Human Feedback Types
strongExpert Verification
Directly usable for protocol triage.
"Clinical notes contain unstructured text provided by clinicians during patient encounters."
HFEPX · Eval paper review
LTR-ICD: A Ranking-Aware Framework for Automatic ICD Coding
Mohammad Mansoori, Amira Soliman, Farzaneh Etminani
Published
Oct 15, 2025
Citations
0
Trust level
Moderate
Usefulness score
65/100 (Medium)
Extraction confidence
70% (Moderate)
Derived from extracted protocol signals and abstract evidence.
Rater population
Domain Experts
Signals refreshed
Aug 21, 2026
Should you rely on this paper?
This paper has useful evaluation signal, but protocol completeness is partial; pair it with related papers before deciding implementation strategy.
Use this for comparison and orientation, not as your only source.
Best use
Secondary protocol comparison source
Use if you need
A secondary eval reference to pair with stronger protocol papers.
What to verify
Validate the evaluation procedure and quality controls in the full paper before operational use.
Main weakness
No major weakness surfaced.
Human feedback signal
Detected
From extracted signals
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
65/100
Moderate-confidence candidate
Useful as a secondary reference; validate protocol details against neighboring papers.
If you are doing eval pipeline work, start here
Abstract
Clinical notes contain unstructured text provided by clinicians during patient encounters. These notes are usually accompanied by a sequence of diagnostic codes following the International Classification of Diseases (ICD). Correctly assigning and ordering ICD codes is essential for medical diagnosis and reimbursement. However, automating this task remains challenging. State-of-the-art methods treated this problem as a classification task, leading to ignoring the order of ICD codes that is essential for different purposes. In this work, as a first attempt, we approach this task from a retrieval system perspective to consider the order of codes, thus formulating this problem as a classification and ranking task. Our results and analysis show that the proposed framework has a superior ability to identify high-priority codes compared to other methods. For instance, our model's accuracy in correctly ranking primary diagnosis codes is 47%, compared to 20% for the state-of-the-art classifier. Additionally, in terms of classification metrics, the proposed model achieves a micro- and macro-F1 scores of 0.6065 and 0.2904, respectively, surpassing the previous best model with scores of 0.6035 and 0.2741.
What we could verify
These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.
Evaluation Modes
strongAutomatic Metrics
Includes extracted eval setup.
"Clinical notes contain unstructured text provided by clinicians during patient encounters."
Quality Controls
missingNot reported
No explicit QC controls found.
"Clinical notes contain unstructured text provided by clinicians during patient encounters."
Benchmarks / Datasets
missingNot extracted
No benchmark anchors detected.
"Clinical notes contain unstructured text provided by clinicians during patient encounters."
Reported Metrics
strongAccuracy, F1, F1 macro
Useful for evaluation criteria comparison.
"For instance, our model's accuracy in correctly ranking primary diagnosis codes is 47%, compared to 20% for the state-of-the-art classifier."
Rater Population
strongDomain Experts
Helpful for staffing comparability.
"Clinical notes contain unstructured text provided by clinicians during patient encounters."
Benchmarks and datasets
No benchmark or dataset names were extracted from the available abstract.
Reported metrics
accuracyf1f1 macro
Human feedback details
- Uses human feedback
- Yes
- Feedback types
- Expert Verification
- Rater population
- Domain Experts
- Unit of annotation
- Ranking
- Expertise required
- Medicine, Coding
Evaluation details
- Evaluation modes
- Automatic Metrics
- Agentic eval
- None
- Quality controls
- Not reported
- Evidence quality
- Moderate
- Use this page as
- Secondary protocol comparison source
Research brief
Metadata summary
Clinical notes contain unstructured text provided by clinicians during patient encounters.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Key takeaways
- Clinical notes contain unstructured text provided by clinicians during patient encounters.
- These notes are usually accompanied by a sequence of diagnostic codes following the International Classification of Diseases (ICD).
- Correctly assigning and ordering ICD codes is essential for medical diagnosis and reimbursement.
Researcher actions
- Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
- Validate inferred eval signals (Automatic metrics) against the full paper.
- Use related-paper links to find stronger protocol-specific references.
Caveats
- Generated from abstract + metadata only; no PDF parsing.
- Signals below are heuristic and may miss details reported outside the abstract.
Recommended queries
Contribution summary
- For instance, our model's accuracy in correctly ranking primary diagnosis codes is 47%, compared to 20% for the state-of-the-art classifier.
- Additionally, in terms of classification metrics, the proposed model achieves a micro- and macro-F1 scores of 0.6065 and 0.2904, respectively, surpassing the previous best model with scores of 0.6035 and 0.2741.
Researcher checklist
-
Human feedback protocol is explicit
Detected: Expert Verification
-
Evaluation mode is explicit
Detected: Automatic Metrics
-
Quality control reporting appears
No calibration/adjudication/IAA control explicitly detected.
-
Benchmark or dataset anchors are present
No benchmark/dataset anchor extracted from abstract.
-
Metric reporting is present
Detected: accuracy, f1, f1 macro