Identify & classify medical conditions based on medical history. Learn to classify using tagged de-identified population medical data.
Leverage expert’s medical knowledge for context. Use embedding to quantify features relationships.
Use big data of untagged medical records to train Deep Neural Networks using self-supervised learning.
Analyze doctor’s notes and records to augment and enrich the structured data, and learn local jargon.
Collect and combine multiple data types and sources (EMR, Claims, doctor’s notes, personal sensors). Process feedback from users and EMR.
The software can be used by the clinician in real time during a patient’s visit or on demand as a report. Shorten diagnosis time to months from several years.
Our solution can be implemented directly into the EMR software and therefore can be integrated into the clinician’s workflow.
Engine built on both EMR & Claims data. we aggregate data from Israeli and US companies and health systems.
Using knowledge trained on our database and integrating clinician's knowledge and concluding for a single patient.
Successful machine learning solutions requires providing clinicians with information on how a decision was reached. Predicta Med’s results and key data features are provided so clinicians can be confident in the recommendation.
Our technology is patent pending.