{"id":1333,"date":"2021-12-09T06:10:14","date_gmt":"2021-12-09T06:10:14","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2021\/12\/09\/clinical-text-mining-using-the-amazon-comprehend-medical-new-snomed-ct-api\/"},"modified":"2021-12-09T06:10:14","modified_gmt":"2021-12-09T06:10:14","slug":"clinical-text-mining-using-the-amazon-comprehend-medical-new-snomed-ct-api","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2021\/12\/09\/clinical-text-mining-using-the-amazon-comprehend-medical-new-snomed-ct-api\/","title":{"rendered":"Clinical text mining using the Amazon Comprehend Medical new SNOMED CT API"},"content":{"rendered":"
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Mining medical concepts from written clinical text, such as patient encounters, plays an important role in clinical analytics and decision-making applications, such as population analytics for providers, pre-authorization for payers, and adverse-event detection for pharma companies. Medical concepts contain medical conditions, medications, procedures, and other clinical events. Extracting medical concepts is a complicated process due to the specialist knowledge required and the broad use of synonyms in the medical field. Furthermore, to make detected concepts useful for large-scale analytics and decision-making applications, they have to be codified. This is a process where a specialist looks up matching codes from a medical ontology, often containing tens to hundreds of thousands of concepts.<\/p>\n

To solve these problems, Amazon Comprehend Medical<\/a> provides a fast and accurate way to automatically extract medical concepts from the written text found in clinical documents. You can now also use a new feature to automatically standardize and link detected concepts to the SNOMED CT (Systematized Nomenclature of Medicine\u2014Clinical Terms) ontology. SNOMED CT provides a comprehensive clinical healthcare terminology and accompanying clinical hierarchy, and is used to encode medical conditions, procedures, and other medical concepts to enable big data applications.<\/p>\n

This post details how to use the new SNOMED CT API to link SNOMED CT codes to medical concepts (or entities) in natural written text that can then be used to accelerate research and clinical application building. After reading this post, you will be able to detect and extract medical terms from unstructured clinical text, map them to the SNOMED CT ontology (US edition), retrieve and manipulate information from a clinical database, including electronic health record (EHR) systems, and map SNOMED CT concepts to other ontologies using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM<\/a>) if your EHR system uses an ontology other than SNOMED CT.<\/p>\n

Solution overview<\/h2>\n

Amazon Comprehend Medical is a HIPAA-eligible natural language processing (NLP) service that uses machine learning (ML) to extract clinical data from unstructured medical text\u2014no ML experience required\u2014and automatically map them to SNOMED CT, ICD10, or RxNorm ontologies with a simple API call. You can then add the ontology codes to your EHR database to augment patient data or link to other ontologies as desired through OMOP CDM. For this post, we demonstrate the solution workflow as shown in the following diagram with code based on the example sentence \u201cPatient X was diagnosed with insomnia.\u201d<\/p>\n

\"\"<\/p>\n

To use clinical concept codes based on a text input, we detect and extract clinical terms, connect to the clinical data base, transform SNOMED code to OMOP CDM code, and use them within our records.<\/p>\n

For this post, we use the OMOP CDM<\/a> as a database schema as an example. Historically, healthcare institutions in different regions and countries use their own terminologies and classifications for their own purposes, which prevents the interoperability of the systems. While SNOMED CT standardizes medical concepts with a clinical hierarchy, the OMOP CDM provides a standardization mechanism to move from one ontology to another, with an accompanying data model. The OMOP CDM standardizes the format and content of observational data so that standardized applications, tools and methods can be applied across different datasets. In addition, the OMOP CDM makes it easier to convert codes from one vocabulary to another by having maps between medical concepts in different hierarchical ontologies and vocabularies. The ontologies hierarchy is set such that descendants are more specific than ascendants. For example, non-small cell lung cancer is a descendent of malignant neoplastic disease. This allows querying and retrieving concepts and all their hierarchical descendants, and also enables interoperability between ontologies.<\/p>\n

We demonstrate implementing this solution with the following steps:<\/p>\n

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  1. Extract concepts with Amazon Comprehend Medical SNOMED CT and link them to the SNOMED CT (US edition) ontology.<\/li>\n
  2. Connecting to the OMOP CDM.<\/li>\n
  3. Map the SNOMED CT code to OMOP CDM concept IDs.<\/li>\n
  4. Use the structured information to perform the following actions:\n
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    1. Retrieve the number of patients with the disease.<\/li>\n
    2. Traverse the ontology.<\/li>\n
    3. Map to other ontologies.<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n

      Prerequisites<\/h2>\n

      Before you get started, make sure you have the following:<\/p>\n