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The Medical Speech to Text Model converts spoken language into written text, specifically tailored for the medical field. The resulting text is further analyzed and relevant medical entities are extracted together with assertion statuses for those and relations between them.
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
The Clinical De-Identification model is designed to recognize and anonymize PHI in Italian-language clinical notes. It employs state-of-the-art natural language processing techniques to detect sensitive information such as patient names, addresses, medical record numbers, and other identifiers....
Model Package - Fulfilled on Amazon SageMaker
Free Trial
The Clinical De-Identification model is designed to recognize and anonymize PHI in Spanish-language clinical notes. It employs state-of-the-art natural language processing techniques to detect sensitive information such as patient names, addresses, medical record numbers, and other identifiers....
Model Package - Fulfilled on Amazon SageMaker
At Lelapa AI, we're committed to advancing language technology and broadening its reach with our specialized Vulavula Multilingual Named Entity Recognition (NER) Model, particularly designed for Africa's linguistic diversity. This innovative model efficiently identifies and categorizes named...
Model Package - Fulfilled on Amazon SageMaker
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 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
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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