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This model specializes in identifying key risk factors such as Coronary Artery Disease, Diabetes, Family History, Hyperlipidemia, Hypertension, Medications, Obesity, and Smoking Habits in clinical documentation. Designed for precision, it assists healthcare professionals in crucial risk assessment...
Model Package - Fulfilled on Amazon SageMaker
This solution identifies and anonymizes Personally Identifiable Information like Name, SSN, Email, Phone numbers from tabular data. The Solution is designed to work on structured data source.
Model Package - Fulfilled on Amazon SageMaker
Free Trial
The Clinical De-Identification model is designed to recognize and anonymize PHI in Portuguese-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
This model is engineered for the extraction of adverse drug events (ADEs) from unstructured clinical texts, leveraging several components finely tuned for this purpose: - Entity Recognition: Initially, the model accurately identifies entities related to adverse events (such as rash, nausea) and...
Model Package - Fulfilled on Amazon SageMaker
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
The Clinical De-Identification model is designed to recognize and anonymize PHI in German-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. Once...
Model Package - Fulfilled on Amazon SageMaker
Free Trial
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
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
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