Article: Open-Source Framework Simplifies Machine Learning Processes

Open Health, Open Source, Procedures (Medical)

Open-Source Framework Simplifies Machine Learning Processes

Researchers at MIT’s Data to AI Lab (DAI Lab) have developed a new framework that can streamline machine learning processes to help organizations uncover actionable insights from big data. The system, called Cardea, is open-source and uses generalizable techniques so that hospitals can share machine learning solutions with each other, leading to increased transparency and collaboration.

To develop Cardea, researchers leveraged automated machine learning, or AutoML. The goal of AutoML is to democratize predictive tools, making it easier for people to build, use, and understand machine learning models.

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Article: Real-Time Data Analytics Tool Helps Track, Treat Drug Abuse

Cures, Drugs, Open Health, Procedures (Medical)

Real-Time Data Analytics Tool Helps Track, Treat Drug Abuse

A team at the New Jersey Institute of Technology (NJIT) has developed a real-time data analytics tool to help treatment centers and counselors identify and treat drug abuse.  Nonprofits trying to help users overcome their addiction often have a difficult time getting real-time, actionable information on the fast-changing, underground culture of drug abuse.

Using machine learning and data analytics, researchers at NJIT have created DrugTracker, a community-focused drug abuse monitoring and support system.  DrugTracker monitors online platforms such as Twitter and Reddit and combines this information with geospatial data to find out where users are obtaining drugs, as well as trends or changes in the landscape.

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