Decision Trees: An Overview and Their Use in Medicine

By Vili Podgorelec, Peter Kokol, Bruno Stiglic, Ivan Rozman. Journal of medical systems, 2002

713 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Information Management, Information Systems

View Paper: https://www.semanticscholar.org/paper/fb9f4005f6c3c7caa9512b4c7d9288ea05a727b2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Stefan Szeider
2. Ognjen Rudovic
3. Björn Schuller
4. H. Asadi
5. André Schidler
6. M. Lee
7. Rosalind W. Picard
8. Hadi Banaee

In Health Information Management

1. Hadi Banaee
2. Mobyen Uddin Ahmed
3. Amy Loutfi

In Information Systems

1. Björn Schuller
2. André Schidler
3. Stefan Szeider
4. Rosalind W. Picard
5. Hadi Banaee
6. Rohitash Chandra
7. Azal Ahmad Khan
8. Omkar Chaudhari

Citations in the corpus

• Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013, methodology): “Decision tree is a reliable technique to use in different areas of medical domain in order to make a right decision [76,77].”
• SAT-based Decision Tree Learning for Large Data Sets (2021, background): “Since low depth decision trees require fewer tests per sample, they are strongly preferred in applications like in medical diagnosis where tests might be costly, risky, or intrusive (Podgorelec et al. 2002).”
• Personalized machine learning for robot perception of affect and engagement in autism therapy (2018, methodology): “humanrobot interaction (e.g., (11, 40)), for estimation of continuous outputs. On the other hand, GBRTs are commonly used in clinical decision tasks due to their easy interpretation of input features (58). For more details about training and evaluation of the models, see Appendix/A. 1The network lay”
• eDoctor: machine learning and the future of medicine (2018, background): “…of analysis, machine-assisted decision trees can be constructed by training the algorithm on large databases of patient cases where the outcome is known (supervised) to construct a decision tree based on which variables it can determine the highest separability on the desired categories [75].”
• A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, background): “Hence, decision trees have made a huge impact in the area of medicine, and biotechnology [63, 64] since transparency of the decision-making process is important.”