Medical Knowledge and Decision Support

We study “Medical Knowledge and Decision Support” with data science and artificial intelligence, and use qualitative approaches to explore digital transformation in the clinic.

The overarching objectives of the research of the Medical Knowledge and Decision Support group are to implement innovative data science approaches, accelerate discovery, translation and integration of evidence into clinics, and to align technological progress with the needs of clinicians.

Methods

Our research draws methodologically from data science, bioinformatics, computer science, and psychology. We build ontologies and an associated ecosystem for logic-based automation. We apply and develop predictive models using AI/ML, particularly LLMs and neuro-symbolic approaches. We apply mathematical approaches including constraint-based modelling. In addition, we use qualitative approaches to study human perspectives.

Use cases

Use Cases

We address a wide range of application use cases across our projects, including the discovery and prediction of functions and regulatory factors, biomedical and clinical evidence synthesis and automation, pre-trained large language and multi-modal model evaluation, extraction of data from clinical notes for predictive modelling, decision support, medical question answering, and interpretability and explanations for AI applications.

Clinical Areas

Our projects span a range of biomedical and clinical domains, including fundamental metabolism and metabolomics; drug discovery and molecular property prediction; RNA biology and regulatory interactions; oncology and personalised cancer biology; behavioural science and health psychology; mental health including anxiety, depression and psychosis; ophthalmology; infectious diseases; and physiotherapy.


Contact and Collaboration

If you are interested in partnering with us, have an idea you’d like to discuss, or simply want to get in touch, we would love to hear from you! 

Please reach out via email to: janna.hastings@unisg.ch or janna.hastings@uzh.ch

School of MedicineUniversity of St.Gallen (HSG)
St. Jakob-Strasse 21
9000 St.Gallen, Switzerland

Institut for Implementation Science in Health Care (IfIS)
Universität Zürich
Universitätsstrasse 84
8006 Zürich, Switzerland

New Publication: Factors Guiding Clinical Decision-Making in Genitourinary Oncology

We are excited to announce that our latest paper, “Factors Guiding Clinical Decision-Making in Genitourinary Oncology,” has been published in Cancer Medicine.

This study explores the complex factors that influence treatment decisions in genitourinary oncology, particularly in prostate cancer.

Our research identifies 59 distinct factors that influence clinical decisions, categorized into three key types:

1. Decision-Maker-Related Factors: These factors consider the personal attributes of healthcare professionals and patients, such as the physician’s experience, knowledge, and personal motivation, as well as patient preferences and their ability to comprehend treatment options.

2. Decision-Specific Factors: This category includes clinical aspects like disease stage, treatment toxicity, and comorbidities. It also covers how well the patient tolerates different treatment modalities and the complexity of balancing short-term outcomes with long-term effects.

3. Contextual Factors: These refer to external circumstances influencing decisions, such as healthcare system policies, availability of resources, and even socioeconomic factors that can affect access to treatment.

    This research aims to enhance understanding and refine the process of clinical decision-making in oncology, ultimately leading to better patient care and integration of decision-support tools.

    You can read the full publication here.