28sungmin

Inspect DICOM (X-ray) header labels — verify presence/missingness of 21 clinically important header tags and compute a curation score (0-100). Use for LYDUS data quality assessment of DICOM imaging metadata before large-scale analysis.

Detect and label de-identification–target personal information (names, IDs, addresses, dates/times/periods, other hospitals) in unstructured Korean clinical notes using regular-expression rules, wrapping each match as <LABEL>string</LABEL>.…

Check the label-curation quality of GE MUSE ECG exports — verify presence/missingness of 35 ECG header attributes (3 classes) and compute a curation score (0-100), optionally rendering the 12-lead waveform. Use for LYDUS data quality assessment of ECG-derived…

Detect demographic bias in LYDUS data quality metrics by computing all 16 QUIQ quality metrics (completeness, range/date/format/sequence validity, preciseness, fidelity, class/instance diversity, sentence/vocabulary diversity, logical accuracy,…

Validate format of medical codes (ICD-9/10/11, SNOMED-CT, RxNorm, LOINC, ATC) in a QUIQ-format table. Identifies code type from variable name/description, then validates each value against the corresponding regex. Use for data quality assessment of medical…

Convert MIMIC-IV clinical data into QUIQ (Quality Intelligence Unified Query) long-format table. Use when transforming MIMIC-IV tables to QUIQ format for data quality assessment, when applying QUIQ Mapping_info rules to MIMIC-IV variables, or when building a…

Detect IQR outliers (1.5×IQR rule) in numeric variables of a QUIQ-format table and compute Range Validity (% of non-outlier values). SQL version available via DuckDB PERCENTILE_CONT. No API key required. Use for LYDUS data quality assessment of numeric value…

Generate VIA (Variable Information Archive) table from a user-provided documentation file (PDF or text). Requires a data dictionary or schema documentation to produce meaningful variable descriptions. If no document is provided, the skill will ask for one…

Detect demographic bias in LYDUS data quality metrics by computing all 16 QUIQ quality metrics (completeness, range/date/format/sequence validity, preciseness, fidelity, class/instance diversity, sentence/vocabulary diversity, logical accuracy,…

Calculate cross-sectional consistency for categorical string variables in a QUIQ-format table using a local exo LLM to group semantically equivalent values. Runs on an on-prem local exo LLM (closed-network safe; no external API key). Use for data quality…

Validate date values in a QUIQ-format table. Checks Event_date column and Mapping_info_1='date' rows using standard format parsing, Korean date formats, and optional LLM fallback. Use for data quality assessment of temporal data.

Validate format of medical codes (ICD-9/10/11, SNOMED-CT, RxNorm, LOINC, ATC) in a QUIQ-format table. Identifies code type from variable name/description, then validates each value against the corresponding regex. Use for data quality assessment of medical…

Detect logical outliers in clinical variables using a multi-model ensemble (Quantile Regression + Gradient Boosting + Autoencoder for numeric; OneClassSVM + IsolationForest + Autoencoder for categorical). Uses an on-prem local exo LLM (OpenAI-compatible;…

Evaluate accuracy of unstructured clinical notes and radiology reports in a QUIQ-format table using a local exo LLM. Detects diagnostic, procedural, drug, demographic, and date errors in clinical notes; identifies critical errors in radiology impressions.…

Evaluate completeness (fidelity) of unstructured clinical notes and radiology reports in a QUIQ-format table. Uses a local exo LLM to check whether required template items are mentioned in each note. Runs on an on-prem local exo LLM (closed-network safe; no…

Convert MIMIC-IV clinical data into QUIQ (Quality Intelligence Unified Query) long-format table. Use when transforming MIMIC-IV tables to QUIQ format for data quality assessment, when applying QUIQ Mapping_info rules to MIMIC-IV variables, or when building a…