Globally, the number of people with multiple co-occurring diseases will increase
substantially over the coming decades, with important consequences for patients, carers …

Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …

Pancreatic cancer is an aggressive disease that typically presents late with poor outcomes,
indicating a pronounced need for early detection. In this study, we applied artificial …

Process mining techniques can be used to analyse business processes using the data
logged during their execution. These techniques are leveraged in a wide range of domains …

Background Socioeconomic disadvantage is a risk factor for many diseases. We
characterised cascades of these conditions by using a data-driven approach to examine the …

Modeling real-world multidimensional time series can be particularly challenging when
these are sporadically observed (ie, sampling is irregular both in time and across …

Background Globally, there is a paucity of multimorbidity and comorbidity data, especially for
minority ethnic groups and younger people. We estimated the frequency of common disease …

Personalized predictive medicine necessitates the modeling of patient illness and care
processes, which inherently have long-term temporal dependencies. Healthcare …

We simultaneously investigated the genetic landscape of ankylosing spondylitis, Crohn's
disease, psoriasis, primary sclerosing cholangitis and ulcerative colitis to investigate …

Multimorbidity—the co-occurrence of multiple diseases—is associated to poor prognosis,
but the scarce knowledge of its development over time hampers the effectiveness of clinical …