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

FeatEHR-LLM: Leveraging Large Language Models for Feature Engineering in Electronic Health Records | Signal Canvas | ScienceToStartup

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/featehr-llm-leveraging-large-language-models-for-feature-engineering-in-electronic-health-records

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-27
Score updated: 2026-04-27
Score fresh until: 2026-05-27
References: 0
Source count: 3
Coverage: 50%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

Canonical ID featehr-llm-leveraging-large-language-models-for-feature-engineering-in-electronic-health-records | Route /signal-canvas/featehr-llm-leveraging-large-language-models-for-feature-engineering-in-electronic-health-records

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/featehr-llm-leveraging-large-language-models-for-feature-engineering-in-electronic-health-records

MCP example

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  }
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source_context

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Evidence Receipt

Route status: building

Claims: 0

References: Pending verification

Proof: Verification pending

Freshness state: computing

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

PDF: https://arxiv.org/pdf/2604.22534v1

Source count: 3

Coverage: 50%

Last proof check: 2026-04-27T20:14:10.432Z

Signal Canvas receipt window

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

/buildability/featehr-llm-leveraging-large-language-models-for-feature-engineering-in-electronic-health-records

Watchwatch

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

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

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

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