June 3rd, 2020 | Published in Research
HIDSS4Health and HIDA
The aim of the Helmholtz Information & Data Science School for Health (HIDSS4Health) is to attract, promote and train the best young talents at the interface between data science and health-related applications. HIDSS4Health offers a structured doctoral training program embedded in a highly interdisciplinary research environment, bringing together experts from the data and life sciences. The scientific curriculum is complemented by training measures that provide doctoral researchers with the key qualifications expected from future leaders in science and industry.
As domain scientists from all research fields need to be equipped with knowledge, methods and tools in the areas of Information & Data Science, a training and education initiative for researchers in the Helmholtz Association has been established. The Helmholtz Information & Data Science Academy (HIDA) will coordinate networking, education and training activities throughout the entire Helmholtz Association. It bundles the resources of all Information & Data Science Schools (HIDSS), including HIDSS4Health.
“Augmenting physician workflow to personalize care decisions by predicting next steps and informational needs in (precision) oncology”
With the possibility of whole genomic sequencing for oncologic patients, many processes in their treatment have to be adapted. Physicians in oncology have significantly more data to consider in order to tailor diagnostic or therapy approaches to the need of a specific patient. The National Center for Tumor Diseases (NCT) in Heidelberg established workflows for physicians to utilize genomic and clinical data to assess second or third line therapy options for patients. Within the workflows, a lot of external knowledge from databases is needed. Hence, for each database (or database portal), many queries have to be formulated and queried in order to obtain needed information. The steps involved to research information for a single patient are identified for the majority of cases. However, the steps can vary in order or only be necessary depending on the result of previous steps. Hence, a precise detection and discrimination is necessary to infer plausible next steps. The overall goal is to support a physician in researching options for diagnosis or therapy for patients based on genomic and clinical information by preemptively supplying information needed at the particular workflow step. To achieve that, we aim to precisely identify workflow steps based on usage signals (screen video, click events, time events, …) and possible triggers for further informational needs. A model will be trained to identify steps currently worked on and predict possible next steps (ideally multiple alternatives with likelihoods). These steps of the model will be evaluated for accuracy and (expected) utility in the process at hand.
- Prof. Dr.-Ing. Michael Beigl (HIDSS4Health Data Science PI)
- Prof. Dr. Frank Ückert (HIDSS4Health Life Science PI)
- Paula Breitling (Doctoral researcher at TECO and HIDSS4Health elected member of steering committee)
- MITRO division of the National Center for Tumor Diseases (NCT)
- August 2019 – ongoing