AI for Clinical Trials
Clinical Trials data traditionally analyzed using Natural Language Processing methods. Now the genAI apps are used to generate summaries. Both approaches are missing a key thing : Broad visibility from all trials happening at all levels. Company wide, specific domain wide, industry wide visibility will speed up, bring new methods and saves costs.
NaturalText's way of analysing Clinical Trials
Clinical trials data is the example of mixed up data. Data is stored in nested structured usually XML format, in those structures, long form text and numbers are present to convey the meaning. Any method of analysing this faces a problem because those methods can handle only one form of data.
genAI apps cant handle it because of relational data structures. Each piece of information is related to other nested structures which makes it difficult for the genAI to make sense.
NaturalText offers Symbolic Zero-Shot AI for clustering trials data, ranking the popular trials based on molecules, inclusion criteria, exclusion criteria and so on. It creates a network map of both connected information within a single trial and between trial documents.
Extracts molecule names, product names etc to form a list. Companies can choose advanced options of building network graph or knowledge graph and ontologies.
Offering a better search experience. If the companies wanted to build a search engine based on the text data, NaturalText search ranking method can build the indexes, use connected relationships as ranking score and contextual search to bring relevant and consistent results.
Understanding Context. NaturalText understands context from textual data easily and without any labelling, pre-training or specialised fine-tuning.
Change the way to understand what is going on. Connect to info@naturaltext.com
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