Objective:

How to leverage information from previously executed clinical trials to optimally design a new trial.

Key Challenges:

• Lack of format standardization in protocol documents
• Identifying amendment entries to the same study
• Presence of non-standard tables
• Presence of scanned objects/images in the protocol document

Primary Fields of Interest:

• Trial Disease Area
  • Therapeutic Area (Oncology, Cardio, etc.)
  • Clinical Indication (NHL, NSCLC, etc.)
  • Trial Phase
  • Therapy (Monotherapy, Adjuvant, etc.)
  • Disease Phase (Metastatic, Locally Advanced, etc.)

• Study Design
  • Treatment Duration
  • Key Screening Procedures
  • Target Population (Inclusion/Exclusion Criterion)
  • Dosage/Active Agent
  • Route of administration
  • Complex Design Features (Adaptive, Cross Over, etc.)

• Protocol Definitions
  • Patient Randomization Target
  • Site Activation Target
  • Study Rationale
  • Study Type (Interventional, Observational)
  • Definition of Adverse Events
  • Assessment of Compliance/Adherence

Approach:

• Feature extraction from a repository of protocol documents to identify key features and tags
• Parse through the trial document for topic classification
• Apply NLP techniques to obtain an intelligent library of feature sets from a past trial repository
• Superimpose operational and performance data to identify drivers and provide study recommendations