COVID-19 has a created an inflection point that is accelerating the use of AI in healthcare. More data was created in the last two years than in the previous 5,000 years of humanity. Alongside this trend, we see an acceleration of decision support applications that are based on extracting clinical insights and analytics from data. AI and Machine Learning play an important role in our ability to understand big data and learn from it. In the journey to transform healthcare, NLP technology for health & life sciences has great potential, as we recently discussed in this blog.
Today we are announcing Text Analytics for Health as generally available with Text Analytics in Azure Cognitive Services. The service allows developers to process and extract insights from unstructured biomedical text, including various types of clinical notes, medical publications, electronic health records, clinical trial protocols, and more, expediting the ability to learn from this data and leverage it for secondary use.
Identifying medical concepts in text, determining boundaries and classification into domain-specific entities. Concepts include Diagnosis, Symptoms, Examination, Medications, and more. Recent additions to the GA service include expanding the Genomics category to enable extracting mutation types and expression in addition to identifying genes and variants. The current version of the service we are releasing as generally available contains 31 different entity types, and we will be increasing this in the future.
Associating medical entities with common ontology concepts from standard clinical coding systems, such as UMLS, SNOMED-CT, ICD9 and 10 etc.
Identifying and extracting semantic relationships and dependencies between different entities to provide deeper understanding of the text, like Dosage of Medication or Variant or Gene. Recent additions made to the service toward its general availability include expanding the types of relationships, and the service now supports 35 different types.
Assertion detection, to support better understanding of the context in which the entity appears in the text. The Assertions help you detect whether an entity appears in negated form, as possible, likely, unlikely (for example, “patients with possible NHL”); whether the mention is conditional, or mentioned in a hypothetical way (for example, “if patient has rashes (hypothetical), prescribe Solumedrol (conditional)", or whether something is mentioned in the context of someone else (for example, "patient's mother had history of breast cancer" does not mean the patient has breast cancer).
The service can be used synchronously and asynchronously and is available in most Azure regions, currently in English. The service can be used via a hosted endpoint or by downloading a container, to meet your specific security and data governance requirements. Either way, the service does not store the data it processes and is covered under the Azure compliance .
During the last year, the service was available under a gated preview program. With today’s announcement on general availability, we are removing the gating off the service.