ArticleProceedings of machine learning research2022
Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHR.
Article in Proceedings of machine learning research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Automated biomedical hypothesis generation with time-aware hypergraph contrastive learning.Knowledge and information systems · 2026Article
- A Narrative Review of Multimodal Data Fusion Strategies for Precision Risk Prediction in Coronary Artery Disease: Advances, Challenges, and Future Informatics Directions.Rambam Maimonides medical journal · 2025Review
- Article
- TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2024Article
- PromptLink: Leveraging Large Language Models for Cross-Source Biomedical Concept Linking.International ACM SIGIR Conference on Research and Development in Information Retrieval. Annual International ACMSIGIR Conference on Research & Development in Information Retrieval · 2024Article
- From Basic to Extra Features: Hypergraph Transformer Pretrain-then-Finetuning for Balanced Clinical Predictions on EHR.Proceedings of machine learning research · 2024Article
- Developing a novel causal inference algorithm for personalized biomedical causal graph learning using meta machine learning.BMC medical informatics and decision making · 2024Article
- Fusion Modeling: Combining Clinical and Imaging Data to Advance Cardiac Care.Circulation. Cardiovascular imaging · 2023Review
- Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting.Advances in neural information processing systems · 2023Article
- Enhancing Personalized Healthcare via Capturing Disease Severity, Interaction, and Progression.Proceedings. IEEE International Conference on Data Mining · 2023Article
- R-Mixup: Riemannian Mixup for Biological Networks.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2023Article
- Weakly-Supervised Scientific Document Classification via Retrieval-Augmented Multi-Stage Training.International ACM SIGIR Conference on Research and Development in Information Retrieval. Annual International ACMSIGIR Conference on Research & Development in Information Retrieval · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Electronic Health Record modeling is crucial for digital medicine. However, existing models ignore higher-order interactions among medical codes and their causal relations towards downstream clinical predictions. To address such limitations, we propose a novel framework CACHE, to provide
Indexed as
Identifiers
37255863PMC10227831What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.