NLP (Natural Language Processing) Technology Application in Pharma Industry

FREMONT, CA: NLP, subfield of artificial intelligence is concerned with the interaction between computers and human language, in particular how to process and analyze large amounts of language data. It holds the potential to deliver tremendous value for drug safety by automating text mining, enabling pharmaceutical companies to uncover valuable information hidden among troves of unstructured data. NLP holds particular relevance for healthcare and pharma, both of which are ambiguous in unstructured data. 

In knowledge driven industries such as life sciences and healthcare, finding the right information quickly from huge volumes of text is crucial in supporting the best business decisions. However, around 80 percent of available information exists as unstructured texts. These unstructured data can be transformed into structured data with the help of text mining process. Text mining is an artificial intelligence technology that uses NLP to transform the unstructured text in documents into normalized, structured data which facilitates the analysis of unlimited amounts of text-based data without fatigue in a consistent, unbiased manner.

NLP technology automates text mining and can understand concepts within complex contexts and decipher ambiguities of language to extract key facts and relationships, or provide summaries. Given the huge quantity of unstructured data that is produced everyday, from electronic health records (EHR), this automation has become critical in analyzing text-based data efficiently. When applied to EHRs, clinical trial records can extract the clean structured data needed to drive advanced predictive models used in machine learning, thereby reducing the need for expensive, manual annotation of training data.

NLP helps to interpret the handwritten information recorded by physicians and nurses. it can draw conclusions and interpret abbreviations and even misspellings.

With the help of NLP, physicians no longer need to spend a lot of time reviewing and analyzing medical images like x-ray, CAT, MRI etc. ML algorithms can learn from archived images to recognise patterns and can accurately interpret results of radiological scans within a short span of time.