BIG DATA TO INDIVIDUALIZE MANAGEMENT OF CHRONIC DISEASES

The video discusses Ellen Floyd's battle with Scleroderma and how her medical history, along with those of 3000 other patients, is being used to develop personalized treatments. Supported by the National Science Foundation, computer scientists at Johns Hopkins University are leveraging big data and machine learning to improve healthcare outcomes. These models analyze medical records to predict disease progression and guide treatment decisions, particularly for autoimmune diseases. The goal is to fine-tune treatments for better accuracy and broader global impact.

What is the main goal of using patient medical histories in the treatment of Scleroderma?

• To standardize treatment for all patients

• To reduce the cost of treatment

• To design personalized treatments based on past outcomes

• To eliminate the need for medication

What is the primary focus of Suchi Saria's team at Johns Hopkins University?

• Building hospitals around the world

• Training doctors in patient care

• Creating statistical models to analyze medical data

• Developing new medications for autoimmune diseases

How do the computer models assist doctors at the Johns Hopkins Scleroderma Center?

• By prescribing medication automatically

• By predicting the likely course of a patient's disease

• By performing surgeries

• By diagnosing new diseases

What is a potential risk of immunosuppressive therapy mentioned in the video?

• Increased risk of heart disease

• Increased risk of infection and certain cancers

• Increased risk of diabetes

• Increased risk of neurological disorders

What is the broader hope for the tools developed at Johns Hopkins?

• To save patient lives globally

• To eliminate all autoimmune diseases

• To replace doctors worldwide

• To increase hospital revenue