The evolution of artificial intelligence (AI) has taken the healthcare sector by a storm promising enhanced data analytics for effective decision making. However, most of the AI tools have failed to deliver practical solutions in terms of data analytics particularly in healthcare, where decoding the value of data is becoming ever important. Lawrence Ganti, CEO Lifesciences of Innoplexus, has observed, “In order to make a lasting impact in the healthcare or life sciences industry, AI tools need to speak and understand the language of life sciences.” Most often, these tools make use of natural language processing (NLP) technology to process natural language and hence cannot understand very specific scientific level language thereby fail to process the healthcare data. Simply investing into algorithms and computing power is not adequate when it comes to the life science industry; it is essential for AI tools to focus on the relevance of data and the ability to extract relevant data, understand it and then apply computing power and algorithms on top of it. Understanding this need, Innoplexus has focused on building a life science language processing engine that incorporates significant and sophisticated biomedical ontology to understand the clinical language, aggregate and then process it. “Our engines have a Ph.D. level understanding of the life sciences terminologies and are accurate enough to extract the right data that can further be used for analytics leading to efficient decision making.”
Innoplexus offers a proprietary analytical framework, CAAV for AI and data analytics for healthcare. The company first crawls all the information regarding life sciences from the web—capturing data from more than 327 million web domains and 50,000 data feeds. After crawling a considerable amount of data, Innoplexus works on converting that data into understandable information where the company’s biomedical ontology language processing comes to play. Here, the relevant data is extracted, analyzed and visualized in the form of insights. “We use AI and machine learning across the entire framework right from the data crawling, aggregation, analytics to the visualization part,” says Ganti. Alongside its proprietary framework, the company also offers syndicated products such as Ontosight® and Key Opinion Leaders discovery and management platform instead that generate continuous intelligence across all phases of drug development and supports key opinion leader discovery and management respectively.
“We provide a custom developed cloud-based ecosystem that is deployed within the firewalls of our clients that gives them the flexibility to integrate a number of different data sources.”
Innoplexus has been instrumental in speeding up the process of drug development by building up the core competencies for life science and healthcare companies. In one instance, a client had a large number of patients dropping off from a chronic therapy and required predictive analytics to identify when a patient was about to withdraw from the medication. With Innoplexus, the client was able to integrate predictive analytics with their existing data to determine patient dropouts. “By deploying our solution, the client was able to take a proactive approach to reach out to patients and reduce the number of patient dropouts by well over 50 percent,” says Ganti.
He believes that educating the client is an important part in any client engagement, which is why the company spends a considerable amount of time in explaining the fundamentals of AI and its applications in the life sciences and healthcare industry. With a forward-looking mindset, the company has over 60 patent filings for its technology which in itself is a significant milestone. Besides, Innoplexus has successfully made a mark in India, Germany and the U.S. With that, the company aims to expand into the west coast, Latin America, and Europe.
Originally published by Cio Review.
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