Flask

Ischemic Stroke Risk Assessment

The goal of this project was to develop an AI backend engine for an intelligent decision support system which asses an ischemic stroke risk. The system was developed to allow preventive interventions for patients with high risk of a stroke. In collaboration with a health insurer we collected historical electronic health record, social-demographic data, and quality of life related data to train and evaluate machine learning models. The data was analyzed to find which aspects present in the collected datasets had the highest impact on the ischemic stroke risk.

Expert System for Blood Management

The goal of this project was to develop a rules-based decision support system. The solution was assisting medical doctors to make fast decision about blood transfusion. In collaboration with multiple healthcare domain experts and researchers we built a knowledge base describing rules on executing blood transfusion. The knowledge was then modelled within a rule-based decision support system. The system was deployed and served as a REST API within a docker container, what enabled horizontal scalability of the solution.

Predicting Blood Transfusion Needs

The goal of this project was to develop an AI-backed intelligent decision support system assisting medical doctors to make decision about blood transfusion. In collaboration with multiple hospitals we collected historical data about blood transfusions, patients, and medical tests relevant to blood transfusion. The data was analyzed to find which aspects present in the collected datasets had the highest impact on a decision if blood transfusion had been executed or not.

SolarData - Simulation the Performance of Photovoltaic Energy Systems

This platform allows to simulate the amount of energy produced by a photostatic energy system. It extracts weather data from Grib files (binary format of weather data). Based on this data and geographic coordinates the system computes how much energy a PV installation is able to produce for particular weather conditions and location. Finally, it generates a graphical report to present this data on charts. SolarData is used to evaluate current performance and determine the future value of PV generation projects (expressed as the predicted energy yield) and, by extension, influence how PV projects and technologies are perceived in terms of investment risk.

Machine Learnin backed FAQ Chatbot

The goal of this project was to develop an AI backend for a FAQ chatbot. A client had collected significant amount of questions and answers that were used to train machine learning models. Besides text, the questions had assigned tags which were used to cluster questions info topic-based segments. The overall algorithm was trained as follows: first we trained a topic classifier based on the assigned tags; for this we performed TF-IDF transformation and we trained an XGBoost classifier second, for each topic we built a Doc2Vec embeddings targeting only the specific topic.