Model Building and Planning Assistance in Psychotherapy

Project IDSAIR: Development of Tools for Dynamic Systems Analysis in Resilience Research

Resilience research ideals with the analysis and prognosis of the complex processes that enable the maintenance or rapid recovery of mental health during or after stressful events. This requires the qualified evaluation of the collected medical data and their targeted use in terms of optimal individual patient care.

Profound Predication and Therapy of Mental Illnesses

The project goals of IDSAIR are the development of prediction models for mental illnesses and a planning assistant for their targeted treatment. The prediction models enable a patient-specific estimation of the stress-related risk of disease. The planning assistant supports treating physicians and therapists in guideline-compliant selection of suitable diagnostics and therapies.

AI-based Data Analysis and Knowledge-based Decision Support

To develop the prediction models, OMICS data from genome, transcriptome, etc. first undergo preprocessing and downstream analysis. Development of prediction models first relies on a preprocessing and downstream analysis of OMICS data from genome, transcriptome, etc. This is then followed by pattern recognition using supervised and unsupervised machine learning methods and an creation of models. For software-assisted treatment planning, a data model is developed with the case parameters relevant for decision-making. Therapy guidelines are converted into a software-compatible format and provided as a medical knowledge base. Based on this information, the planning assistant determines the individual medically indicated treatment options for a patient case using predicate logic and multi-criteria decision-making. This enables physicians to plan high-quality therapies in a time-efficient manner.

Project Partner

Leibniz Institute for Resilience Research

The project is funded by the Ministry of Science, Further Education and Culture of the State of Rhineland-Palatinate.