Retrospectively collected medical data has the opportunity to improve patient care through knowledge discovery and algorithm development. Broad reuse of medical data is desirable for the greatest public good, but data sharing must be done in a manner which protects patient privacy. Here we present Medical Information Mart for Intensive Care (MIMIC)-IV, a large deidentified dataset of patients admitted to the emergency department or an intensive care unit at the Beth Israel Deaconess Medical Center in Boston, MA. MIMIC-IV contains data for over 65,000 patients admitted to an ICU and over 200,000 patients admitted to the emergency department. MIMIC-IV incorporates contemporary data and adopts a modular approach to data organization, highlighting data provenance and facilitating both individual and combined use of disparate data sources. MIMIC-IV is intended to carry on the success of MIMIC-III and support a broad set of applications within healthcare.

Retrospectively collected medical data has the opportunity to improve patient care through knowledge discovery and algorithm development. Broad reuse of medical data is desirable for the greatest public good, but data sharing must be done in a manner which protects patient privacy.

The Medical Information Mart for Intensive Care (MIMIC)-III database provided critical care data for over 40,000 patients admitted to intensive care units at the Beth Israel Deaconess Medical Center (BIDMC). Importantly, MIMIC-III was deidentified, and patient identifiers were removed according to the Health Insurance Portability and Accountability Act (HIPAA) Safe Harbor provision. MIMIC-III has been integral in driving large amounts of research in clinical informatics, epidemiology, and machine learning. Here we present MIMIC-IV, an update to MIMIC-III, which incorporates contemporary data and improves on numerous aspects of MIMIC-III. MIMIC-IV adopts a modular approach to data organization, highlighting data provenance and facilitating both individual and combined use of disparate data sources. MIMIC-IV is intended to carry on the success of MIMIC-III and support a broad set of applications within healthcare.

Maintaining a healthy population is essential for improving quality of life and overall societal well-being. One approach to achieving a healthy population is by improving patients' care pathways. This is particularly vital for patients with multiple chronic conditions, who require well-coordinated care across various medical specialties. One approach to improving and analyzing the care pathways of these types of patients is process mining. The clinical event knowledge graph is a recent framework in process mining introduced for patients with multimorbidity that facilitates standardized interpretation of care pathways by linking to ICD-10 and SNOMED-CT. It also facilitates storing recent multi-entity event data in the event graph and analyzing care pathways for multimorbid patients from multiple perspectives. MIMIC-IV is a dataset that facilitates data analysis in healthcare; however, it is not specialized for process mining and requires extensive data preprocessing to prepare it for process mining. This paper contributes to the MIMIC-IV-Ext-CEKG dataset, an extracted dataset from MIMIC-IV that facilitates using the Clinical Event Knowledge Graph framework and other process mining tasks. This paper describes its characteristics and how it is extracted from the MIMIC-IV dataset. MIMIC-IV-Ext-CEKG facilitates deploying MIMIC-IV for process mining.

Embeddings for SNOMED CT concepts produced by models of FastText trained on different corpora. Each file contains a JSON file that links the ID of a SNOMED CT concept to its corresponding embedding. Files ft_mimicN_dict.json contain the embeddings of models trained on subsets of MIMIC-IV, where N denotes the percentage of MIMIC-IV used in the training of the model; whereas ft_snomed_ct_walks_dict.json contains the embeddings of a FastText model trained on an artifical corpus obtained by performing walks on SNOMED CT (https://doi.org/10.1016/j.jbi.2023.104297).

These embeddings were generated and studied in the paper Assessing the Effectiveness of Embedding Methods in Capturing Clinical Information from SNOMED CT () and more information can also be found in the following repository: https://github.com/JavierCastellD/AssessingSNOMEDEmbeddings.

This challenge, sponsored by SNOMED International, seeks to advance the development of Entity Linking models that operate on unstructured clinical texts. Participants in the challenge will train entity linking models using a subset of MIMIC-IV-Note discharge summaries that have been annotated with SNOMED CT concepts by a team of medical professionals. The full dataset (which is comprised of a training set and a test set) consists of approximately 75,000 annotations across nearly 300 discharge summaries. The competition is being hosted by DrivenData, whose platform will manage registration, code submission, and evaluation.

Patient characteristics in hospital survivors and non-survivors.

Histopathological findings in the 46 patients and subsequent outcome.