Predicta Med utilizes deep learning algorithms to analyze aggregated electronic medical records and claims data, identifying correlations to undiagnosed autoimmune diseases. This platform enables early detection and targeted intervention, improving patient outcomes while reducing healthcare costs and physician workload.
Funding
$2.3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
GFFounders
Product
Problem
Diagnosing autoimmune diseases is often a lengthy and complex process due to the wide range of symptoms and the difficulty in distinguishing them from other conditions. This diagnostic delay can lead to delayed treatment, disease progression, and increased healthcare costs.
Solution
Predicta Med offers a deep-learning platform designed to accelerate the early detection of autoimmune diseases. The platform aggregates and analyzes data from electronic medical records (EMR) and claims data, enriching it with a proprietary dataset and medical logic. By applying advanced machine learning techniques, the platform identifies correlations and patterns indicative of specific autoimmune conditions, providing primary care physicians with actionable insights to identify at-risk patients and facilitate earlier intervention. This approach aims to improve patient outcomes, reduce healthcare costs, and alleviate the burden on physicians.
Target Audience
The primary target audience includes primary care physicians and healthcare providers seeking to improve early detection and intervention for patients at risk of autoimmune diseases.
Features
- Data aggregation from diverse sources, including EMR and claims data
- Proprietary data enrichment process using a unique dataset and medical logic
- Deep learning algorithms for identifying correlations between patient data and autoimmune diseases
- Predictive models validated for rheumatoid arthritis, ulcerative colitis, psoriatic arthritis, lupus, multiple sclerosis, Crohn’s disease, and celiac disease
- Actionable insights for primary care physicians to identify at-risk patients
- Context embedding to improve the accuracy of the AI models
- Self-supervised learning techniques to continuously improve model performance
- Natural language processing to extract relevant information from unstructured data
- Multi-source data fusion to combine information from various sources for a comprehensive analysis