Pharma
Biotech

Pharmaceutical companies are increasingly recognizing that patients’ social media posts are a valuable source of insight into patient-reported outcomes, views, symptoms, use of competitive products and more. Analyzing social media posts has the potential to provide insights from a broad population of patients, healthcare professionals, and key opinion leaders.

The standard methodology to gather these insights is primary market research. Pharma companies work hard to establish and maintain focus groups for live interviews, questionnaires and other ways to gather insights from patients and providers. However, the research process is time-consuming and expensive, requiring pharma companies to invest a significant amount of resources; and the number of patients that can be targeted is small.

Analysis of social media has the potential to be more efficient, more cost-effective, and address much larger patient populations than primary market research. The problem in analyzing social media content, however, is that social platforms represent a constantly flowing firehose of noisy information, so separating the signal from the noise poses a significant challenge.

Text mining with Natural Language Processing (NLP) technology represents an alternative approach to unlocking the value of social media content. By enabling artificial intelligence-based NLP tools to gather and analyze social data, pharmaceutical companies can more swiftly and efficiently glean insights that will influence the drug development process.

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Pharma
Biotech

We are pleased to announce that Linguamatics, an IQVIA company, and the leading Natural Language Processing (NLP) text analytics provider, has joined Accenture’s open partner ecosystem which is designed to help independent software vendors (ISVs) and life sciences companies team more effectively to accelerate drug discovery efforts and improve patient outcomes.

The INTIENT Network for Research is an integral part of Accenture’s cloud-based informatics research platform, which has been designed to help life sciences organizations improve productivity, efficiency and innovation in drug discovery. Accenture is currently working with a select number of ISVs and organizations—including Linguamatics— to integrate their technology and content into Accenture’s research platform. This will allow life sciences companies to give their researchers access to innovative capabilities, such as Linguamatics’ innovative Natural Language Processing-based AI for high-value knowledge discovery and decision support from text. The Linguamatics award-winning platform is proven across multiple real-world use cases to deliver actionable insights that address pressing bench-to-bedside challenges with quantifiable ROI.

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Pharma
Biotech

Using Natural Language Processing (NLP), Agios Pharmaceuticals discovers new therapeutic candidate

As the search for novel anti-cancer agents continues apace, the biopharma industry struggles to make sense of the myriad studies that are published describing putative small molecule inhibitors, potential genetic targets, and possibly susceptible points of attack. One area of growing interest in oncology is cancer cell metabolism: companies are striving to develop compounds that can interrupt or inhibit the metabolic process, leading to tumor cell suppression or death. The dual challenges are to identify promising lead compounds, and to detect suitable genes implicated in the metabolic process and sensitive to chemical intervention.

Rather than starting from scratch with a blank structure-activity canvas and pursuing the traditional (and potentially lengthy and risky) lead identification/lead optimization route to a pre-clinical candidate, Agios Pharmaceuticals decided to short-circuit the process and build on previously published studies. They wanted to locate and source known inhibitors for use as tool compounds in their chemical genetics screens and to identify genes with “druggable Achilles’ heels” susceptible to chemical attack, and they chose to use NLP to quickly and effectively scour the literature.

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Pharma
Biotech
Government
Agrochem
FMCG

We are proud to announce that the IQVIA Linguamatics Natural Language Processing (NLP) platform was recently awarded Questex’s 2019 Fierce Innovation Award – Life Sciences Edition in the Data Analytics/Business Intelligence category. In addition, the NLP healthcare platform was recognized with a Best in Show honor for ‘Best Technology Innovation’.

Sponsored by the publisher of FierceBiotech and FiercePharma, the Fierce Innovation Awards identify and showcase outstanding innovation that is driving improvements and transforming the healthcare industry. An expert panel of judges reviews all submissions to determine which companies demonstrate innovative solutions, technologies and services that have the potential to make the greatest impact for biotech and pharma companies.

The IQVIA Linguamatics NLP platform supports life sciences organizations seeking to speed up drug development and improve patient outcomes by breaking down data silos, boosting innovation, enhancing quality and reducing risk and complexity. NLP is an artificial intelligence technology that transforms unstructured and semi-structured text into normalized, structured data suitable for analysis or to drive machine learning algorithms.

The platform uses sophisticated algorithms to identify, extract and connect key concepts, facts and relationships buried in the text rather than just retrieve documents based on keyword search. Key solution areas include:

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Academic Medical Center
Payer
Provider

Better diagnosis needs more than diagnosis codes

It’s well known that cardiovascular diseases are one of the major causes of death both in the US and globally. This level of disease puts great pressures on health systems to manage the patient load, both at the population level and at the individual level. As with all diseases, treatment is more effective and less costly if patients can be diagnosed earlier on their care journey. One barrier here is that diagnosis codes for conditions such as valvular heart disease can be inaccurate and vary across health systems. More information resides in the unstructured text of medical records but this is slow and tedious to extract manually.

Fast accurate diagnosis of aortic stenosis with Natural Language Processing

A recent short paper by Solomon et al from Kaiser Permanente Northern California (KPNC) used Natural Language Processing (NLP) algorithms to extract detailed clinical information from echocardiography (ECG) reports. NLP is an Artificial Intelligence (AI) technology used to transform free, unstructured text in documents and databases into normalized, structured data suitable for analysis. Their results were more accurate than using diagnosis codes to identify aortic stenosis, for a patient cohort of over 500,000 individuals.

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