OPTIMIZED FOR ONCOLOGY INFORMATICS
Turbocharge clinical research or data-abstraction projects.
Inspirata’s NLP engine* autonomously mines clinical documents and reports, in real time, with near-perfect effectiveness.
Whether you are working on a cutting-edge research at an academic medical center or an innovative data-driven healthcare technology solution, our proven NLP engine may save you time, budget, and a lot of frustration.
Execute faster and with greater confidence with more timely, accurate, and complete datasets.
Leverage difficult-to-surface or entirely untapped data sources to enhance your clinical research or business intelligence.
Scale your clinical research and business analytics teams.
Leverage our database of thousands of clinical concepts such as genomic biomarkers, symptoms, side effects, and medications.
Identify disease characteristics, medications, or risk factors from clinical documents to stratify patients and improve the quality of care.
POWERFUL, FLEXIBLE, FULLY AUTOMATED
See the power of our NLP engine for yourselves.
Gain a deeper understanding of how Inspirata’s AI/NLP technology solves various challenges in the oncology informatics space. Inspirata selectively leverages the strengths of multiple AI/NLP methodologies to achieve the best possible results. Watch the two videos below for a demonstration of how different problems require different solutions, and how our hybrid technology stands above the rest.
Inspirata’s AI/NLP technology achieved over 99% sensitivity (i.e. ability to find true positives) and over 98% specificity (i.e. ability to find true negatives). The graphic below illustrates how this performance remained consistently high over 44 QC studies conducted at third-party sites since 2020.
COVID-19 Open Research Dataset
226,000+
Scholarly Articles
99,000+
Full-Text Documents
Our team, in conjunction with HDR UK, applied Inspirata’s NLP engine to the COVID-19 Open Research Dataset (CORD-19) to help researchers around the globe find answers to important questions about the virus.
A full account of this work was published in the Health and Care Informatics edition of the British Medical Journal (BMJ) in January 2021.