omics strategies. At the same time, I interested in basic and clinical research studies profiling the molecular pathology of diseases, investigating etiology and pathogenesis, and identifying humoral, genetic, and imaging biomarkers for clinical settings for diseases detection, diagnosis, prognosis, and prediction. I also used computational methods investigating diseases using high-throughput approaches, such as next-generation sequencing, single-cell omics, digital pathology, functional imaging, and others. Multiomics analysis includes multiple dimensions at the same time, such as genomics, epigenomics, transcriptome, proteomics and metabonomics. New biomarkers from multiple omics studies have been validated on more dimensions, with more precise mechanisms, diagnosis, and prognosis detection. Finally, I am very concerned about the high-quality care of patients and hope that various means can be used to improve their prognosis
Guangsheng Pei
UT MD Anderson Cancer Center
Research Assistant Professor School of Biomedical Informatics in the University of Texas Health Science Center at Houston. My research mainly focuses on High-Dimensional Data Analysis, Deep learning, Machine learning, Multivariate statistics, GWAS, Complex traits and diseases genomics, WGS, Gene Expression, Tissue specificity, Cell type specificity, Single cell and Single nuclei RNA-seq, Cell type deconvolution, Cancer mutational signature, Somatic mutation and Metagenomics.
Collection Overview
Machine Learning in medicine, nursing, and healthcare has garnered significant attention due to its potential to revolutionize these fields. This Methods Collection aims to explore the advancements and challenges associated with applying machine learning in healthcare, with a focus on its impact on diagnosis, treatment, patient outcomes, and healthcare management.
Topics of interest include:
1. Machine Learning Algorithms for Disease Diagnosis:
• Automated disease identification and classification using machine learning techniques.
• Integration of patient data, such as medical records, imaging, and genetics, for precise and personalized diagnoses.
2. Predictive Analytics and Risk Stratification:
• Machine learning approaches for predicting patient outcomes and identifying high-risk individuals.
• Risk stratification models for targeted interventions and improved patient management.
3. Personalized Treatment Approaches:
• Machine learning-based systems for devising personalized treatment plans.
• Utilization of patient data to optimize treatment selection and adjust dosages accurately.
4. Healthcare Management and Resource Allocation:
• Machine learning algorithms for optimizing hospital resource allocation and enhancing operational efficiency.
• Predictive analytics for identifying patients at a high risk of readmission and implementing preventive measures.
5. Ethical and Legal Considerations:
• Addressing biases, fairness, and transparency in machine learning algorithms used in healthcare.
• Ensuring privacy, security, and obtaining informed consent for collecting and utilizing patient data.
6. Clinical Workflow Integration and Adoption:
• Challenges and strategies for integrating machine learning tools into existing clinical workflows.
• Training and upskilling healthcare professionals to proficiently use machine learning technologies.
7. Interpretable and Explainable Models:
• Development of interpretable machine learning models in healthcare to enhance trust and regulatory compliance.
Articles
Abstracts
Development and Evaluation of a Retrieval-Augmented Generation (RAG) Chatbot for Operating Room Nursing Support
DanDan Shan*1
1Department of Operating Room, Henan Provincial Key Medicine Laboratory of Nursing，Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Henan University People’s Hospital, Zhengzhou, Henan, 450003, China