·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Cursor makes its biggest India push yet ahead of SpaceX acquisition with localized pricing4h◆Photonic reservoir computing with complex networks5h◆XS-VLA: Coupling Coarse-grained Spatial Distillation with Latent Flow Matching for Lightweight Robotic Control5h◆Agentic Permissions Policy Algebra for Taint Confinement in LLM Agents5h◆Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks5h◆The One-Word Census: Answer-Choice Conformity Across 44 Language Models5h◆Creative Integration: A Decidable Criterion of Creativity5h◆BERT-based Models vs. Large Language Models for Low-Resource Named Entity Recognition: A Comparative Study on Marathi5h◆Joint Optimization for Greedy Longest-match Tokenization5h◆Kimi K3: Open Frontier Intelligence5h◆The Few-shot Dilemma: Over-prompting Large Language Models5h◆Speculative Pipeline Decoding: Higher-Accuracy Drafting with Hidden Latency via Pipeline Parallelism5h◆Bayesian Complete-Pooling in Cross-Subject Classification for Motor Imagery Electroencephalogram5h◆StageGuard: Physiologically Constrained Sleep Staging5h◆Soft-Constrained Optimization of Latent Space in Variational Autoencoders5h◆Beyond Error-vs-Discard Characteristic: Toward Stable and Reliable Evaluation for Face Image Quality Assessment5h◆Analyzing the Importance of Blank for CTC-Based Knowledge Distillation5h◆Predicting Channel Closures in the Lightning Network with Machine Learning5h◆Evaluation of Blood Vessel Segmentation Methods on Hard-to-Detect Vascular Structures5h◆MOCA: A Transformer-based Modular Causal Inference Framework with One-way Cross-attention and Cutting Feedback5h◆Cursor makes its biggest India push yet ahead of SpaceX acquisition with localized pricing4h◆Photonic reservoir computing with complex networks5h◆XS-VLA: Coupling Coarse-grained Spatial Distillation with Latent Flow Matching for Lightweight Robotic Control5h◆Agentic Permissions Policy Algebra for Taint Confinement in LLM Agents5h◆Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks5h◆The One-Word Census: Answer-Choice Conformity Across 44 Language Models5h◆Creative Integration: A Decidable Criterion of Creativity5h◆BERT-based Models vs. Large Language Models for Low-Resource Named Entity Recognition: A Comparative Study on Marathi5h◆Joint Optimization for Greedy Longest-match Tokenization5h◆Kimi K3: Open Frontier Intelligence5h◆The Few-shot Dilemma: Over-prompting Large Language Models5h◆Speculative Pipeline Decoding: Higher-Accuracy Drafting with Hidden Latency via Pipeline Parallelism5h◆Bayesian Complete-Pooling in Cross-Subject Classification for Motor Imagery Electroencephalogram5h◆StageGuard: Physiologically Constrained Sleep Staging5h◆Soft-Constrained Optimization of Latent Space in Variational Autoencoders5h◆Beyond Error-vs-Discard Characteristic: Toward Stable and Reliable Evaluation for Face Image Quality Assessment5h◆Analyzing the Importance of Blank for CTC-Based Knowledge Distillation5h◆Predicting Channel Closures in the Lightning Network with Machine Learning5h◆Evaluation of Blood Vessel Segmentation Methods on Hard-to-Detect Vascular Structures5h◆MOCA: A Transformer-based Modular Causal Inference Framework with One-way Cross-attention and Cutting Feedback5h◆
News/Classifier Chain-based Pathological Test Recommendation
arxiv
PublishedJuly 10, 2026 at 4:00 AM
▲bullish

Classifier Chain-based Pathological Test Recommendation

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

arXiv:2607.08299v1 Announce Type: new Abstract: Accurate and timely diagnoses are essential for quality patient care. However, delayed recommendation of diagnostic tests and physicians' subjective interpretations can hinder effective care. This study introduces a pathological test recommendation sys

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Mentioned models
06
  • 01
    Logistic Regression
  • 02
    Decision Tree
  • 03
    Random Forest
  • 04
    Classifier Chain (CC)
  • 05
    Majority Voting ensemble model
  • 06
    SHAP (SHapley Additive Explanations)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machine learning#diagnosis#healthcare#explainability

No replies yet. Be first.

Mentioned models
06
  • 01
    Logistic Regression
  • 02
    Decision Tree
  • 03
    Random Forest
  • 04
    Classifier Chain (CC)
  • 05
    Majority Voting ensemble model
  • 06
    SHAP (SHapley Additive Explanations)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machine learning#diagnosis#healthcare#explainability

Related coverage

More from ARXIV
arxivReverso: Efficient Time Series Foundation Models for Zero-shot Forecasting5harxivCoherent Without Grounding, Grounded Without Success: Observability and Epistemic Failure5harxivAutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models5harxivSheaf-Laplacian Obstruction and Projection Hardness for Cross-Modal Compatibility on a Modality-Independent Site5h
The Bubble Brief
WEEKLY

Read machine learning insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews