The client was building a non-invasive glucose monitoring system. One of the first steps was to calibrate the physical device in order to transform the raw signal measurements to glucose levels. The preliminary work involved analyzing chronoamperometry data in order to find optimal parameters for the device and a way to calibrate the device.
The goal of this project was to develop a Deep Learning powered solution detecting a disease on medical X-ray images. The model I have developed achieved an AUC of 0.98. The solution is part of a bigger system which is developed to assist medical doctors in their daily work.
The goal of this project was to develop a machine learning backed investment strategy based on multiple binary positive and negative signals sourced from price, market, and environmental analysis. The strategy I have developed performed 4x better than the baseline.
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 in collaboration with a health insurer to allow preventive interventions for patients with high risk of a stroke.
The goal of this project was to develop an AI-backed decision support system helping medical doctors to decide about blood transfusion. In collaboration with multiple hospitals, we collected historical data about blood transfusions, patients, and medical tests relevant to blood transfusion.
The goal of this project was to develop an AI backend for a frequently asked questions chatbot. A client had collected a significant amount of questions and answers that were used to train machine learning natural language processing models.
The goal of this project was to develop an anomaly detection pipeline for business intelligence systems in order to trigger a BI report distribution - via slack or e-mail - when an anomalous situation was detected or send an alert to predefined groups of managers.