BASE / Content Hub / NLP: The future of data management in life sciences

NLP: The future of data management in life sciences

 

Emails

Conversations between researchers, clinicians, or various departments often contain valuable but informal insights that are not readily quantifiable.

Research papers

These documents can be complex and are not always straightforward to categorize, especially for those who are not experts in the field.

Social media posts

These platforms offer a wealth of public opinions and insights but are often overlooked as valuable data sources.

Patient records

These documents often contain both structured and unstructured data and raise concerns about compliance and data protection, especially in the era of GDPR.

Dossiers and templates

Regulatory bodies might recommend specific structures, but each company can adopt its own lexicon. These documents encapsulate the expertise and processes of entire organizations and could be gold mines for big data.

Why information extraction matters

The extraction of data from these unstructured documents is not just a technical challenge; it’s a necessity for several reasons:

Accelerated discoveries

Mining data from archived documents can expedite research and development processes, enhancing productivity without overstretching resources.

Comprehensive analysis

Machines are particularly skilled at analysing large sets of data and identifying correlations within them, something that would take humans significantly more time.

Scalability

NLP tools offer the ability to process large volumes of data in a time-efficient manner, making them invaluable for large-scale projects and long-term data management.

Demystifying NLP

Natural Language Processing (NLP) is a dynamic field that has undergone remarkable transformations since its inception. It serves as a bridge between human language and machine comprehension, aiming to teach computers to understand, interpret, and generate human language in a way that is both meaningful and useful. Initially, the field was dominated by rule-based methods, which were rigid and limited in scope. However, the shift towards statistical models in the late 20th century opened new avenues for understanding language patterns.

The real game-changer came with the advent of machine learning algorithms in the 2000s. These algorithms could learn from data, improving their performance as they were exposed to more and more examples. This led to the development of more sophisticated NLP applications, from chatbots to translation services. More recently, deep learning techniques have pushed the boundaries even further. Architectures like OpenAI’s GPT-3 and Google’s BERT have set new standards for what machines can understand and generate, achieving performances that are sometimes indistinguishable from human capabilities, as claimed in this article.

Interesting facts

The Turing test

Proposed by Alan Turing in 1950, this test measures a machine’s ability to exhibit human-like intelligence, as explained by the Stanford Encyclopedia of Philosophy.

Data-driven

The advancement of NLP owes much to the availability of extensive online data, which has been a game-changer in training algorithms. For example, Google’s BERT was trained on BooksCorpus (800 million words) and the English Wikipedia (2,500 million words).

Multilingual models

Recent developments have produced NLP models capable of understanding multiple languages, sometimes without explicit training, as discussed this study from researchers at Cornell University.

Leveraging NLP in life sciences

In the life sciences industry, NLP can offer various benefits:

Contextual analysis

NLP can discern between different medical conditions described in a patient's narrative, such as a common cold and an allergic reaction.

Sentiment detection

In drug trials, participant feedback is vital. NLP can sort this feedback into positive, neutral, or negative sentiments, providing a more nuanced understanding of trial results.

Relationship extraction

NLP can identify overlaps in research, revealing potential interdisciplinary insights that could lead to groundbreaking discoveries.

Regulatory task enhancement

NLP and Generative AI advancements can streamline data extraction, making it easier to comply with various regulations and guidelines.

Summarization and text generation

By leveraging LLMs and NLP, we can accelerate text summarization, efficiently extract pertinent information from extensive documents, and even generate new content.

Conclusion

Natural Language Processing (NLP) stands at the forefront of AI research and has the potential to redefine the future of human-machine interactions. Its application in the life sciences sector can unlock a wealth of often-underutilized data, thereby refining research and development processes and leading to more effective and efficient outcomes.

Get in touch

If you’re as captivated by the transformative potential of life sciences as we are, why not reach out? We’re always eager to discuss how we can collaboratively harness the power of innovation to achieve your goals. 

Author

Manfredi Miraula

Senior Data Engineer

Content Hub

Related content

Thank you for downloading our whitepapers

We have sent a download link to your mailbox