Reprint
Feature Papers in Medical Statistics and Data Science Section
Edited by
May 2024
296 pages
- ISBN978-3-7258-1043-7 (Hardback)
- ISBN978-3-7258-1044-4 (PDF)
https://doi.org/10.3390/books978-3-7258-1044-4 (registering)
This book is a reprint of the Special Issue Feature Papers in Medical Statistics and Data Science Section that was published in
Biology & Life Sciences
Computer Science & Mathematics
Medicine & Pharmacology
Public Health & Healthcare
Summary
Recent years have witnessed an increase in the use of advanced data analysis methods in biomedical, clinical, and epidemiological research. The aim of this reprint is to highlight the practical aspects of new data analysis methods and to provide insights into the challenges in biostatistics and data science. By considering recent developments in medical technology, the reprint includes novel approaches in the field of medical statistics and data science, covering meta-analysis, the assessment of clinical outcomes, machine learning, medical diagnostics, and genomic factors.
Format
- Hardback
License
© 2024 by the authors; CC BY-NC-ND license
Keywords
recurrent neural networks; event detection; data preprocessing; outlier removal; type 1 diabetes; automated detection; craniovertebral angle; neuronal network; Openpose; RGB; C7; tragus; laboratory diagnostics; requirements engineering; genomics; gene expression; metagenomics; medical diagnostics; standardized regression coefficient; statistics; meta-analysis; research synthesis; data presentation; carotid intima-media thickness; overweight; childhood; colon adenocarcinoma; bioinformatic analysis; MMP3 and TESC; data science; machine-learning; digital medicine; artificial intelligence; SARS-CoV-2 vaccines; deep learning; spike protein; ACE2; CpG DNA; machine learning; clinical trials; RCT; automated meta-analysis; deep learning; automation; big data; machine learning; nephrology; chronic kidney disease; prediction models; outcomes; personalized medicine; primary prevention; treatment; structural models; protein structures; membrane bilayer; DOPE; Modeller; AlphaFold2; weighted trajectory analysis; Kaplan–Meier estimator; clinical outcome assessment; logrank test; ordinal variables; Parkinson’s disease; hyperbaric oxygen therapy; machine learning; genomic factors; single-cell RNA-seq; WSI; patches; tumour; cancer; deep learning; Camelyon17; algorithmic fairness; electronic health records; data missingness; data imputation; diabetes; model explainability; mental workload; statistical feature selection; Shapley-based feature selection; alpha and theta EEG band ratios; machine learning