Extraction and Analysis of COVID-19 Patient Records

Processing, Structuring, and Analyzing COVID-19 Medical Records

1. Introduction

The overarching aim of this project is to convert large volumes of unstructured or semi-structured medical records into structured, queryable datasets, and to perform advanced analytical operations that yield actionable insights into COVID-19 patient outcomes, comorbidities, and treatment patterns.

This process involves:

Extracting crucial clinical details from free-text encounter notes using Large Language Models (LLMs).
Standardizing clinical terminologies (e.g., ICD-10 codes, RxNorm) to ensure consistent, interoperable records.
Storing the output in robust formats (e.g., Parquet) for efficient reading and manipulation in Apache Spark.
Enabling advanced analytics such as demographic breakdowns, medication frequency analysis, co-morbidity insights, and machine learning classification.

Key technologies include PySpark for distributed data processing, FAISS (Facebook AI Similarity Search) for medication mapping, and visualization libraries for rendering clear, interpretable results.

2. Data Ingestion and Unstructured Note Processing

2.1 Input Sources

Multiple .txt Files: Each file contains semi-structured encounter notes (SOAP format) and free-text observations on demographics, vitals, lab results, and treatments.
Batch Ingestion: For large deployments, an automated pipeline can iterate through directories, reading thousands of text files in parallel.

2.2 LLM-Driven Data Extraction

Leveraging LLMs (e.g., GPT-based APIs) via prompt engineering:

Entity Recognition: Identifies patient name, DOB, address, insurance, symptoms, conditions, and lab results.
Structured JSON Output: The pipeline instructs the model to output hierarchical JSON covering demographics, SOAP notes, medical history, etc.
Fallback Defaults: Missing or ambiguous fields are marked with placeholders like "unknown" or 0 to maintain a consistent schema.

2.3 Validation via Pydantic Models

Once the LLM outputs JSON, Pydantic schemas enforce strict typing (e.g., float for BMI, List[str] for diagnoses).
Invalid or incomplete data triggers errors, ensuring contaminated records do not proceed further.

3. Data Structuring and Schema Design

3.1 Hierarchical Schema

Data is organized into nested fields:

Encounter Information (date, duration, provider, facility).
Demographics (name, DOB, gender, address, insurance, MRN).
Conditions (ICD-10-coded problems, co-morbid diagnoses).
Medications (RxNorm-mapped, usage frequency).
Vitals (temperature, heart rate, BP, oxygen saturation).
Laboratory Results (COVID-19 tests, influenza panels).
Plan (treatments, follow-ups, patient education).

3.2 Transformation into Parquet

The validated JSON is flattened into tabular columns using a schema definition.
Data is written to Apache Parquet:
- Columnar storage for faster reads and queries in Spark.
- Compression advantages reducing I/O overhead.

4. Semantic Enrichment Using FAISS

One central challenge is free-text medication descriptions. To standardize these:

Embedding Generation: A specialized model (e.g., OpenAI Embeddings) converts each medication name into a vector.
FAISS Index: Builds a similarity index of known medication names or codes (e.g., RxNorm, brand names, generics).
Vector Search: Maps each free-text mention to its nearest standard medication entry, even handling spelling variations or brand/generic differences.

This ensures consistency when counting medication frequencies or analyzing prescription patterns.

5. PySpark Workflow and Analytical Processes

5.1 Large-Scale Data Handling

Spark DataFrames: Efficiently processes the flattened Parquet data.
SQL-like Transformations: Complex filtering, grouping, and aggregation logic is performed without enumerating raw code, harnessing Spark’s distributed engine.
Schema Inference: Spark respects the nested structure defined earlier for robust read operations.

5.2 Demographic and Comorbidity Analysis

COVID-19 Subset Extraction: Filters lab results or conditions to focus on positive COVID-19 patients.
Age Binning: Groups patients into brackets such as [0-5], [6-10], [11-17], [18-30], [31-50], [51-70], [71+] for demographic distribution visualizations.
Comorbidity Pairs: Self-join or cross-tab on patient conditions to rank comorbid pairs (e.g., Hypertension & Diabetes), employing lexicographical ordering to avoid duplications.

5.3 Medication Usage Patterns

Medication Frequency: Ranks prescriptions by usage count among COVID-19 patients.
Cross-Matching: Cross-tabulates top diagnoses vs. top medications, revealing which treatments are common for each condition.

5.4 Vital Signs and Disease Severity

Flattened Vitals: Temperature, respiratory rate, heart rate, O2 saturation, and blood pressure are extracted into columns.
Correlation Analysis: Compares vital sign anomalies with severity markers (e.g., ICU admission).
Time-Series or Seasonal Trends: If date/time data is present, analyses can show daily or monthly case evolutions.

6. Data Visualization and Advanced Insights

6.1 Visualization Approaches

Bar Charts: Show top comorbidities, medication frequencies, or age-group distributions.
Heatmaps: Display correlations between multiple numeric features (e.g., age, O2 saturation, heart rate) or relationships between conditions and prescribed medications.
Line/Area Charts: Illustrate cumulative case counts or time series of new positives.

6.2 Complex Analyses

ICU vs. Non-ICU Patient Comparisons: Summarize differences in symptom clusters, vitals, and comorbidities.
Obesity and COVID-19 Severity: Test the correlation between BMI and adverse outcomes or ICU admissions.
Hypothesis Testing: T-tests or chi-square tests for differences in medication usage across demographic groups.

7. Key Technical Challenges and Solutions

Nested JSON: Addressed via Pydantic for structure, then PySpark’s flattening functions.
Multiple explode() Constraints: Solved by carefully staging transformations or using sub-DataFrames.
FAISS Optimization: Tuning vector dimensions, indexing parameters, and similarity thresholds for medication matching.
Missing Data: Dealt with placeholders (unknown, [], or 0), plus filtering out incomplete rows for certain statistical analyses.
Performance:
- Parquet format for efficient data scanning and compression.
- Spark caching to reduce repeated transformations.
- Partition pruning to limit memory usage.

8. Conclusion

By merging unstructured textual encounter notes with robust validation, standard taxonomy mapping, and advanced Spark-based analytics, this project delivers a scalable, end-to-end system for healthcare data intelligence. The LLM pipeline transforms semi-structured notes into high-fidelity JSON records, FAISS enforces consistent medication naming, and PySpark fosters seamless large-scale queries. Researchers and clinicians can then visualize data on demographics, comorbidities, and treatments, supporting critical decision-making in COVID-19 patient care.

Looking forward, the pipeline can expand to real-time ingestion for ongoing epidemiological surveillance, incorporate additional medical taxonomies like UMLS or SNOMED CT, and implement predictive modeling to forecast patient outcomes. Ultimately, it serves as a blueprint for transforming messy clinical data into actionable insights that drive informed policy and evidence-based medicine.