Data Science for Enabling Precision Medicine in Thyroid Disease
We are a world leading group that focuses on a diverse range of topics. We research at interface between computer science, mathematics, biology and epidemiology.
We have expertise working on common, complex diseases that have a genetic as well as an environmental component. We work with data from the Danish health registries, and large local and international biobanks. In our projects, we employ traditional biostatistical and epidemiological methods as well as state-of-the-art machine/deep learning approaches.<span data-ccp-props="{" 335559685="" :720="">
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Our group’s focus can be outlined as “Data Science for Enabling Precision Medicine in Thyroid Disease”.
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Autoimmune hypothyroidism is one of the most common endocrine diseases worldwide. Having hypothyroidism affects the quality of life of the patients and increases their likelihood for having other adverse health conditions including cardiovascular, metabolic, mental and fertility conditions as well as thyroid cancer and other immune-mediated diseases. Hence, there is a critical need for understanding the patient profiles to offer improved personalized healthcare and life-style recommendations to individuals at risk. We aim to shed new light on the underlying causes and progression of the disease by analyzing large patient data sets from Denmark and abroad, including genetic factors and other molecular biomarkers. We use and develop bioinformatics and machine learning methods to define disease trajectories, to look for patient clustering patterns, and to understand the genetic and non-genetic underpinnings of the disease.
The Hjaltelin group has developed an AI-algorithm, in collaboration with the Brunak group, capable of detecting pancreatic cancer early on using the nationwide Danish National Patient Registry (comprising 8 million) patients, and cross-validation on 3 million American patients:
- Nature medicine: Nature Medicine article
- DR: DR article
- Videnskab.dk: Videnskab.dk artikel
Furthermore, the Hjaltelin group works on multimodality data and advanced machine learning models for broader cancer detection and risk assessment as explained in our review:
https://www.thelancet.com/journals/lanonc/article/PIIS1470-2045(24)00277-8/abstract
The group also has a high emphasis on systems-level understanding of comorbidity and multimorbidity, exploring how these can impact disease progression and patient outcomes:
- Nature Reviews Genetics: https://www.nature.com/articles/nrg.2016.87
Early detection of pancreatic cancer using multimodal patient trajectories and AI
Pancreatic cancer is one of the hardest to detect early, with a 5-year survival rate of just 12%. This project leverages artificial intelligence and Danish health registries to uncover links between pancreatic cancer and new-onset diabetes. By analyzing patient data across multiple healthcare domains, including hospital diagnoses, lab measurements, medications, and primary care visits, this project seeks to identify subtle symptoms and risk factors of the cancer early on and to pinpoint high-risk individuals. The project aims to make significant strides in personalized cancer detection through timely diagnosis and individual risk profiles.
Ovarian cancer and early symptoms
Ovarian cancer is the 8th most common cancer among women worldwide, often going undiagnosed until its later stages due to vague symptoms that mimic milder conditions or menopause. As a result, survival rates remain low, with only a 50% chance of survival over five years. However, when detected at an early stage (stage 1), this rate can increase to 90%.
In this project we seek to improve understanding and detection of early symptoms of ovarian cancer using multimodal health data and explainable AI methods.
