Abstract Background: Natural Language Processing (NLP) is widely used to extract clinical
insights from Electronic Health Records (EHRs). However, the lack of annotated data …

Background Machine learning systems are part of the field of artificial intelligence that
automatically learn models from data to make better decisions. Natural language processing …

In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC),
the advent of functional neuroimaging, novel technologies and methods provide new …

The timely identification of patients who are at risk of a mental health crisis can lead to
improved outcomes and to the mitigation of burdens and costs. However, the high …

Background The potential of artificial intelligence (AI)–based large language models, such
as ChatGPT, has gained significant attention in the medical field. This enthusiasm is driven …

Objective Natural language processing (NLP) combined with machine learning (ML)
techniques are increasingly used to process unstructured/free-text patient-reported outcome …

Objective We develop a deep learning framework based on the pre-trained Bidirectional
Encoder Representations from Transformers (BERT) model using unstructured clinical notes …

Problem: Clinical practice requires the production of a time-and resource-consuming great
amount of notes. They contain relevant information, but their secondary use is almost …

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 …