Abstract The ability of Large Language Models (LLMs) to analyze and respond to freely
written text is causing increasing excitement in the field of psychiatry; the application of such …

Topic modeling has become a popular approach to identify semantic structures in text
corpora. Despite its wide applications, interpreting the outputs of topic models remains …

Objective This work aims to explore the value of Dutch unstructured data, in combination
with structured data, for the development of prognostic prediction models in a general …

Inpatient violence is a common and severe problem within psychiatry. Knowing who might
become violent can influence staffing levels and mitigate severity. Predictive machine …

The clinical notes in electronic health records have many possibilities for predictive tasks in
text classification. The interpretability of these classification models for the clinical domain is …

Testamentary capacity (TC), a set of capacities involved in making a valid Will, has become
prominent in capacity evaluations due to the demographic increase in older persons and …

In many domains that employ machine learning models, both high performing and
interpretable models are needed. A typical machine learning task is text classification, where …

There is limited research on fairness in automated decision-making systems in the clinical
domain, particularly in the mental health domain. Our study explores clinicians' perceptions …

The technical landscape of clinical machine learning is shifting in ways that destabilize
pervasive assumptions about the nature and causes of algorithmic bias. On one hand, the …

Topic modeling is a prevalent task for discovering the latent structure of a corpus, identifying
a set of topics that represent the underlying themes of the documents. Despite its popularity …