The Top Applications of NLP In Pharma and Medicine

NLP in pharma is at the tip of the iceberg. with an evolving application of it, the pharma industry leveraging the growth of online data has opened up new opportunities for the pharma industry to better understand the health risks, market opportunities, clinical effectiveness, etc. AI-driven technologies like Machine Learning and Natural Language Processing (NLP) are enabling researchers in the pharma industry to rapidly identify and extract opportunities from multiple sources. 

Real-world data sources like EHRs, patient forums, etc contain unstructured data making it difficult and time-consuming to glean actionable insights from the data. AI-powered NLP technology eases this problem. Pharma companies are leveraging NLP in drug discovery, text mining EHR data, and harnessing data to drive future insights for commercial advantages, providing actionable insights in providing better care and efficacy. 

What encouraged pharma companies to tie up with AI-based startups or develop an internal AI system? Atomwise, an AI-based startup, generated USD 4 billion after implementing internal AI systems that function on NLP to conquer the problems in drug discovery, identifying drug targets, and re-proposing drugs.

NLP In Pharma: Applications

The following are a few applications that detail how pharmaceutical companies can leverage NLP to mine unstructured data and gain insights.

• Drug discovery

NLP is used in drug diagnosis, drug identification, and discovery, which helps in developing more customized drug development. The pharmaceuticals have large volumes of unstructured data sets that have unrecognized hidden patterns of data and provide information regarding various diseases, and functional genomes that help in discovering a new drug or re-proposing a drug. For instance, clinithink in partnership with Rady children’s institute of genomic medicine has discovered a drug for a rare genetic disease, using machine learning algorithms and clinical NLP data.

Novo Nordisk uses NLP-based text mining tools to harness the data to extract information regarding the safety and efficacy of the drug, pharmacodynamics, desultory usage of drugs, controlled trials, and dosing percentages. This help Novo Nordisk better understand the concerns of patients and providers.

• Enhanced medical experience

Unanswered patients’ queries are the biggest problem during the diagnosis process. If the patient is not aware of a particular disease, the diagnosis and curing processes will take a long time and it may affect the patient’s health. As prevention is better than cure, the description of different stages of the disease, information regarding the right diagnosis, and preventive drugs may help in a better and quick cure.

Clinicspots Indian based mid-sized health startup has introduced an innovative medical question and answer system that functions on NLP. The system tries to understand and process patient/user queries related to medical diagnosis and medicine description. The system provides information to the end-user in three different forms- a direct answer to the question, a list of similar questions and answers, and a list of links to related questions.

Based on NLP-supervised systems, clinicspots can perform language detection to understand client intent and conduct sentiment analysis. That helps pharma and healthcare to understand client trends, inventory requirements, and the advanced medical diagnosis required.

• Mining EHR (Electronic Health Record) data with NLP

Previously, to protect the patient’s privacy, the hospitals didn’t provide access to biotech and pharma companies to read EHR data. But with the increase in electronic records, they provide access to unidentified and unstructured data formats of the patients. With the use of NLP technologies, drug companies can decode and understand the medical transcripts and patient records, which help in providing high value to discontinuous studies mentioned earlier.

• Harnessing the real-world healthcare data

For biotech and pharma companies, effective mining and harnessing of real-world data will provide more value in discovering and enhancing the development and post-market delivery of drug therapies. NLP technologies using language detection and sentiment analysis tools can provide information. This helps in a better understanding of disease states and treatment patterns across broad populations, which supports product development and commercial decision-making.

Employing this information, the pharma and biotech companies can understand treatment effectiveness that provides insights into the patterns of care, the long-term safety of the drug, health resource utilization, and disease epidemiology.

• Finding consumer insights to drive commercial advantages

The researchers in the pharmaceutical company have developed a workflow model to process the unstructured data collected through call transcripts. By using advanced analytical tools and NLP technologies, the datasets can be harnessed to create visual output to trends and build predictive models. which further helps in extracting information regarding registered complaints, requests about the side effects of drugs, the aftereffect of drug-to-drug interactions, etc.

• Future insights 

NLP helps in improving phenotype extraction of precision medicine. Precision medicine focuses on better treatment and for that, the patient’s medical history on genes, hereditary, lifestyle, living environment, etc., is required. 

An ongoing project at the University of Iowa has the potential to aid in interpreting the genetic test results. The researchers at the university are using NLP technology, to obtain high-quality comprehensive phenotype information of the patients who have undergone clinical genome testing.

The pharma companies are leveraging advanced applications of AI, Deep Learning networks, and NLP  for a 360-degree development of the business. These technologies are helping Pharma industries in discovering new routes to understand and analyze their consumers’ and suppliers’ insights for better drug experience, care, and efficacy. 

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