In current times people think that machine learning is the panacea, a belief that is far from the truth. At panacea lab we aim to build machine learning, computer vision, and NLP methods that help to generate insights from multi-modal large-scale data sources. With applications to precision medicine, medical informatics, astroinformatics and other domains, our work addresses domain-specific problems with data science methods and practices. Additionally, we are fully invested in helping science reproducibility by releasing (when possible) open-source and publicly available datasets and tools to aid reproducibility efforts.
With the new challenges brought by the data deluge in all fields, we are open to collaborations. Contact us here.
R package for Automated PHenotype Routine for Observational Definition, Identification, Training and Evaluation (APHRODITE). Built the OHDSI collaborative.
R package for Mapping Between OHDSI Concept Identifiers to Unified Medical Language System (UMLS). Built for the OHDSI collaborative.
A curated and standardized adverse drug event resource to accelerate drug safety research. Built for the OHDSI collaborative.
Provenance-centered dataset of drug-drug interactions.
Introducing a Data Mining Framework for the Creation of Large-scale Content-based Image Retrieval Systems
For a other code and datasets visit the lab's github page.
For a full list of publications go here.
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