You will get Standardized Healthcare Dataset Harmonization & Cleaning
Project details
Are your healthcare analyses stalled by fragmented EHR databases, messy hospital registries, or inconsistent clinical coding?
Unclean healthcare data is unusable. Converting raw electronic health records into research-ready, harmonized datasets requires specialized clinical vocabulary knowledge and rigorous ETL engineering.
As a medical candidate (MBBCh) and health data engineer, I provide production-grade dataset cleaning and harmonization tailored for epidemiological groups, health-tech startups, and hospital research teams. I transform chaotic data into structured, standardized clinical datasets ready for statistical analysis or machine learning.
WHAT THIS SERVICE INCLUDES:
• Raw Data Preprocessing: Deduplication, structural normalization, and comprehensive data quality audits.
• Clinical Standards Harmonization: Mapping local, messy source codes to international vocabularies (ICD-10-CM, SNOMED CT, LOINC, RxNorm).
• Advanced Data Imputation: Implementing Multiple Imputation by Chained Equations (MICE) scripts for rigorous missing data handling.
• Scalable ETL Pipelines: Building automated SQL/Python (Pandas/PySpark) to process large-scale, multi-center health databases.
Unclean healthcare data is unusable. Converting raw electronic health records into research-ready, harmonized datasets requires specialized clinical vocabulary knowledge and rigorous ETL engineering.
As a medical candidate (MBBCh) and health data engineer, I provide production-grade dataset cleaning and harmonization tailored for epidemiological groups, health-tech startups, and hospital research teams. I transform chaotic data into structured, standardized clinical datasets ready for statistical analysis or machine learning.
WHAT THIS SERVICE INCLUDES:
• Raw Data Preprocessing: Deduplication, structural normalization, and comprehensive data quality audits.
• Clinical Standards Harmonization: Mapping local, messy source codes to international vocabularies (ICD-10-CM, SNOMED CT, LOINC, RxNorm).
• Advanced Data Imputation: Implementing Multiple Imputation by Chained Equations (MICE) scripts for rigorous missing data handling.
• Scalable ETL Pipelines: Building automated SQL/Python (Pandas/PySpark) to process large-scale, multi-center health databases.
Data Tool
PythonWhat's included
| Service Tiers |
Starter
$250
|
Standard
$600
|
Advanced
$1,200
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 8 days |
Number of Revisions | 2 | 2 | 3 |
Frequently asked questions
About Ahmed
Clinical Biostatistician & Healthcare AI Consultant | MBBCh
Port Said, Egypt - 10:35 pm local time
I consult for digital health companies, clinical research teams, and academic faculty to build, validate, and publish defensible healthcare machine learning models and biostatistical pipelines.
As a clinical candidate (MBBCh) and biostatistical consultant, I combine deep clinical literacy with rigorous machine learning methodology. I ensure your observational research, risk score calculators, and clinical AI products transition seamlessly from raw data to peer-reviewed publications or clinical deployment.
CORE CONSULTING TIERS:
Tier 1: Clinical Research Analytics & Advanced Medical Biostatistics
• Observational registry study design, propensity score matching (PSM/IPTW), and survival modeling (Cox PH, Fine-Gray competing risks).
• Handling missing data patterns via Multiple Imputation by Chained Equations (MICE).
• Drafting publication-grade Methods and Results sections compliant with STROBE, CONSORT, and PRISMA standards.
Tier 2: Clinical AI Development, External Validation & Reporting Audits
• Prognostic and diagnostic risk calculator development utilizing EHR datasets (MIMIC-IV, eICU) and clinical registries.
• Multi-cohort out-of-distribution (OOD) generalization testing, transportability auditing, and discrimination/calibration diagnostics.
• Explainable AI (SHAP / feature attribution) integration for black-box clinical models.
• Pre-submission auditing for full TRIPOD-AI and TRIPOD+AI compliance.
TECHNICAL STACK & FRAMEWORKS:
• Statistical/ML Environments: Python (Scikit-Learn, PyTorch, Pandas), R (Bioconductor, Tidyverse), SQL.
• Vocabularies & Standards: ICD-10, SNOMED CT, LOINC | TRIPOD-AI, STROBE, CONSORT.
If you require publication-ready biostatistics or rigorous external validation for your clinical ML pipeline, send a message or invite to discuss your dataset.
Steps for completing your project
After purchasing the project, send requirements so Ahmed can start the project.
Delivery time starts when Ahmed receives requirements from you.
Ahmed works on your project following the steps below.
Revisions may occur after the delivery date.
Data Quality Audit & Preprocessing
Initial ingestion, deduplication, handling inconsistent formats, and diagnosing missingness patterns.
Clinical Vocabulary & Schema Mapping
Harmonizing raw source codes to standardized clinical vocabularies (e.g., ICD-10-CM, SNOMED CT).