ML use cases in HealthCare

Machine learning is aimed at training models to begin recognizing patterns using training data.

In healthcare, machine learning can take over routine tasks such as the management of patient records and claims to process it. The key objective is to reduce human labor in administrative and regulatory duties that machines can do more efficiently. The use of AI-powered chatbots, for example, can provide a more personalized and convenient healthcare experience.

Few ML use-cases in healthcare:

1. Disease and Diagnosis Identification: Diagnosing cancer at the initial stages, for instance, is not an easy process, and the same applies to other genetic ailments.

2. Disease Prediction:?Diabetes is a common yet dangerous disease. It is known to cause other serious health conditions such as vision loss, heart and kidney diseases. An early diagnosis of this disease can potentially save lives.

3. Personalized Treatment: It means pairing a patient’s health with predictive analytics. IBM Watson Oncology is a leader in this area by using patient medical history to provide multiple treatment plans. Personalized treatment will become even better as more advanced biosensors enter the market, availing more data for ML algorithms.

4. Making diagnoses via image analysis:?Microsoft is revolutionizing healthcare data analysis with its InnerEye project. This startup uses Computer Vision to process medical images to make a diagnosis.

5 . ADHD is Attention-deficit/hyperactivity disorder, it feels like “Incomparable boredom mixed with infinite fascination.”

As someone with ADHD (very very mild, I can hyperfocus ) also i know a bunch of friends who have ADHD with varying intensity and subtypes. Machine learning has been one of the biggest boons in improving the quality of life for many of my friends and me.

Here are some of the ML used resources that we use :

[ 1] Silence skipper

[ 2] Text to speech

[ 3] Text to dyslexic font

[ 4] Grammarly

[ 5] Duolingo

[ 6] Translate

[ 7 ] Google maps