Mar 19, 2021
No image
Service categories
Service Lines
Artificial Intelligence
Software Development
Domain focus
Healthcare
Other
Programming language
Python
Frameworks
TensorFlow
Challenge
Since epilepsy is a severe disease, sometimes doctors need a second opinion to decide on the treatment type that would be the most fitting and safe for a particular type of seizure.
Since epilepsy is a severe disease, sometimes doctors need a second opinion to decide on the treatment type that would be the most fitting and safe for a particular type of seizure.
Solution
The solution made by our R&D team allows predicting drug or non-drug resistant epilepsy with high accuracy (82% at the moment) for a patient.
In situations where a patient has nonDRE, they could try experimental treatment methods.
The solution works as a service that can make a prediction for any patient at a doctor’s request.
The solution made by our R&D team allows predicting drug or non-drug resistant epilepsy with high accuracy (82% at the moment) for a patient.
In situations where a patient has nonDRE, they could try experimental treatment methods.
The solution works as a service that can make a prediction for any patient at a doctor’s request.
Results
We had daily calls with the US-based part of the team.
Data preparation
As input, we used the data of about 450 thousand patients. It included general information, ICD codes, information about treatment and doses. In an experimental way, we cleared the data. In the next steps, we used raw data for our model.
ML model
First, we tried different methods and models. We started with the most difficult ones and continued with more common ones. Also, we applied NLP techniques.
Integration
This model was integrated with Symfony Health to work as a service with access to patient data. It gives a prediction to the doctor before the patient’s visit.
We had daily calls with the US-based part of the team.
Data preparation
As input, we used the data of about 450 thousand patients. It included general information, ICD codes, information about treatment and doses. In an experimental way, we cleared the data. In the next steps, we used raw data for our model.
ML model
First, we tried different methods and models. We started with the most difficult ones and continued with more common ones. Also, we applied NLP techniques.
Integration
This model was integrated with Symfony Health to work as a service with access to patient data. It gives a prediction to the doctor before the patient’s visit.