·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Contact
Latest
Canva apologizes after its AI tool replaces ‘Palestine’ in designs33m◆China blocks Meta’s $2B Manus deal after months-long probe1h◆OpenAI could be making a phone with AI agents replacing apps1h◆Rebuilding the data stack for AI2h◆Join the new AI Agents Vibe Coding Course from Google and Kaggle2h◆The AI-designed car is taking shape4h◆Meta inks deal for solar power at night, beamed from space5h◆The next phase of the Microsoft OpenAI partnership9h◆From Local to Cluster: A Unified Framework for Causal Discovery with Latent Variables11h◆Consequentialist Objectives and Catastrophe11h◆EgoMAGIC- An Egocentric Video Field Medicine Dataset for Training Perception Algorithms11h◆ReCast: Recasting Learning Signals for Reinforcement Learning in Generative Recommendation11h◆A Probabilistic Framework for Hierarchical Goal Recognition11h◆CNSL-bench: Benchmarking the Sign Language Understanding Capabilities of MLLMs on Chinese National Sign Language11h◆The Shape of Adversarial Influence: Characterizing LLM Latent Spaces with Persistent Homology11h◆Toward Principled LLM Safety Testing: Solving the Jailbreak Oracle Problem11h◆Learning from Natural Language Feedback for Personalized Question Answering11h◆TS-Arena -- A Live Forecast Pre-Registration Platform11h◆Report for NSF Workshop on AI for Electronic Design Automation11h◆Initial results of the Digital Consciousness Model11h◆Canva apologizes after its AI tool replaces ‘Palestine’ in designs33m◆China blocks Meta’s $2B Manus deal after months-long probe1h◆OpenAI could be making a phone with AI agents replacing apps1h◆Rebuilding the data stack for AI2h◆Join the new AI Agents Vibe Coding Course from Google and Kaggle2h◆The AI-designed car is taking shape4h◆Meta inks deal for solar power at night, beamed from space5h◆The next phase of the Microsoft OpenAI partnership9h◆From Local to Cluster: A Unified Framework for Causal Discovery with Latent Variables11h◆Consequentialist Objectives and Catastrophe11h◆EgoMAGIC- An Egocentric Video Field Medicine Dataset for Training Perception Algorithms11h◆ReCast: Recasting Learning Signals for Reinforcement Learning in Generative Recommendation11h◆A Probabilistic Framework for Hierarchical Goal Recognition11h◆CNSL-bench: Benchmarking the Sign Language Understanding Capabilities of MLLMs on Chinese National Sign Language11h◆The Shape of Adversarial Influence: Characterizing LLM Latent Spaces with Persistent Homology11h◆Toward Principled LLM Safety Testing: Solving the Jailbreak Oracle Problem11h◆Learning from Natural Language Feedback for Personalized Question Answering11h◆TS-Arena -- A Live Forecast Pre-Registration Platform11h◆Report for NSF Workshop on AI for Electronic Design Automation11h◆Initial results of the Digital Consciousness Model11h◆
News/FeatEHR-LLM: Leveraging Large Language Models for Feature Engineering in Electronic Health Records
arxiv
PublishedApril 27, 2026 at 4:00 AM

FeatEHR-LLM: Leveraging Large Language Models for Feature Engineering in Electronic Health Records

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2604.22534v1 Announce Type: cross Abstract: Feature engineering for Electronic Health Records (EHR) is complicated by irregular observation intervals, variable measurement frequencies, and structural sparsity inherent to clinical time series. Existing automated methods either lack clinical dom

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivFrom Local to Cluster: A Unified Framework for Causal Discovery with Latent Variables11harxivConsequentialist Objectives and Catastrophe11harxivEgoMAGIC- An Egocentric Video Field Medicine Dataset for Training Perception Algorithms11harxivReCast: Recasting Learning Signals for Reinforcement Learning in Generative Recommendation11h
Originally published on arxiv ↗
HomeModelsNews