📄 Core Papers (Annotated)

📄 Paper: “Deep Learning for Sepsis Prediction”

• Key findings
• Methodology summary
• Strengths / weaknesses
• Relevance to our work

📄 Literature Synthesis (CRITICAL NOTE)

• Trends across papers
• Gaps in existing research
• Contradictions in findings
• Opportunities for innovation

📄 Weekly Literature Updates

• Newly published papers
• Quick summaries (5–10 bullets each)

📄 Experiment #017 – LSTM Model v3

• Hypothesis
• Dataset version
• Feature set
• Model architecture
• Training parameters

• AUC: 0.87
• Precision/Recall

• Overfitting after epoch 12
• Missing value handling improved results

• Try transformer-based model

📄 Experiment #018 – Transformer Model v1

• Direct continuation
• Comparison vs LSTM

📄 Experiment Index

• Table of all experiments
• Links + key metrics

📄 Dataset Documentation

• Source descriptions
• Cleaning procedures
• Bias considerations
• Missing data handling

📄 Feature Engineering Guide

• Feature list with definitions
• Why each feature matters
• Derived features

📄 Model Architectures

• LSTM
• Transformer
• Baseline models

• Diagrams
• Code snippets
• Performance comparisons

📄 Model Performance Dashboard

• Metrics across experiments
• Visual comparisons

📄 Error Analysis

• False positives
• False negatives
• Patterns in failure cases

📄 Clinical Interpretation

• What results mean in real-world care
• Limitations

📄 Weekly Lab Meeting Notes

• Discussion summaries
• Decisions made
• Open questions

📄 Internal Discussions

• Debate on model approaches
• Tradeoffs

📄 Task Assignments

• Who is doing what
• Deadlines

📄 Draft Paper (Working Version)

• Abstract
• Introduction
• Methods
• Results
• Discussion

📄 Figures & Visualizations

• Charts
• Model diagrams

📄 Submission Tracker

• Journals submitted to
• Reviewer feedback
• Revision notes

📄 “What We’re Learning About Early Sepsis Detection”

• Simplified insights
• Non-technical explanation

📄 Key Breakthroughs (Updated)

• Major milestones
• Visual summaries

📄 Open Questions for the Community

• Invite feedback
• Encourage collaboration

📄 Active Grants

• NIH grant details
• Milestones

📄 Grant Writing Notes

• Proposal drafts
• Supporting arguments

📄 Compliance & Ethics

• IRB approvals
• Data privacy notes

💥 Why This Is a Perfect Notearama Use Case

1. Research is fragmented

• Papers → PDFs
• Notes → private
• Experiments → scattered
  
  
  👉 This becomes a single living system of record

2. Research is dynamic, not static

• Traditional publishing = slow + final
• Research reality = iterative + messy
  
  
  👉 Notearama supports continuous publishing of evolving insights

3. Visibility is limited

• Most research stays hidden in notebooks
  
  
  👉 Notearama exposes it to:
• Search engines
• AI agents
• Broader audiences

4. Collaboration is inefficient

• Email threads
• Slack fragments
  
  
  👉 This becomes a shared, structured collaboration layer

📄 Notebook Change Log

• New experiments added
• Updated models
• Revised conclusions

📄 “What’s New This Month”

• High-level summary for stakeholders