Data analytics and machine learning in healthcare

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Solution Accelerators for Healthcare

Based on best practices from our work with the leading healthcare organizations, we’ve developed solution accelerators for common analytics and machine learning use cases to save weeks or months of development time for your data scientists, engineers and analysts.

Clinical health 
data lake

Healthcare payers and providers generate petabytes of structured and unstructured data, including medical tests, claims, prescribed treatments, medical images, streaming IoT device data and more. By aggregating this data into a clinical data lake, healthcare organizations can build longitudinal views of their patients’ health, answer more complex questions and generate powerful predictive analytics. This solution accelerator provides a template for building a healthcare data lake and comorbidity dashboard.

Disease 
prediction

Modern imaging technologies enable healthcare providers to rapidly digitize high-resolution pathology slides. These large data sets can be used to build automated diagnostics with machine learning that, in turn, help providers improve the efficiency and effectiveness of diagnosing cancer and infectious disease. This solution accelerator provides an automated methodology for rapidly identifying regions of metastases in whole slide images with deep learning.

Automating 
digital pathology

Modern imaging technologies enable healthcare providers to rapidly digitize high-resolution pathology slides. These large data sets can be used to build automated diagnostics with machine learning that, in turn, help providers improve the efficiency and effectiveness of diagnosing cancer and infectious disease. This solution accelerator provides an automated methodology for rapidly identifying regions of metastases in whole slide images with deep learning.

Blog/Notebook:

Genetic 
association studies

Genome-wide association studies help identify genetic variations that are associated with a particular disease. This information can be used to better detect, treat and prevent chronic conditions such as asthma, cancer, diabetes and heart disease. This solution accelerator and open-source project provides a new scalable method for whole genome regressions.

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