Success Stories

Objective

A leading scientific information provider required labeled data for content related to drug discovery and research funding.

Solution

• Innodata created structured labeled data in XML from structured and unstructured data sources.

• Professional teams of bio-medical and chemistry experts labeled millions of pages of scientific patent information and created structured data for predictive and prescriptive analytics platforms.

• We used advanced entity extraction, text and data mining, augmented by professional scientific SMEs to create high-quality labeled scientific datasets.

Results

• Innodata provided comprehensive information that has helped the client build and maintain reliable, up-to-date information of global patterns that matter.

• The patent analytics platform is used by leading global drug and chemical manufacturers in efforts to avoid patent infringement, identify potential investments in new drug development and research fund attribution.

Objective

Post-market adverse drug reaction surveillance is a mandatory regulatory responsibility of pharmaceutical companies. The adverse reaction data is hidden in the scientific content published in research journals.

Thus, a leading pharmacovigilance platform required labeled datasets for training AI models based on this information in order to help its clients improve their drug safety workflow processes.

Solution

• Innodata setup a team of bio-medical subject matter experts to label unstructured data; the adverse drug reactions are published across various journals and written as scientific research abstracts.
• Innodata’s medical experts annotated the abstracts from scientific journals broken into individual words with a customized labeling application.
• The SMEs studied the abstracts and labeled individual words to a controlled ontology of medical terms to create an annotated dataset for machine learning.

Results

• Innodata’s labeled datasets have helped the company build machine learning models that predict adverse drug events with high accuracy and enables pharma companies to navigate the increasingly complex and ever-changing regulatory environment.
• Today, our client can confidently offer its customers a fully compliant, configurable, easy-to-adopt and audit-ready solution for scientific literature monitoring and pharmacovigilance. 

Objective

A leading AI photography platform required annotated data for photographs. In order to teach the tool to recognize “context” from its photos, the company needed large volumes of footage with detailed labeling, and required it within 24 hours.

Solution 

• Innodata gathered a team of annotation resources, setup the infrastructure and trained the team in 4 weeks.
• An initial POC test was conducted with multiple vendors, Innodata ranked #1.

Results

• The client has an on-demand, flexible and scalable data annotation team on-call.
• Client has been able to meet the market demands effectively.
• To-date, Innodata has processed 2.5 million images in 20 weeks and delivered the batches in near real-time.