Nlpsummit.org
Healthcare 2023 Program
Web3:45 pm ET – 4:15 pm ET. Join the world’s largest applied NLP community at the virtual Healthcare NLP Summit 2023! Two days of immersive, industry-focused content including over 30 technical sessions, focusing on NLP applications in …
Actived: Just Now
Collaborative Healthcare NLP: Customisable NLP platforms for …
WebFor many years, the NLP healthcare application development are driven by NLP engineers. The engineers-centred healthcare NLP unintentionally creates barriers among healthcare workers and slows the deployment of NLP in health and related research. These barriers include 1) barrier to access healthcare NLP applications； 2) barrier to applying custom …
AI and NLP Carving a Future to Save Healthcare Simple and …
WebAdvancements in Artificial Intelligence (AI) and Natural Language Processing (NLP) present transformative opportunities for enhancing healthcare. AI-driven technologies can streamline administrative tasks, optimizing operational efficiency and reducing costs. NLP facilitates effective communication between healthcare professionals and systems, improving …
Using NLP for Diagnosis of Mental Health Disorders
WebMental Health Disorders. Natural language processing (NLP) tools have shown promise for automatically diagnosing mental-health disorders by analyzing written texts and speech transcriptions. In this session, we will survey some of the latest developments in the field, with specific examples of how computational approaches can …
A Real-time NLP-based Clinical Decision Support Platform for …
WebA Real-time NLP-based Clinical Decision Support Platform for Psychiatry and Oncology. A modern health care system generates a wide range of clinical data formats including structured, unstructured, image, and high-frequency waveform data. The volume of data is also growing quickly, enabling more personalized data-driven clinical interventions.
Developing Guidelines for Responsible Generative AI in Healthcare
WebThe Coalition for Health AI (CHAI) is a non-profit tasked with developing concrete guidelines and criteria for responsibly developing and deploying AI applications in healthcare. This session introduces CHAI, how it works with the US government and across the healthcare community, what it has built that you can use today, and how you can get involved.
AI in Healthcare – Promise and Perils
WebV. “Juggy” Jagannathan, Ph.D., researches artificial intelligence and computer science for 3M Health Information Systems, developing new natural language and deep learning technologies that automatically structure patient-physician conversations into clinical documents.
RAG on FHIR: Using FHIR with Generative AI to make healthcare …
WebGenerative AI / LLMs have amazing potential to revolutionize many aspects of life. However, they have many flaws, including the limitations imposed by the data they are trained on. These limitations are especially important in healthcare; people are understandably reluctant to allow their health data to be used in Machine Learning (ML). Retrieval-augmented …
Back to the future: 1854, London
WebIn this session, Thomas takes the audience on a journey back to London in 1854 and shows how John Snow, the father of modern epidemiology, would fight cholera with present-day technology. The aim is to show how the use of NLP, LLMs and GenAI could have changed the approach and efficiency of John Snow work, but also how this will revolutionize the …
Patient Experience and Value-Based Care
WebAltais is focused on helping physicians and the clinical community maximize the health and well-being of their patients. In his role at Altais, Ram is responsible for creating tools, technology, and services that help enhance the patient experience, improve health outcomes, lower the cost of care, and deliver a sense of professional gratification …
EHR Question Answering
WebEHR Question Answering. The last decade has seen widespread adoption of electronic health records (EHRs) across hospitals and clinics in the US. Physicians frequently seek answers to questions from a patient’s EHR to support clinical decision-making. It is not too hard to imagine a future where a physician interacts with an EHR …
Using Large Language Models as Clinical Decision Support tools in …
WebLarge language models have the greatest potential to improve the quality of care in settings where the baseline quality of care is low, healthcare providers have more limited training, and the scope of practice is broad. At Penda Health, in Nairobi, Kenya, non-physician healthcare providers (Clinical Officers) provide urgent care and primary care services to …
Extracting Disease Severity Measures from Unstructured Clinical …
WebWhile disease severity dictates how virtually all diseases are managed and treated, disease-specific severity measures are seldom available in structured clinical data. Therefore, an application that could extract such severity measures from clinical notes would be valuable to understand disease activity in patients and subsequent disease management.
Empowering Healthcare through NLP: Harnessing Clinical …
WebClinical documents play a pivotal role in patient care, research, and Health Information Management (HIM). At Intermountain Health, we present a comprehensive approach to unlocking valuable insights from these documents through Natural Language Processing (NLP) on the Databricks Lakehouse Platform. Our strategy involves leveraging advanced …
Harnessing Causality, Encoded Clinical Knowledge, and …
WebClinicians, confronted with a multitude of complex and pivotal decisions throughout a patient's healthcare journey, often rely on population-level averages, intuition, and incomplete patient records. Meanwhile, the electronic health record (EHR) is a rich repository of unnormalized clinical data, documentation, and patient attributes both …
“Be Like Water” – NLP and GenAI Opportunities and Challenges in
WebA complex field like healthcare provides many opportunities for machine learning and generative AI applications. In practical terms, this often involves the art of building bridges between the possible and the feasible - while technical tools exist that make fancy applications possible, only a subset of these are feasible at a non-profit healthcare center …
Improving Precancerous Case Characterization via Transformer …
WebThe application of natural language processing (NLP) to cancer pathology reports has been focused on detecting cancer cases, largely ignoring precancerous cases. Improving the characterization of precancerous adenomas assists in developing diagnostic tests for early cancer detection and prevention, especially for colorectal cancer (CRC). Here we …
Measuring and Mitigating Bias in Healthcare Language Models
WebAlgorithms have intrinsic biases which can create systematic errors in real-world applications. These errors often yield unintended outcomes that can degrade performance, reliability, and trust. In this talk, Gaurav Kaushik, PhD (Founder, ScienceIO) will provide an overview of methodologies to measure and mitigate bias to improve the performance and …
Building a Better Patient Chart: Combining structured, …
WebDiameter Health has deep expertise in doing structural and semantic normalization on structured medical data. However, this is only part of the picture, since an Electronic Medical Record (EMR) is made up of three components: structured / computer-readable data, narrative / human-readable data, and missing data.
Healthcare NLP Summit 2021
WebApplied Natural Language Processing in Healthcare. The NLP Summit is the gathering place for those putting state-of-the-art natural language processing to good use. This inaugural virtual conference showcases NLP best practices, real-world case studies, challenges in applying deep learning & transfer learning in practice – and the latest open
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