MIMIC-IV-Ext clinical decision support for referral, triage and diagnosis

Accurate medical decision-making is critical for both patients and clinicians.
Patients often find it difficult to interpret their symptoms, determine their
severity, and select the right specialist to see. At the same time, clinicians
face challenges in integrating complex patient data to make timely, accurate
diagnoses. Recent advancements in large language models (LLMs) offer the
potential to bridge this gap by supporting decision-making for both patients
and healthcare providers. To support the development of LLMs in healthcare, we
have curated and extended the MIMIC-IV dataset.

We extracted, processed, and extended the MIMIC-IV dataset, which resulted in
9,150 real-world medical cases with various pathologies. From this larger set
2,200 were further refined to meet the specific needs of our task. This
extended dataset is designed to evaluate and improve LLMs' ability to assist
with triage, specialist referral, and diagnosis, using critical patient
information such as history of present illness, vitals signs and other
relevant data. This allows for the development of models that can help both
clinicians and patients in making better-informed decisions, ultimately
improving healthcare delivery and patient outcomes.