Improving disease prediction using ICD-9 ontological features
By Mihail Popescu, Mohammad Khalilia. 2011
21 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Health Information Management, Molecular Biology, Knowledge Graphs & Representation, Medical & Health AI
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
- Neil Houlsby
- Xiaohua Zhai
- Mario Lucic
- Quoc V. Le
- Katherine A. Heller
- Ghassen Jerfel
- Xuezhi Wang
- Percy Liang
In Health Information Management
In Molecular Biology
- Jacob Eisenstein
- Andrea Montanari
- Katherine A. Heller
- Dino Pedreschi
- David Sculley
- Kellie Webster
- Xuezhi Wang
- Neil Houlsby
In Medical & Health AI
- Katherine A. Heller
- Dino Pedreschi
- Xiaohua Zhai
- Neil Houlsby
- Harini Suresh
- Surabhi Verma
- Christian Walter Peter Omlin
- Waddah Saeed
Citations in the corpus
- Underspecification Presents Challenges for Credibility in Modern Machine Learning (2020, positive): “This is compatible with previous findings that inputting medical/domain relational knowledge has led to better out of domain behavior (Nestor et al., 2019), performance (Popescu and Khalilia, 2011; Choi et al., 2017; Tomašev et al., 2019a,b) and interpretability (Panigutti et al., 2020).”
- Doctor XAI: an ontology-based approach to black-box sequential data classification explanations (2020, background): “Moreover, the authors of [37] show how disease classification performance can improve using features based on the ICD-9 codes semantic similarity.”