Medical Data Mining: Latest Advances and Prospects
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".
Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 517
Special Issue Editors
Interests: predictive modeling; explainable AI; data visualization; temporal data representations; patient phenotyping; intersection of machine learning and causal inference
Interests: model development & validation; data visualization; decision support systems; personalized medicine; interpretable clinical models
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Special Issue Information
Dear Colleagues,
The digitization of healthcare has accelerated the recording and collection of medical data, including patient data, clinical reports, diagnoses and therapy recommendations. As volumes of these data continue to grow, there is the need to generate new and potentially useful information in an automated, hypothesis-free, and time-efficient manner. Data mining and machine learning methods can provide empirical support for medical decision-making and ultimately have the potential to promote the discovery of new phenomena and the creation of medical knowledge that improves patient health and life expectancy. This Special Issue is devoted to new methods, workflows, architectures, and systems for knowledge discovery in medical applications. We solicit innovative works on predictive modeling, classification, clustering, association rule learning, anomaly detection, stream mining, text mining and other related data mining techniques and their applications for medicine, including clinical decision support, therapy planning, treatment outcome prediction, patient health monitoring and alerting.
Topics of interest include (but are not limited to):
- Data mining and machine learning methods for big clinical data;
- Data analysis and visualization in medicine;
- AI-based decision support for clinical diagnosis, therapy planning, and treatment outcome prediction;
- Intelligent treatment recommendation systems;
- Patient phenotyping for personalized medicine;
- Temporal analysis of electronic health records, e.g., stream mining;
- Explainable AI for clinical decision support;
- Detection of adverse events and alerting;
- Outlier analysis for medical error detection;
- Graphical models for clinical data;
- Clinical text mining and natural language processing;
- Medical knowledge engineering;
- Data-driven causal inference in medicine;
- Patient subgrouping via clustering and community detection;
- Knowledge discovery from data collected by smart / sensor-based medical devices;
- Exploratory data analysis for digital interventions via mobile devices;
- Development and validation of clinical models;
- Clinical applications of deep learning.
Dr. Uli Niemann
Dr. Jorge Simões
Guest Editors
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