  A hospital readmission is when a patient who was discharged from the hospital
is admitted again for the same or related care within a certain period.
Hospital readmissions are a significant problem in the healthcare domain, as
they lead to increased hospitalization costs, decreased patient satisfaction,
and increased risk of adverse outcomes such as infections, medication errors,
and even death. The problem of hospital readmissions is particularly acute in
intensive care units (ICUs), due to the severity of the patients' conditions,
and the substantial risk of complications. Predicting Unplanned Readmissions in
ICUs is a challenging task, as it involves analyzing different data modalities,
such as static data, unstructured free text, sequences of diagnoses and
procedures, and multivariate time-series. Here, we investigate the
effectiveness of each data modality separately, then alongside with others,
using state-of-the-art machine learning approaches in time-series analysis and
natural language processing. Using our evaluation process, we are able to
determine the contribution of each data modality, and for the first time in the
context of readmission, establish a hierarchy of their predictive value.
Additionally, we demonstrate the impact of Temporal Abstractions in enhancing
the performance of time-series approaches to readmission prediction. Due to
conflicting definitions in the literature, we also provide a clear definition
of the term Unplanned Readmission to enhance reproducibility and consistency of
future research and to prevent any potential misunderstandings that could
result from diverse interpretations of the term. Our experimental results on a
large benchmark clinical data set show that Discharge Notes written by
physicians, have better capabilities for readmission prediction than all other
modalities.
