logical-accuracy

name: logical-accuracy description: Detect logical outliers in clinical variables using a multi-model ensemble (Quantile Regression + Gradient Boosting + Autoencoder for numeric; OneClassSVM + IsolationForest + Autoencoder for categorical). Uses Claude CLI (no API key required). Use for LYDUS data quality assessment of logical consistency in event, diagnosis, prescription, and procedure variables. tier: community category: lydus parameters: quiq_path: description: Path to QUIQ-format CSV file (output of quiq skill). type: string save_path: description: Directory path to save output files (logical_accuracy_summary.csv, logical_accuracy_total.txt, outlier_*.csv). type: string operation_type_manual: description: "True = manually specify target_variable; False = automatic top-N by count." type: boolean target_variable: description: Target variable name (used when operation_type_manual=True). type: string automatic_num: description: Number of top variables to analyze automatically (used when operation_type_manual=False). Default 5. type: integer recommend_num: description: Number of correlated variables recommended by LLM. Default 5. type: integer

Logical Accuracy

Detects logical outliers in clinical variables using a multi-model anomaly detection ensemble. A value is flagged as an outlier only when all three models agree — minimizing false positives.

When to Use This Skill

• After QUIQ conversion, to assess whether recorded clinical values are logically consistent with the patient's clinical context
• To detect physiologically implausible values (e.g., abnormal lab results inconsistent with other measurements)
• As part of LYDUS quality management assessment

SQL Support

Not applicable. This skill requires:

• Claude CLI calls for sex/birthdate variable identification and correlated-variable recommendation
• statsmodels Quantile Regression
• sklearn (GBR, OneClassSVM, IsolationForest, RobustScaler)
• PyTorch Autoencoder with early stopping

Filtering Logic

Two Operation Modes

Pipeline

For each target variable:

1. LLM identifies sex variable and birthdate variable in the QUIQ data
2. LLM recommends recommend_num correlated variables
3. Clinical context vector is built by joining target + recommended variables on (Patient_id, Event_date ± 7 days)
4. Outlier detection based on evaluate_mode:

evaluate_mode=0 (Numeric Event Variables)

• Quantile Regression (q=0.01, 0.99)
• Gradient Boosting Regressor (q=0.01, 0.99)
• Autoencoder (reconstruction error > 98th percentile)
• Outlier: outside ALL bounds simultaneously (upper OR lower)

evaluate_mode=1 (Categorical Variables)

• One-Class SVM (nu=0.02, kernel=rbf)
• Isolation Forest (contamination=0.02)
• Autoencoder (mean reconstruction error > 98th percentile)
• Outlier: flagged by ALL three models simultaneously

Autoencoder Architecture

Encoder: Linear(d → d//1.3) → Tanh → Linear(d//1.3 → d//2)
Decoder: Linear(d//2 → d//1.3) → Tanh → Linear(d//1.3 → d)
Optimizer: Adam (lr=0.001), Loss: MSE, Early stopping (patience=5, min_delta=0.001)

Output

How to Run

import pandas as pd
from scripts.logical_accuracy import get_logical_accuracy

quiq = pd.read_csv("/path/to/quiq.csv")

var_list_target, dict_total, dict_outlier = get_logical_accuracy(
    quiq=quiq,
    operation_type_manual=False,
    target_variable="",        # ignored when operation_type_manual=False
    automatic_num=5,
    recommend_num=5
)

As a script with config

# config.yaml
quiq_path:            /path/to/quiq.csv
save_path:            /path/to/output
operation_type_manual: false
target_variable:      ""       # only needed when operation_type_manual=true
automatic_num:        5
recommend_num:        5

python scripts/logical_accuracy.py --config config.yaml

Critical Notes

1. All-models-agree criterion — a value is an outlier only if all 3 models flag it. This is intentionally conservative to reduce false positives.

2. Clinical context window — correlated numeric variables are matched within ±7 days of the target measurement date (closest value used).

3. Sex + birthdate enrichment — LLM identifies these automatically. If not found, the context vector is built without them.

4. Original code bug fixed — line 661 in LYDUS_Logical_Accuracy.py had a missing comma: 'Outlier Num' 'Logical Accuracy (%)' → Python string concatenation created column 'Outlier NumLogical Accuracy (%)'. Fixed in this skill.

5. Memory management — gc.collect() is called after each major step. For large QUIQ tables (millions of rows), consider limiting automatic_num.

6. Dependencies — statsmodels, scikit-learn, torch, numpy, pandas (LLM: Claude CLI via subprocess)

References

• LYDUS 품질관리 프로그램 활용 가이드라인 (비공개 내부 문서)
• Original Python implementation: LYDUS_Logical_Accuracy.py (이성민 작성)