Remove Datasets Remove Medical Remove Unstructured Data
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Medical Datasets for Machine Learning: Aims, Types and Common Use Cases

AltexSoft

Everyday the global healthcare system generates tons of medical data that — at least, theoretically — could be used for machine learning purposes. Regardless of industry, data is considered a valuable resource that helps companies outperform their rivals, and healthcare is not an exception. Medical data labeling.

Medical 52
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How to get datasets for Machine Learning?

Knowledge Hut

Datasets are the repository of information that is required to solve a particular type of problem. Also called data storage areas , they help users to understand the essential insights about the information they represent. Datasets play a crucial role and are at the heart of all Machine Learning models.

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Natural Language Processing in Healthcare: Using Text Analysis for Medical Documentation and Decision-Making

AltexSoft

This allows machines to extract value even from unstructured data. Healthcare organizations generate a lot of text data. But a lot of data (by different estimations, 70 or 80 percent of all clinical data) remains unstructured , kept in textual reports, clinical notes, observations, and other narrative text.

Medical 52
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Processing medical images at scale on the cloud

Tweag

To allow innovation in medical imaging with AI, we need efficient and affordable ways to store and process these WSIs at scale. load training metadata dataset = PatchDataset ( slides_specs = slides_specs ) train_loader = DataLoader ( dataset ) trainer = pl. Then this dataset can be plugged to our PyTorch script using.to_torch.

Medical 60
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Length of Stay in Hospital: How to Predict the Duration of Inpatient Treatment

AltexSoft

This article describes how data and machine learning help control the length of stay — for the benefit of patients and medical organizations. The length of stay (LOS) in a hospital , or the number of days from a patient’s admission to release, serves as a strong indicator of both medical and financial efficiency. Source: Intel.

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Big Data vs Data Mining

Knowledge Hut

View A broader view of data Narrower view of data Data Data is gleaned from diverse sources. Results Broader and exploratory results Targeted results Big Data vs Data Mining Here is a more detailed illustration of the difference between big data and data mining:- 1.

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Claims Processing with Generative AI: Making Sense of the Data

Precisely

From documenting losses and damages to verifying that a claim submission meets all the necessary criteria, each step requires meticulous attention to detail and often entails reviewing lengthy narrative documents such as accident reports, medical records, and legal demands letters.