Fortune 500 Biotech Pioneer Uses Snorkel Flow for Chronic Disease Data Extraction
Company Size
1,000+
Region
- America
Country
- United States
Product
- Snorkel Flow
Tech Stack
- Machine Learning
- Custom Model Development
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Digital Expertise
- Productivity Improvements
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Healthcare & Hospitals
- Life Sciences
Applicable Functions
- Product Research & Development
- Quality Assurance
Services
- Software Design & Engineering Services
- System Integration
About The Customer
The customer is a Fortune 500 biotech pioneer known for its innovative approaches in the life sciences and healthcare sectors. This company is at the forefront of developing treatments and conducting extensive clinical trials to address chronic diseases. With a large-scale operation and a significant amount of data to process, the company required advanced technological solutions to maintain its competitive edge and continue its groundbreaking work. The biotech giant has a global presence and is committed to leveraging cutting-edge technology to enhance its research capabilities and operational efficiency.
The Challenge
Building AI applications to extract entities requires high domain expertise and large amounts of labeled training data, which is expensive and time-consuming. The biotech company faced the challenge of processing a vast amount of clinical trial documents to extract critical chronic disease data. Traditional methods of manual labeling were not only slow but also costly, making it impractical for the scale required. The need for a more efficient and accurate solution was paramount to meet the demands of their research and development processes.
The Solution
The biotech company implemented Snorkel Flow to address their data extraction challenges. Snorkel Flow allowed them to build a custom machine learning model with an impressive 99.1% accuracy. By adjusting the label schema and re-labeling programmatically, they were able to streamline the data extraction process significantly. This approach eliminated the need for extensive manual labeling, which was both time-consuming and costly. The use of Snorkel Flow enabled the company to process approximately 300,000 documents in minutes, a task that would have otherwise taken a substantial amount of time and resources. The solution not only improved accuracy but also drastically reduced the time required to adjust label schemas from one year to just one day.
Operational Impact
Quantitative Benefit
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