Integration of Clinical, Laboratory and Multi-Omics Data to Leverage Machine Learning for Diagnostics
Jan Kruta, FHNW Muttenz
Zurich Seminars in Bioinformatics
- 12:15 UZH Irchel Y55-l-06/08 and ZOOM Call
Abstract Early and accurate diagnosis is crucial for preventing disease development and defining therapy strategies. Due to predominantly unspecific symptoms, diagnosis of autoimmune diseases is notoriously challenging. Clinical decision support systems are a promising method with the potential to enhance and expedite precise diagnostics by physicians. However, due to the difficulties of integrating and encoding multi-omics data with clinical values, as well as a lack of standardization, such systems are often limited to certain data types. Accordingly, even sophisticated data models fall short when making accurate disease diagnoses and presenting data analyses in a user-friendly form. Therefore, the integration of various data types is not only an opportunity but also a competitive advantage for research and industry.
We have developed an integration pipeline to enable the use of machine learning for patient classification based on multi-omics data in combination with clinical values and laboratory results, that resulted in 95% prediction accuracy of autoimmune diseases studied. Our results deliver insights into autoimmune disease research and have the potential to be adapted for applications across disease conditions.