R package to fetch and explore clinical trials data from freely available registries. Fetch data in bulk, customize data and build comprehensive html reports. Currently, it supports the ClinicalTrials.gov registry and CTTI AACT (Access to Aggregate Content of ClinicalTrials.gov).

End-to-end data science project predicting clinical trial completion using AACT data. Includes EDA, feature engineering, ML models (LogReg, XGBoost), SHAP interpretability, and Streamlit deployment.

Aggregated Analysis of ClinicalTrials.gov (AACT) publicly available database is imported into a PostgreSQL database for Cancer Research Analysis. This database contains more than 40 relations, each having millions of records. Complex pSQL queries are used to communicate with the PostgreSQL database using pgAdmin 4. Answers related to cancer tria…

Docker image of database dump from ClinicalTrials.gov

⭐️ AAct Portable Version 2026 | Setup Installer v1.0 | Keygen License Key | Activation Full Version | Latest Build Pro Updated | Bypass Mod Loader | Pre-Activated Serial | Get Desktop Working Windows 10/11 | Direct Genuine Original PC Download ⭐️

DecisionLENS — LangGraph agentic data quality agent for clinical trials — cross-validates AACT, PubMed, OpenFDA with rule engine + XGBoost risk model + LLM prioritisation. Streamlit dashboard with full provenance and LLMOps tracing.

End-to-end ML project for clinical trial site performance prediction: data engineering (AACT), feature modeling, and predictive analytics.

AACT-powered advanced meta-analysis: auto-extract trial data from a ClinicalTrials.gov/AACT snapshot, then run pairwise/NMA/dose-response/proportions with a full advanced suite (Egger/Peters/trim-fill/PET-PEESE, leave-one-out, meta-regression, subgroup, cumulative, rare-events, NMA Bucher inconsistency). Offline, config-driven.

Machine learning model to rank which registered clinical trials are most likely to terminate (AACT/ClinicalTrials.gov)

Maps clinical trial density (from the AACT ClinicalTrials.gov mirror) against global disease burden (IHME SDI) to surface "research deserts" — geographies where burden is high but trial activity is low.

AACT Prospective Cancer Trials — SQL cohort & patterns (ClinicalTrials.gov/AACT). pgAdmin + SQL view, counts, intervention distribution, top-term plots.

🔍 Analyze cancer trial data with SQL queries on the AACT database, uncovering insights into breast cancer studies and intervention patterns.