Fortune 500 Biotech Pioneer Uses Snorkel Flow for Chronic Disease Data Extraction
公司规模
1,000+
地区
- America
国家
- United States
产品
- Snorkel Flow
技术栈
- Machine Learning
- Custom Model Development
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Digital Expertise
- Productivity Improvements
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
适用行业
- 医疗保健和医院
- 生命科学
适用功能
- 产品研发
- 质量保证
服务
- 软件设计与工程服务
- 系统集成
关于客户
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.
挑战
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.
解决方案
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.
运营影响
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