Solve Big Data Problems in Pharmaceutics & Bio-tech
Analyze large volumes of text data including research and support data with ease. Improve communication and increase clinical trial efficiency.
Analyze large volumes of text data including research and support data with ease. Improve communication and increase clinical trial efficiency.
The pharmaceutical and biotechnology professionals use terms and phrases native only to their industry. Advanced machine learning based text analytics solutions can process these unique terms and phrases. Organize research, clinical trial, examiner notes with ease and extract actionable insights.
BytesView’s text mining solutions is designed to process and text data related to the pharmaceutical and bio-tech industry. Extract text data from clinical surveys and trials, research papers, patient reports, and more ease. Accelerate research with increased trial efficiency and communication with the help of text analytics.
Use topic labelling to identify symptoms in patients. Predict after effects of taking a particular drug without actually taking the drug.
Analyze reviews and support data to analyze the intent of the users. Extract complaints registered complaints and identify recurring cases of side-effects.
Identifying the gender of the patient is important to infer a diagnosis. Drugs may act differently on users from different gender groups.
Managing guidelines from regulatory agencies and clinical trials is difficult. Deploy semantic solutions to navigate through complex unstructured text.
Analyze the sentiments of the conversations revolving around your product and services. It can be great tool for developing a digital strategy.
Analyze social media conversations to reviews related to your products and understand the emotions expressed by your customers. Use the insights to identify key issues.
Feature extraction to summarize the patients entire history. Extract data such as symptoms, reports, diagnosis, drugs, etc. Navigate through research data with ease.
Use keyword extraction to identify and extract relevant information or documents from large volumes of unstructured textual data.
Identify and extract documents related to a clinical trial reports, examiner notes, research papers, medical terms etc. Extract named entities such as patients, diagnosis, drugs, and more.