This solution helps users automate the coherent summary generation from documents. It identifies frequently used entity clusters in the document to capture salient and most important candidate sentences. An abstractive summary is generated from these sentences using reinforcement learning to...
Model Package - Fulfilled on Amazon SageMaker
Legal entity name extraction is an optimal way to identify and classify legal organization name and their aliases in an unstructured text. It can consume the texts such as legal documents and process it to identify all the legal entities/aliases in the document.
Model Package - Fulfilled on Amazon SageMaker
Free Trial
This model is designed to identify and map diseases and syndromes mentioned in text to their respective Concept Unique Identifiers (CUI) in the Unified Medical Language System (UMLS). This model simplifies the process of medical entity coding, playing a crucial role in healthcare data...
Model Package - Fulfilled on Amazon SageMaker
Free Trial
This model can identify and contextualize clinical events entities from clinical documentation, assign assertion statuses and determine temporal relations between those. Covered entities: DATE, TIME, PROBLEM, TEST, TREATMENT, OCCURENCE, CLINICAL_DEPT, EVIDENTIAL, DURATION, FREQUENCY, ADMISSION,...
Model Package - Fulfilled on Amazon SageMaker
This solution creates a knowledge graph based on entity-name pairs from data collected from multiple sources of information such as Wikipedia, company's website, CrunchBase etc. This solution creates a graph model of a company's profile based on unstructured data.
Model Package - Fulfilled on Amazon SageMaker
Free Trial
This model is engineered for radiology texts and reports, adeptly identifying entities such as imaging tests, imaging techniques, imaging findings, and more. It also automatically detects the assertion status of the findings: Confirmed, Suspected, Negative, and can find relations between diagnosis,...
Model Package - Fulfilled on Amazon SageMaker
Free Trial
This model extracts biological and genetics entities from medical texts to enhance therapeutic research, early diagnosis, and personalized care, driving forward data-driven medical advancements. The model was tailored to identify and extract various biological entities such as genes, anatomical...
Model Package - Fulfilled on Amazon SageMaker
Free Trial
This model was created to facilitate the accurate mapping of drugs to their corresponding RxNorm codes and related drug classes. It is an essential tool for healthcare professionals and pharmacists, ensuring precise medication identification and categorization, which is crucial for patient safety,...
Model Package - Fulfilled on Amazon SageMaker
Free Trial
This model extracts more than 40 oncology-related entities, including therapies, tests, staging, histological type, oncogene, and radiation dose from clinical documentation, pathology, and diagnostic reports, optimizing oncology workflows and advancing personalized cancer treatments. It also...
Model Package - Fulfilled on Amazon SageMaker
With YZR's API and collaborative AI platform: - Business experts spend much less time correcting, tagging and grouping textual data manually with a NLP-powered solution - Data and IT teams integrate faster and with more confidence automated textual data quality pipelines into ETLs, data lakes,...
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