There are two principal challenges of applying ML in cancer progression modeling: Having enough phenotypically rich data to train models and professional validation of generated insights. So we had all the ingredients to deal with the scientific problem and achieve the objective.

• In the majority of cases, medical data is highly disorganized. Thus the first step was to convert scattered medical definitions to strict machine-readable language using extensive domain expertise in healthcare, SQL, and NLP tools (Python-based).
• Then we implemented deep machine learning technologies (CNN-based models) to make predictions.
• And finally, we visualized obtained models using R and Shiny application in a human-readable format.

We created a predictive model to forecast the risk of a disease progression (e.g., relapse, protracted clinical course) for patients with lung cancer and lymphoma: After transforming raw data to the OMOP CDM data standard, we defined the required cohorts. Based on it, we applied CNN-based deep learning models, predicting cancer outcomes to data collected for patients during the "time at risk" window.

1. Domain Expertise in Healthcare
2. Natural Language Processing (NLP)
3. Relational Database Management (SQL-based)
4. Data Science and Data Analytics
5. Programming languages: R, Python, SQL
6. Machine Learning
7. Predictive Analysis