Blood pressure estimation

The Medical Information Mart for Intensive Care III (MIMIC-III) dataset is a large, de-identified and publicly-available collection of medical records. Each record in the dataset includes ICD-9 codes, which identify diagnoses and procedures performed. Each code is partitioned into sub-codes, which often include specific circumstantial details. The dataset consists of 112,000 clinical reports records (average length 709.3 tokens) and 1,159 top-level ICD-9 codes. Each report is assigned to 7.6 codes, on average. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework. Source: MIT Laboratory for Computational Biology

HYPE Dataset - Version 1.0.0 REFERENCE PAPER ------------------- Morassi Sasso, A., Datta, S., Jeitler, M., Steckhan, N., Kessler, C. S., Michalsen, A., Arnrich, B., & Böttinger, E. (2020). HYPE: Predicting Blood Pressure from Photoplethysmograms in a Hypertensive Population. In M. Michalowski & R. Moskovitch (Eds.), Artificial Intelligence in Medicine. AIME 2020. Lecture Notes in Computer Science, volume 12299 (pp. 325–335). Springer, Cham. https://doi.org/10.1007/978-3-030-59137-3_29. CONTENT ------------------ - Sensor (PPG & Blood Pressure) and clinical data from 9 hypertensive subjects in two experiments (stress test and 24 hours) - PPG data from Empatica E4: photoplethysmography (PPG) data. - Spacelabs (SL 90217): blood pressure data. - Demographics and self-reported data during 24 hours (exercise, medication, etc.). AVAILABILITY ------------------ - Available to the scientific community through a data agreement. - The requester must be affiliated to a research institution.

This is a comprehensive dataset of 6,388 surgical patients composed of intraoperative biosignals and clinical information. The biosignal data included in the dataset is high quality data such as 500 Hz waveform signals and numeric values at intervals of 1-7 seconds. More than 60 surgery related clinical information is also provided to help interpret the signals. The dataset is provided free of charge to help researchers who want to study and develop new medical AI algorithms using monitoring signals from surgical patients. We expect that the distribution of this world's largest biosignal dataset will greatly contribute to the advancement of medical AI research.