Harnessing AI for Pharma R&D
A leading Pharma company's R&D team sought to leverage Key Opinion Leaders (KOLs) for events, boosting drug discovery and consumer awareness through their publications.
Pharma & Biotech
Natural Language Querying
The client needed an automated system to extract English and Japanese publications across diverse databases using the same search strategies applied in English queries to identify KOLs without expert intervention.
We leveraged machine learning and natural language processing to build a unified search system, optimizing cross-language database searches. The solution seamlessly integrated with an existing search engine to extract Japanese articles from English queries, saving translation time. We used the following techniques to build the solution:
Using the Universal Medical Language System, we created word embeddings for both English and Japanese. Word embeddings represent words as unique real-valued vectors, where similar words share the same values. This sharpens the computer’s understanding of word context and relationships in text.
Nearest Neighbor Algorithm
We implemented the K-Nearest Neighbor algorithm to fetch relevant results, facilitating English queries to retrieve equivalent Japanese and closely related terms.
By integrating cutting-edge natural language processing and machine learning methodologies, Mu Sigma engineered a powerful system capable of generating an adaptive and intelligent solution, organically enhancing the embedded search strategy.
This system distinguishes itself from conventional approaches by considering multiple variables beyond simple term matching, thereby delivering the most pertinent results to queries. Furthermore, it demonstrated its preparedness for seamless expansion to encompass a diverse range of languages beyond English and Japanese.
Rise in identified Key Opinion Leaders
In building Japanese search strategies
The firm's is derived from the statistical terms "Mu" and "Sigma," which symbolize a
probability distribution's mean and standard deviation, respectively.