nference provides a clinical AI platform that integrates and analyzes extensive healthcare data, including electronic health records and imaging, to enhance patient outcomes through personalized treatment insights. The platform addresses the challenge of fragmented healthcare data by utilizing advanced algorithms for data harmonization and real-world evidence generation, enabling researchers to derive actionable insights for various therapeutic areas.
Funding
Funding not disclosed
Founders
Product
Problem
Healthcare data is often fragmented and unstructured, residing in disparate systems and formats. This makes it difficult for researchers and clinicians to access and analyze comprehensive patient information, hindering the development of personalized treatments and improved patient outcomes. Extracting meaningful insights from electronic health records (EHRs), imaging data, and other clinical sources requires significant manual effort and expertise.
Solution
nference provides a clinical AI platform that integrates and harmonizes diverse healthcare data sources, including EHRs, radiology images, pathology slides, ECG waveforms, and clinical notes. The platform utilizes AI-powered de-identification algorithms, a proprietary knowledge graph, and a curated common data model to transform raw patient data into research-ready datasets. By applying advanced mathematics and AI algorithms, nference enables researchers to design patient cohorts, explore patient journeys, and build predictive models. The platform also offers an imaging biomarker platform for anatomical computations and an advanced data science workbench.
Target Audience
The primary target audience includes clinical researchers, data scientists, and healthcare systems seeking to leverage real-world data for personalized treatments, improved patient outcomes, and accelerated research.
Features
- AI-powered de-identification algorithms for structured data, unstructured text, and images
- Proprietary knowledge graph to harmonize noisy patient data into clean variables
- Curated common data model enabling federated analytics and model building
- Triangulation of public and proprietary data sources for holistic insights
- Applied mathematics and AI algorithms to transform medical records into organized datasets
- Data quality validation framework to clinically validate curated real-world data (RWD)
- Imaging Biomarker Platform for deriving new, objective metrics from medical images