Unstructured Data in Healthcare with NLP Introduction In today’s digital healthcare landscape, data plays a pivotal role. However, while medical records, patient feedback, and clinical research generate vast amounts of information, not all of it is easy to manage or analyze.
Fine tuning a large language model refers to taking a model that has already been developed, and training it on more data.
Large language models and neural networks are powerful tools in natural language processing. These models let us achieve near human-level comprehension of complex documents, picking up nuance and improving efficiency across organisations.
On 29 May, Thomas Wood presented a webinar on how AI and Natural Language Processing (NLP) can transform clinical trials in the pharmaceutical industry.
Modelling risk and cost in clinical trials with NLP Fast Data Science’s Clinical Trial Risk Tool Clinical trials are a vital part of bringing new drugs to market, but planning and running them can be a complex and expensive process.
In natural language processing, we have the concept of word vector embeddings and sentence embeddings. This is a vector, typically hundreds of numbers, which represents the meaning of a word or sentence.
Some finance companies have contacted Fast Data Science with a need for a very customised named entity recognition solution. Clients prepare lists of investments which could be funds or companies, and request a check on those companies.
What is NLP in business environments? Natural language processing (NLP) is a branch of AI (Artificial Intelligence), empowering computers to not just understand but also process and generate language in the same way that humans do.
Natural language processing (NLP) is no longer a term that makes people shake their heads and go “huh…?” – on the contrary, most businesses are aware of what it is and the powerful applications it offers through its variety of natural language APIs, text analysis APIs, and text processing APIs.
Can we detect what is fake news or plagiarised in 59 articles for Der Spiegel by Claas Relotius? We used natural language processing to uncover the clues that pointed to a rogue journalist’s history of submitting fake news
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