
Worked on the HealthRex/CDSS repository, delivering clinical decision support features and data-driven analytics over five months. Developed and enhanced a FastAPI-based recommender API, integrating BigQuery for scalable clinical data retrieval and ICD-10 lookups. Implemented secure environment variable management, improved repository hygiene, and refactored data pipelines for VM migration readiness. Leveraged Python, SQL, and Jupyter Notebooks to build reproducible analytics workflows, including LLM-assisted code matching and error analysis frameworks. Focused on enabling PSB submission readiness by preparing experimental notebooks and reference datasets, while maintaining production reliability through logging cleanup and robust error handling to support ongoing clinical informatics research.
August 2025 — HealthRex/CDSS focused on enabling PSB submission readiness for the Embedding Pilot Experiment. Delivered extensive code for PSB submission with data-driven results analysis, plus notebooks for experimental/pilot studies to support submission evaluation and reproducibility.
August 2025 — HealthRex/CDSS focused on enabling PSB submission readiness for the Embedding Pilot Experiment. Delivered extensive code for PSB submission with data-driven results analysis, plus notebooks for experimental/pilot studies to support submission evaluation and reproducibility.
June 2025 — HealthRex/CDSS: Delivered core enhancements to clinical case processing and data pipelines, enabling secure GPT-assisted processing, ICD-10 matching, and scalable BigQuery analytics. Prepared VM migration readiness and introduced saved-file-based order retrieval to minimize downtime. Launched LLM error analysis and a Medical Recommender demo with new ICD-10 endpoints and updated documentation. Implemented production log cleanup to reduce noise and improve reliability. Collectively, these efforts improve data accuracy, operational resilience, and time-to-insight for clinical decision support.
June 2025 — HealthRex/CDSS: Delivered core enhancements to clinical case processing and data pipelines, enabling secure GPT-assisted processing, ICD-10 matching, and scalable BigQuery analytics. Prepared VM migration readiness and introduced saved-file-based order retrieval to minimize downtime. Launched LLM error analysis and a Medical Recommender demo with new ICD-10 endpoints and updated documentation. Implemented production log cleanup to reduce noise and improve reliability. Collectively, these efforts improve data accuracy, operational resilience, and time-to-insight for clinical decision support.
May 2025 focused on security hygiene, data-layer groundwork, and extended decision-support capabilities for HealthRex/CDSS. Key achievements include: 1) Recommender API Security and Maintenance Enhancements (load API keys from environment variables; update .gitignore to exclude logs and notebook artifacts); 2) Phase 1 Recommender with BigQuery Data Layer (establish BigQuery-based data retrieval with foundational notebook and dependencies); 3) Medical Recommender API Enhancements (Antibiotics) (antibiotic susceptibility integration, structured API for clinical cases, language-model-assisted code matching, and ICD-10 lookups via BigQuery). Major bugs fixed: reduced exposure and deployment noise via environment-based keys and improved gitignore. Overall impact: stronger security posture, cleaner deployments, and a scalable data-driven foundation enabling faster feature delivery and clinical decision support. Technologies/skills demonstrated: environment variable management, Git hygiene, BigQuery integration, API design, ICD-10 lookups, and NLP-assisted code matching.
May 2025 focused on security hygiene, data-layer groundwork, and extended decision-support capabilities for HealthRex/CDSS. Key achievements include: 1) Recommender API Security and Maintenance Enhancements (load API keys from environment variables; update .gitignore to exclude logs and notebook artifacts); 2) Phase 1 Recommender with BigQuery Data Layer (establish BigQuery-based data retrieval with foundational notebook and dependencies); 3) Medical Recommender API Enhancements (Antibiotics) (antibiotic susceptibility integration, structured API for clinical cases, language-model-assisted code matching, and ICD-10 lookups via BigQuery). Major bugs fixed: reduced exposure and deployment noise via environment-based keys and improved gitignore. Overall impact: stronger security posture, cleaner deployments, and a scalable data-driven foundation enabling faster feature delivery and clinical decision support. Technologies/skills demonstrated: environment variable management, Git hygiene, BigQuery integration, API design, ICD-10 lookups, and NLP-assisted code matching.
Month: 2025-04 Key features delivered: - Data Analysis Notebook and AIM4 project hygiene for Adult ED Cohort: Adds a detailed Jupyter Notebook for analyzing the Adult ED Cohort without allergies, with utility functions for identifying unique orders and patient encounters, and a mapping for cleaning antibiotic names; also adds a .gitignore for the AIM4 SQL queries directory to improve repository hygiene. - FastAPI-based Recommender API for antibiotic susceptibility data: Introduces a new FastAPI-based API that can query BigQuery for medications and procedures based on diagnosis, gender, and year, with validation and interactive docs; includes setup instructions, error handling, and project structure. Major bugs fixed: - None reported this month. Overall impact and accomplishments: - Accelerated data exploration and decision support for Adult ED insights by delivering data analysis tooling and a scalable API; improved repository hygiene and maintainability; documented setup and error handling for quicker onboarding and fewer runtime issues. Technologies/skills demonstrated: - Jupyter notebooks, data cleaning utilities, FastAPI, BigQuery integration, repository hygiene and project structuring, API validation and documentation, setup and error handling.
Month: 2025-04 Key features delivered: - Data Analysis Notebook and AIM4 project hygiene for Adult ED Cohort: Adds a detailed Jupyter Notebook for analyzing the Adult ED Cohort without allergies, with utility functions for identifying unique orders and patient encounters, and a mapping for cleaning antibiotic names; also adds a .gitignore for the AIM4 SQL queries directory to improve repository hygiene. - FastAPI-based Recommender API for antibiotic susceptibility data: Introduces a new FastAPI-based API that can query BigQuery for medications and procedures based on diagnosis, gender, and year, with validation and interactive docs; includes setup instructions, error handling, and project structure. Major bugs fixed: - None reported this month. Overall impact and accomplishments: - Accelerated data exploration and decision support for Adult ED insights by delivering data analysis tooling and a scalable API; improved repository hygiene and maintainability; documented setup and error handling for quicker onboarding and fewer runtime issues. Technologies/skills demonstrated: - Jupyter notebooks, data cleaning utilities, FastAPI, BigQuery integration, repository hygiene and project structuring, API validation and documentation, setup and error handling.
March 2025 monthly summary for HealthRex/CDSS: Focused on enabling AIM4 integration and cleaning up the repository to support faster feature delivery and easier maintenance. Delivered AIM4 integration by migrating the AIM4 submodule to a regular folder and registering it as a new subproject. Performed targeted cleanup removing duplicated PDF/PNG files and ED culture order data analyses. These changes reduce build noise, improve code clarity, and lay groundwork for smoother onboarding and future feature work.
March 2025 monthly summary for HealthRex/CDSS: Focused on enabling AIM4 integration and cleaning up the repository to support faster feature delivery and easier maintenance. Delivered AIM4 integration by migrating the AIM4 submodule to a regular folder and registering it as a new subproject. Performed targeted cleanup removing duplicated PDF/PNG files and ED culture order data analyses. These changes reduce build noise, improve code clarity, and lay groundwork for smoother onboarding and future feature work.

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