“One-size-fits-all” approach to treatment, based on a broad population average, is not taking into account age, gender, lifestyle, previous diseases, comorbidities, or genetics. That is why it can be challenging to select an optimal treatment schema for all cases. But applying personalized EHR/EMR data and predictive ML can ensure personalized treatment, so our team decided to create a method for treatment outcome prediction.

• All patient-level data (EMR/EHR) received from our clients was transformed into standardized machine-readable format. Later we defined cohorts, i.e., separate classes based on the treatment outcome for particular diseases (“positive”, “negative”, and “no progress”).
• The next step was to create and run a machine learning algorithm over the data and to train a model that would be able to predict a treatment outcome.
• We consider the given model to be used for further predictions.

Having processed a great amount of patient data, we can formulate a treatment strategy based on various criteria, such as age, gender, medical history and genetic profile. So this is how we are helping doctors to provide a personalized therapeutic treatment plan for each patient.

1. Domain Expertise in Healthcare
2. Natural Language Processing (NLP)
3. Relational Database Management (SQL-based)
4. ETL process: OHDSI Software Tools (Whiterabbit and Rabbit-in-a-hat, Usagi, Athena, Achilles, Atlasi)
5. Data Science and Data Analytics
6. Programming languages: R, Python, SQL
7. Machine Learning
8. Predictive Analysis