How can AI be used in order to make better diagnoses and more accurate pathology prediction?
The number of people looking for a medical advice grows partly due to the aging population. Health care professionals have to handle this growing number of patients in a limited amount of time. They have to make a diagnosis quicker with an increasing risk of medical error. Medical doctors gather patients data: general information, pathologies, test results, lifestyle… Using Artificial Intelligence to build predictive models out of this data can help them with pathology prediction and therefore provide better healthcare.
Healthcare professionals can use predictive models to help them make diagnosis. However, most of them are not Data Scientists and don’t have the skills neither in Machine Learning nor in coding to build predictive models. Healthcare professionals handle a small quantity of data that is essentially their patients’ health data and can be considered as Small Data. Traditional Machine Learning tools don’t handle Small Data well.
In this context, MyDataModels offers a product called TADA. It is an easy to use software solution which helps healthcare professionals such as doctors, researchers, nurses, etc. to automatically create predictive models using their patients related Small Data.
TADA can be used without any prior Data Science knowledge. Healthcare professionals can use their patients’ data without normalization or preprocessing and get pertinent results in less than a minute.
MyDataModels offers a self-service software solution for those professionals who own Small Data and have no Data science know-how.
Emergency Room (ER) and call centers receive a growing number of requests handled with increasing difficulty. Correctly guiding patients and optimizing care paths is among the biggest challenges that healthcare professionals will have to deal with in the coming years.
“Predictive model makes possible to identify with great precision a patients’ pathology”
By combining TADA with the answers to a few questions asked to the patients, it is possible to identify with great precision a patients’ pathology. Hence, the treatments can be administered quicker. In a nutshell, through the use of TADA, the patients waiting times can be reduced and their overall care path can be improved.
TADA will never replace healthcare professionals’ expertise, but it can provide them the support they need to speed up diagnosis.