A Pre - Purchase Evaluation and Comparative Study of Solutions from A WS, Google, Azure, John Snow Labs, and Open - Source Models on an Independent Pediatric Dataset Shruti Hegde MS, Mabon Manoj Ninan BS, Jonathan R. Dillman MD, MSc, Shireen Hayatghaibi PhD, Lynn Babcock MD, Elanchezhian Somasundaram PhD Abstract Purpose: General purpose clinical natural language processing tools are increasingly used for the automatic labeling of clinical reports to support various clinical, research and quality improvement applications. However, independent performance evaluations for specific tasks, such as labeling pediatric chest radiograph reports, remain scarce. This study aims to compare four leading commercial clinical NLP systems for entity extraction and assertion detection of clinically relevant findings in pediatric chest radiog raph reports . In addition, the study evaluates two dedicated chest radiograph report labelers, CheXpert and CheXbert, to provide a comprehensive performance comparison of the systems in extracting disease labels defined by CheXpert. Methods: A total of 95,008 pediatric chest radiograph (CXR) reports were obtained from a large academic pediatric hospital for this IRB - waived study. Clinically relevant terms were extracted using four general - purpose clinical NLP systems: Amazon Comprehend Medical (AWS), Google Healthcare NLP (GC), Azure Clinical NLP (AZ), and SparkNLP (SP) from John Snow Labs. After standardization, entities and their assertion statuses (positive, negative, uncertain) from the findings and impression sec tions were analyzed using descriptive statistics, paired t - tests, and Chi - square tests . Entities from the I mpression sections were mapped to 12 disease categories plus a No Findin gs category using a regular expression algorithm. In parallel, CheXpert and CheXbert processed the same reports to extract the same 13 categories (12 disease categories and a No Findings category) . Outputs from all six models were compared using Fleiss' Kappa across the assertion categories .

We evaluate the performance of four leading solutions for de-identification of unstructured medical text - Azure Health Data Services, AWS Comprehend Medical, OpenAI GPT-4o, and John Snow Labs - on a ground truth dataset of 48 clinical documents annotated by medical experts. The analysis, conducted at both entity-level and token-level, suggests that John Snow Labs' Medical Language Models solution achieves the highest accuracy, with a 96% F1-score in protected health information (PHI) detection, outperforming Azure (91%), AWS (83%), and GPT-4o (79%). John Snow Labs is not only the only solution which achieves regulatory-grade accuracy (surpassing that of human experts) but is also the most cost-effective solution: It is over 80% cheaper compared to Azure and GPT-4o, and is the only solution not priced by token. Its fixed-cost local deployment model avoids the escalating per-request fees of cloud-based services, making it a scalable and economical choice.

Smart assistants like Amazon's Alexa and Apple's Siri recognize patterns in speech using Natural Language Processing, comprehend meaning and provide a meaningful response. Search engines surface relevant results based on the similar search behaviors. For instance, when you start typing, Google not only predicts what searches may apply to your query, but looks at the whole picture rather than the exact search words. All thanks to NLP as it associates the ambiguous query to a relative entity and provides useful results. These are not the only use cases where Natural Language Processing emerges as a game changer; there are other applications as well.

Low-code and no-code platforms are used to build applications, websites, mobile apps, forms, dashboards, data pipelines, and integrations. No-code platforms help business users, sometimes termed citizen developers, to migrate from spreadsheets, extend beyond email collaborations, and transition from manual task execution to using tools and automations across departments. Low-code platforms are usually for technologists and provide ways to deliver and support software with little or no coding. "You have to remember low code is just a fancy term for abstraction. We are abstracting away non-essential elements in order to simplify the user experience," says Gordon Allott, President and CEO of K3.

Making AI & NLP solve real-world problems in healthcare, life science and related fields. When embarking on an artificial intelligence (AI) project, there's a lot of discussion about choosing the right tool to execute on your vision. While this is an important part of the equation, it skips a crucial first step: finding the right library. But with so many tools and libraries on the market, how do decision-makers find the best fit for the job at hand? Defining the nature of your project is a good place to start. At my company, John Snow Labs, we often get compared to Allen NLP, Stanza, SciSpacy and other libraries that are focused on academic use cases.

John Snow Labs is an award-winning AI and NLP company, accelerating progress in data science by providing state-of-the-art software, data, and models. Founded in 2015, it helps healthcare and life science companies build, deploy, and operate AI products and services. John Snow Labs is the winner of the 2018 AI Solution Provider of the Year Award, the 2019 AI Platform of the Year Award, the 2019 International Data Science Foundation Technology award, and the 2020 AI Excellence Award. John Snow Labs is the developer of Spark NLP - the world's most widely used NLP library in the enterprise - and is the world's leading provider of state-of-the-art clinical NLP software, powering some of the world's largest healthcare & pharma companies. John Snow Labs is a global team of specialists, of which 33% hold a Ph.D. or M.D. and 75% hold at least a Master's degree in disciplines covering data science, medicine, software engineering, pharmacy, DevOps and SecOps.

Don't miss our upcoming virtual workshop with John Snow Labs, Improve Drug Safety with NLP, to learn more about our joint NLP solution accelerator for adverse drug event detection. The World Health Organization defines pharmacovigilance as "the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other medicine/vaccine-related problem." While all medicines and vaccines undergo rigorous testing for safety and efficacy in clinical trials, certain side effects may only emerge once these products are used by a larger and more diverse patient population, including people with other concurrent diseases. To support ongoing drug safety, biopharmaceutical manufacturers must report adverse drug events (ADEs) to regulatory agencies, such as the US Food and Drug Administration (FDA) in the United States and the European Medicines Agency (EMA) in the EU. Adverse drug reactions or events are medical problems that occur during treatment with a drug or therapy.

With evermore personal data being produced and stored by organizations, data privacy is becoming an increasing priority. Businesses have access to a lot of sensitive information about their customers, service providers, and employees and are required to protect that data in order to minimize the risks of scams or fraud. De-identification is used to overcome data privacy challenges and keep information safe from unauthorized parties. This post explains what de-identification is, how it works and how natural language processing (NLP) is used to automate the process of removing sensitive data from datasets. De-identification is a technique used to remove any data that could identify a person from a dataset.

LEWES, Del., Oct. 05, 2021 (GLOBE NEWSWIRE) -- John Snow Labs, the Healthcare AI and NLP company and developer of the Spark NLP library, today announced that it will enable free access to its enterprise-grade Annotation Lab and NLP Server software for all users. This announcement comes on the first day of the company's annual NLP Summit, a free online event that brings together the AI community to discuss the most important trends, use cases, and solutions advancing natural language processing (NLP). The Annotation Lab, a robust data labeling and AI/ML solution for teams, enables users to annotate documents, images, and videos. The software automatically trains models using active learning and transfer learning. The simple and efficient project-based workflow helps users leverage real-time analytics on productivity, dataset bias, inter-annotator agreement, and more.

Natural language processing (NLP) has been a long-standing dream of computer scientists that dates back to the days of ELIZA and even to the fundamental foundations of computing itself (Turing Test, anybody?). NLP has undergone a dramatic revolution in the past few years, with the statistical methods of the past giving way to approaches based on deep learning, or neural networks. Applying deep learning to NLP has led to massive, sophisticated, general purpose language models, like GPT-3, capable of generating text that is truly indistinguishable from human writing. GPT-3, for example, unlocks features such as those found in Microsoft's new "no-code" Power Apps platform, where you can enter a natural language description of a query, and the back end will generate the code (a Power Fx expression based on Excel syntax). NLP has vast potential across the enterprise, and it's not just the giants like Google or Microsoft that are bringing products to the table.