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TCS ADD™
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TCS ADD™: A Suite of Modern and Open Technology Platforms
TCS ADD™ is a suite of modern and open technology platforms for clinical research and drug development that helps make clinical trials more agile and safe.
Related Insights
Join us at the life sciences event to learn how AI and generative AI can accelerate next-gen clinical R&D and enhance patient safety. Register now for the event.
Events
07 Aug 2024
TCS’ End-To-End Offering for Decentralized Trials, TCS ADD™ Connected Clinical Trials Cited as a Key Strength
Press Releases
25 Oct 2022
Tata Consultancy Services announced that the TCS ADD™ Connected Clinical Trials platform for decentralized trials, has won the India Pharma Award 2022 in the category, Excellence in Ancillary Pharma Services.
Press Releases
15 Dec 2022
Redefining CT decision-making
Leveraging NLP for extracting insights from unstructured data
In clinical trial execution, numerous feedback, comments, reviews, physician notes, and other textual data are captured. These free texts, though captured across multiple systems, are rarely picked up for signal processing and subsequent management, generating meaningful insights, inferencing, managing risks, compliance, etc.
Leveraging next-gen technologies in data ingestion and extraction can enable systems to analyze such free texts. Natural language processing (NLP) has progressed to the point where it can’t just classify the text into categories but also facilitates highlighting the essence of the message or intent conveyed via given text. This information can then be used in tandem with statistical and deep learning models for further classification and to generate actionable insights and any oversight.
The gestation period of clinical trials is very long, and the data that is generated through the entire cycle is continuous and can be obtained during various phases of the trial. This data is progressive over the entire duration of a clinical trial and hence, utilizing this data can lead to some critical insights pertaining to certain trends and patterns mapped to the trial with respect to risks, compliance, or safety aspects. This, in turn, can enable quicker and data-driven decision making.
Free texts can be categorized, ranked, or prioritized, grouped, or clustered into similar texts and extracted, all by leveraging certain NLP and deep learning algorithms.
Risk Based Quality Management (RBQM) is a system for managing quality throughout a clinical trial in tandem with risk based monitoring. The RBQM system receives input from multiple diverse source systems, analyses those inputs, and monitors and forecasts risk mapped to a specific site. Traditionally, structured text is utilized for reporting, analysis, and gaining insights. Unstructured sections and fields in documents or data sources are hardly ever processed because of the intricacies involved.
With the current RBQM systems, handling unstructured data, which involves large volumes of data available in siloes, is limited. This is a source of hidden risks. Traditional RBQM systems do not pick up unseen signals as they are expensive and resource intensive. Identifying these risks will require cognitive intelligence or intensive human efforts.
NLP techniques can be used in RBQM to parse through unstructured data and analyze it. Along with AI/ML, this can also be used for predictive analysis. The input obtained from various data sources is passed through the NLP extraction engine. This engine extracts textual data from documents and creates an intermediate spreadsheet-like data format of the text data.
Learning and deriving insights from historical data can help facilitate data-driven decision-making in multiple facets of clinical trials, including study setup, risk assessment, and signal management.
Uncover the full white paper here.
About the authors
Image of Saurabh Das
Saurabh Das
Saurabh Das is the product head and project manager at TCS ADD™ and heads TCS ADD™ Research and Innovation. He has over 17 years of experience in product development and managing large program executions. He has multiple patents and publications in reputable journals. He's been a regular speaker at SCDM, SCOPE, and PharmaSUG.
Image of Sushilkumar Singh
Sushilkumar Singh
Over 18 years of experience in the Clinical Operations Domain and Risk Based Quality Management, with over five years in clinical software development and services. Provided advisory and strategic support in building the first in the Industry RBQM solution utilizing predictive analytics and ML for workload prediction and proactive Risk Based Study Management.
Image of Niketan Panchal
Niketan Panchal
Niketan Panchal is an experienced Data Scientist with four years of expertise in developing cutting‐edge machine learning and deep learning solutions, particularly specializing in Generative AI. Proficient in Python programming and adept with frameworks including TensorFlow, Torch, Keras, Transformers, Langchain, Django, and Flask. Demonstrated success in implementing advanced Natural Language Processing (NLP), Natural Language Generation (NLG), and machine learning solutions tailored to the unique challenges of the Life Sciences domain. Passionate about leveraging technology to drive innovation and advance research initiatives in Life Sciences.
Associate Authors:
Rajasekhar Gadde
Researcher, TCS ADD™ Platform Research & Innovation, TCS
Rajasekhar Gadde is an experienced data scientist and researcher specializing in Generative AI for the life sciences domain. With expertise in AI-driven automation, data science, and AR/VR applications, he is passionate about transforming clinical research through innovative technologies that enhance trial analytics, regulatory compliance, and scientific decision-making.
Email ID: rajasekhar.gadde@tcs.com
Rohit Kadam
Researcher, TCS ADD™, TCS
Rohit Kadam is a researcher with over four years of experience in the TCS ADD™ Life Sciences Platform. He specializes in AI-driven research and innovation, developing cutting-edge solutions for the life science and healthcare domains. With expertise in AI/ML, generative AI, and data visualization, he has contributed to multiple patents and publications. Using artificial intelligence and other cutting-edge technologies, Rohit has actively collaborated on innovative technology solutions, innovation bootcamps, and AI integration in the life sciences domain.
Email ID: kadam.rohit1@tcs.com
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