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Apollo cloud windows 75/18/2023 ![]() ![]() Map medical concepts to standard medical vocabularies such as RxNorm, ICD-10, MeSH, and SNOMED CT (US users only) ĭerive medical insights from text and integrate them with data analytics products in Google Cloud. The process includes:Įxtract information about medical concepts like diseases, medications, medical devices, procedures, and their clinically relevant attributes ![]() Using this, we parsed unstructured medical text and then generated a structured data representation of the medical knowledge entities stored in the data for downstream analysis and automation. Healthcare Natural Language API: This is a no-code approach that provides machine learning solutions for deriving insights from medical text. Doctors annotated them to label the entities and offset values.Įxperimentation and choosing the right approach - Four models put to testįor entity extraction, both Google Cloud products and open-source approaches were explored. This dataset was primarily used for training and validation of the models.Īpollo 24|7’s Dataset - De-identified doctor’s notes from Apollo24|7 were used for testing. I2b2 Dataset - i2b2 is an open-source clinical data warehousing and analytics research platform that provides annotated deidentified patient discharge summaries made available to the community for research purposes. To perform our experiments on entity extraction, we used two types of datasets. Let’s take a sneak peek at Apollo 24|7’s entity extraction solutions, and the various Google AI technologies that were tested to form the technology stack. These entities can then be used to build a recommendation engine that would help doctors with the “Next Best Action” recommendation for medicines, lab tests, etc. We helped them to parse the discharge summaries and prescriptions to extract the medical entities. ![]() Along with entity extraction, the other key components of the CDSS system are capturing the temporal relationships, subjects, and certainty assessments.Īt Google Cloud, we know how critical it is for the healthcare industry to build CDSS systems, so we worked with Apollo 24|7, the largest multi-channel digital healthcare platform in India, to build the key blocks of their CDSS solution. The market size for the global clinical decision support system appears poised for expansion, with one study predicting a compound annual growth rate (CAGR) of 10.4%, from 2022 to 2030, to $10.7 billion.įor any health organization that wants to build a CDSS system, one key block is to locate and extract the medical entities that are present in the clinical notes, medical journals, discharge summaries, etc. Clinical Decision Support System (CDSS) is an important technology for the healthcare industry that analyzes data to help healthcare professionals make decisions related to patient care. ![]()
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