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Provectus > 实例探究 > 利用 AI 在 HCLS 中实现文档处理自动化
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Automating Document Processing in HCLS with AI: A Case Study on PSC Biotech

技术
  • 分析与建模 - 机器学习
  • 分析与建模 - 自然语言处理 (NLP)
适用行业
  • 生命科学
  • 药品
适用功能
  • 物流运输
  • 产品研发
用例
  • 行为与情绪追踪
  • 物体检测
服务
  • 数据科学服务
  • 培训
挑战
PSC Biotech 希望通过人工智能实现现有流程自动化,从而增强其文档处理操作。他们需要更快、更准确、更大规模地处理 FDA 483 表格观察结果。手动文档处理既耗时、成本高,又容易出错,给合规性和公司的利润带来风险。
关于客户
PSC Biotech 是一家全球生命科学咨询公司,为 HCLS 领域的公司提供服务。它们有助于确保医疗保健产品符合监管标准并有效地开发、制造和分销。 PSC 已为全球一千多家客户提供服务,需要优化他们的文档处理操作。
解决方案
Provectus 开发了一种基于 AI/ML 的解决方案,用于自动文档处理和分类。他们构建了一个用于观察分类的高度准确的机器学习模型,并通过 CI/CD 管道建立了安全且可重复的机器学习基础设施。该解决方案利用了 AWS 数据湖、预先训练的深度学习和 NLP 算法以及 PyTorch、Tensorflow 和 NLTK 等框架。
运营影响
  • The automated solution developed by Provectus for AI/ML-powered processing and classification of documents has led to significant improvements in PSC Biotech's operations. The solution not only designed and built a highly accurate ML model for observation classification but also set up an entire ecosystem to ensure its performance and cost-efficiency. This included a secure and reproducible, end-to-end ML infrastructure with CI/CD pipelines and all integrations for user-friendly management of documents in PSC Biotech’s existing pipeline. As a result, PSC Biotech can now handle FDA Form 483 observations much faster, more accurately, and cost-effectively, and at scale. The new observation classification solution has enabled PSC Biotech to dramatically decrease the time spent on manual review of observations, optimize processing costs, and increase accuracy and throughput of document processing while mitigating risks of infractions made by mappers and reviewers.

数量效益
  • Automated AI/ML solution for observation classification delivered

  • ML model with precision and recall exceeding 70% on new FDA Form 483 observations

  • Secure, reproducible, end-to-end ML Infrastructure for intelligent document processing

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