In predicting heart attack
The treatment decisions taken by doctors depend on the risk scores. The scores are variables and through repetitive adjustment machine learning can identify complex patterns. In a study which was presented in the International Conference on Nuclear Cardiology and Cardiac they revealed that with more than 90% accuracy machine learning algorithm can detect heart attacks.
In making antibiotics more effective
A study conducted by MIT researchers, reveal that a new machine language resemble to discover the mechanism which assists some antibiotics to finish bacteria. With the utility of this mechanism researchers can develop new drugs which can be used alongside the antibiotic to increase their killing ability.
It helps in predicting the response of the drug in Crohn’s suffering patient
Crohn is an inflammatory bowel disease. A study in Jama Network Open reveal that a machine learning model can accurately predict which patients suffering from Crohn’s will respond positively to the treatment. This has ultimately led to reduced cost and better results.
Personalized treatments is more effective by combining individual health along with predictive analytics which leads to better disease assessment. IBM Watson Oncology are using patient medical history along with machine learning helps to generate multiple options for treatment.
Smart Health Records
To maintain health records is very exhaustive. Machine learning helps to ease the process which saves time, money and effort. MIT is developing a next generation smart health records that will use ML based tools to help the diagnosis, treatment suggestions etc.