cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.13026/c29h4k
title: Sleep Bioradiolocation Database

description: The database contains 32 records of non-contact sleep monitoring by a bioradar. The records are accompanied by results of sleep scoring, based on polysomnography according to the rules of the American Academy of Sleep Medicine.
DOI: 10.13026/c29m1g
title: A Comparative Analysis of Sepsis Identification Methods in an Electronic Database

description: This repository hosts code and data corresponding to the article "A Comparative Analysis of Sepsis Identification Methods in an Electronic Database." published in Critical Care Medicine, PMID: 29303796, PMCID: PMC5851804.
DOI: 10.13026/c2d016
title: ECG effects of Dofetilide, Moxifloxacin, Dofetilide+Mexiletine, Dofetilide+Lidocaine and Moxifloxacin+Diltiazem in Healthy Subjects

description: The ECGDMMLD database contains data from a randomized, double-blind, 5-period crossover clinical trial in 22 healthy male and female subjects, 18 to 35 years of age, to compare the electrophysiological response of hERG potassium channel blocking drugs with and without the addition of late sodium or calcium channel blocking drugs. The 5 treatment periods are 1) dofetilide alone, 2) mexiletine with and without dofetilide, 3) lidocaine with and without dofetilide, 4) moxifloxacin with and without diltiazem and 5) placebo. During each treatment period, 12 blood samples for pharmacokinetic measurements are obtained with matched 12-lead ECG recordings.
DOI: 10.13026/c2d097
title: Effect of Deep Brain Stimulation on Parkinsonian Tremor

description: The recordings of this database are of rest tremor velocity in the index finger of 16 subjects with Parkinson's disease (PD) who receive chronic high frequency electrical deep brain stimulation (DBS) either uni- or bi-laterally within one of three targets: Vim = the ventro-intermediate nucleus of the thalamus (n=3); GPi = the internal Globus pallidus (n=7); and STN = the subthalamic nucleus (n=6).
DOI: 10.13026/c2dw96
title: Cerebral Vasoregulation in Elderly with Stroke

description: This database contains multimodal data from a large study investigating the effects of ischemic stroke on cerebral vasoregulation. The cross sectional study compared 60 subjects who suffered strokes, to 60 control subjects, collecting the following data for each patient across multiple days: transcranial doppler of cerebral arteries, 24-h blood pressure numerics, high resolution waveforms (ECG, blood pressure, CO2 and respiration) during various movement tasks, 24-h ECG, EMG, and accelerometer recordings, and gait pressure recordings during a walking test.
DOI: 10.13026/c2f66g
title: CUILESS2016

description: The Concept Unique Identifier (CUI)-less database contains a corpus of "CUI-less" concepts taken from the SemEval2015 Task 14 that have been assigned CUIs. The annotation process allows assignment of CUIS from any Unified Medical Language System (UMLS) semantic group and compositional normalization using more than one CUI per disease entity. Concepts are mapped to SNOMED CT as represented in the September 2016 version found in Unified Medical Language System (UMLS) 2016AB.
DOI: 10.13026/c2g66s
title: Shiraz University Fetal Heart Sounds Database

description: The Shiraz University (SU) fetal heart sounds database (SUFHSDB) contains fetal and maternal PCG recordings from 109 pregnant women.
DOI: 10.13026/c2jq1p
title: EEG During Mental Arithmetic Tasks

description: The database contains EEG recordings of subjects before and during the performance of mental arithmetic tasks. The participants were eligible to enroll in the study if they had normal or corrected-to-normal visual acuity, normal color vision, had no clinical manifestations of mental or cognitive impairment, verbal or non-verbal learning disabilities. Exclusion criteria were the use of psychoactive medication, drug or alcohol addiction and psychiatric or neurological complaints.
DOI: 10.13026/c2qq2m
title: Preterm Infant Cardio-Respiratory Signals Database

description: The Preterm Infant Cardio-respiratory Signals (PICS) database contains simultaneous ECG and respiration recordings of ten preterm infants collected from the Neonatal Intensive Care Unit (NICU) of the University of Massachusetts Memorial Healthcare. Statistical features based on linear estimates of heart rate are used to predict episodes of bradycardia.
DOI: 10.13026/c2th47
title: Pattern Analysis of Oxygen Saturation Variability

description: This database contains one hour oxygen saturation measurements of 36 patients, used for the analysis of oxygen saturation variability.
DOI: 10.13026/c2w01j
title: Tai Chi, Physiological Complexity, and Healthy Aging—Gait (taichidb)

description: The "Tai Chi, Physiological Complexity, and Healthy Aging—Gait" database contains two-channel gait recordings of 87 older adults from the Greater Boston area.
DOI: 10.13026/c2zw95
title: MMG Database
DOI: 10.13026/gxmm-es70
title: eICU Collaborative Research Database Demo

description: This project is a demo version of the eICU Collaborative Research Database, a
multi-center database comprised of deidentified health data for over 200,000
admissions to ICUs across the United States between 2014-2015. The database
includes vital sign measurements, care plan documentation, severity of illness
measures, diagnosis information, and treatment information. Data is collected
through the Philips eICU program, a critical care telehealth program that
delivers information to caregivers at the bedside. This open access demo
allows researchers to ascertain whether the eICU Collaborative Research
Database is suitable for their work. It includes over 2,500 unit stays
selected from 20 of the larger hospitals in the eICU Collaborative Research
Database.
DOI: 10.13026/jc2a-ca12
title: Deidentified Medical Text

description: Available here is a gold standard corpus of 2,434 nursing notes that have been thoroughly deidentified by a multi-pass process that included meticulous reviews by three or more experts working independently, as well as by a variety of automated methods. All detected instances of PHI in these nursing notes have been replaced by realistic surrogate data. The gold standard corpus is currently available only to those who have been granted access to PhysioNet Clinical Databases.
DOI: 10.13026/v217-zr73
title: MIMIC-III Waveform Database Matched Subset

description: The MIMIC-III Waveform Database Matched Subset contains 22,317 waveform
records, and 22,247 numerics records, for 10,282 distinct ICU patients. These
recordings typically include digitized signals such as ECG, ABP, respiration,
and PPG, as well as periodic measurements such as heart rate, oxygen
saturation, and systolic, mean, and diastolic blood pressure.

This database is a subset of the [MIMIC-III Waveform
Database](/content/mimic3wdb/), representing those records for which the
patient has been identified, and their corresponding clinical records are
available in the [MIMIC-III Clinical Database](/content/mimiciii/).

  *[ICU]: Intensive care unit
  *[ECG]: Electrocardiogram
  *[ABP]: Arterial blood pressure
  *[PPG]: Photoplethysmogram
DOI: 10.13026/0pzc-dm64
title: eICU Collaborative Research Database

description: The eICU Collaborative Research Database is a multi-center database comprising
deidentified health data associated with over 200,000 admissions to ICUs
across the United States between 2014-2015. The database includes vital sign
measurements, care plan documentation, severity of illness measures, diagnosis
information, and treatment information. Data is collected through the Philips
eICU program, a critical care telehealth program that delivers information to
caregivers at the bedside.
DOI: 10.13026/0zgk-9j94
title: ShAReCLEF eHealth Evaluation Lab 2014 (Task 2): Disorder Attributes in Clinical Reports

description: This project focuses on Task 2 of the 2014 ShARe/CLEF eHealth evaluation lab,
which extended Task 1 of the 2013 ShARe/CLEF eHealth evaluation lab by
focusing on identification and normalization of disorder attributes. The task
comprises two subtasks: attribute normalization (task 2a) and cue
identification (task 2b). We instructed participants to develop a system which
either kept or updated a default attribute value for each task. Participant
systems were evaluated against a blind reference standard of 133 discharge
summaries using Accuracy (task 2a) and F-score (task 2b).

This is the second year of the ShARe/CLEF eHealth Evaluation Lab, a shared
task focused on natural language processing (NLP) and information retrieval
(IR) for clinical care. The task is co-organized by the Shared Annotated
Resources (ShARe) project and the CLEF Initiative (Conference and Labs of the
Evaluation Forum, formerly known as Cross-Language Evaluation Forum). The
vision of ShARe/CLEF is two-fold: (1) to develop tasks that potentially impact
patient understanding of medical information and (2) to provide the community
with an increasingly sophisticated dataset of clinical narrative to advance
the state-of-the-art in Natural Language Processing, Information Extraction
and Information Retrieval in healthcare.
DOI: 10.13026/1r5h-4p36
title: Clinical data from the MIMIC-II database for a case study on indwelling arterial catheters:

description: This dataset was created for the purpose of a case study in the book: [
_Secondary Analysis of Electronic Health
Records_](https://link.springer.com/book/10.1007/978-3-319-43742-2) ,
published by Springer in 2016. In particular, the dataset was used throughout
[Chapter 16 (Data Analysis)
](https://link.springer.com/chapter/10.1007/978-3-319-43742-2_16)by Raffa J.
et al. to investigate the effectiveness of indwelling arterial catheters in
hemodynamically stable patients with respiratory failure for mortality
outcomes. The dataset is derived from MIMIC-II, the publicly-accessible
critical care database. It contains summary clinical data and  outcomes for
1,776 patients.
DOI: 10.13026/2grc-ny75
title: Smartphone-Captured Chest X-Ray Photographs

description: Recent research has applied deep learning imaging techniques to detect
pulmonary pathology in chest X-rays (CXR) and achieved performance comparable
with radiologists on specific lung abnormalities. Industry has managed to
deploy such artificial intelligence (AI) solutions in on mobile devices to
help clinicians improve their workflow. However, the feasibility of applying
these modern AI algorithms to CXR photographs has yet to be evaluated. In this
project, 6,453 smartphone photographs were taken from the frontal-view CXR
images from two publicly available databases, MIMIC-CXR and CheXpert. We
constructed four derivative CXR photograph datasets ( _Photo-MMC,  Photo-CXP,
Photo-MED, and Photo-DEV_) including photographs taken by several resident
doctors and photographs taken with different devices.
DOI: 10.13026/2mz7-3v18
title: Wilson Central Terminal ECG Database

description: The Wilson Central Terminal ECG Database (WCTECGdb) contains the ** ** Wilson
Central Terminal (WCT), the voltage of right arm (RA), left arm (LA) and left
leg (LL), six unipolar chest leads associated with three limb leads and six
precordial leads.

With our ECG device, the potentials of RA, LA, LL and six true unipolar leads
are measured using the _right leg_ as the reference point.  Having this
configuration, we have been able to overcome the difficulty of recording the
WCT in a clinical setting. Our data confirmed once again that the WCT does not
have a small value, and could be as high as 241% of lead II and has standard
ECG characteristics such as the p-wave and the t-wave. The dataset comprises
of 540 ten-second segments recorded from 92 patients.
DOI: 10.13026/4h9d-gf10
title: MICRO Motion capture data from groups of participants standing still to auditory stimuli (2012)

description: This dataset comprises head motion observations collected as part of an
experiment in which a group of people were asked to stand still for 6 minutes,
with the first 3 minutes in silence, followed by 3 minutes with music. Head
motion was captured in units of mm at 100Hz using a Qualisys infra-red optical
system. The experiment was carried out at the University of Oslo on March 8th
2012 from a total of 113 participants. Code to read and process these files is
available. The paper corresponding to the work is Jensenius et al., "The
Musical Influence on People's Micromotion when Standing Still in Groups",
Proceedings of the 14th Sound and Music Computing Conference (2017).
DOI: 10.13026/4nen-gv40
title: What's in a Note? Unpacking Predictive Value in Clinical Note Representations

description: Unlocking the full potential of clinical notes within electronic health
records presents a challenge for large scale, computational analyses. Many
studies that incorporate information from clinical notes do so with simple
word-matching approaches and a focused goal of improving performance in a
specific task such as mortality prediction. Our project explores
representation strategies for clinical text and how to combine many words into
larger document-level representations. The code and model shared here are
supplements to the AMIA 2018 Informatics Summit paper of the same name.
DOI: 10.13026/50yh-tt09
title: Motion and heart rate from a wrist-worn wearable and labeled sleep from polysomnography

description: This project contains acceleration (in units of _g_ ) and heart rate (bpm,
measured from photoplethysmography) recorded from the Apple Watch, as well as
labeled sleep scored from gold-standard polysomnography. Data were collected
at the University of Michigan from June 2017 to March 2019, and there are 31
subjects in total. Code to read and process these files is available on
GitHub. The paper corresponding to the work is Walch et al., "Sleep stage
prediction with raw acceleration and photoplethysmography heart rate data
derived from a consumer wearable device", SLEEP (2019).
DOI: 10.13026/530d-1e61
title: WiDS (Women in Data Science) Datathon 2020: ICU Mortality Prediction

description: **WiDS (Women in Data Science)**   **Datathon 2020: ICU Mortality Prediction**
focuses on patient health through data from [MIT's GOSSIS (Global Open Source
Severity of Illness Score)](https://gossis.mit.edu/) initiative. Brought to
you by the Global WiDS team, the [West Big Data Innovation
Hub](https://westbigdatahub.org/), and the [WiDS Datathon
Committee](https://www.widsconference.org/committee-2020.html), this year's
datathon is launching on Kaggle:
[bit.ly/WiDSdatathon2020kaggle](https://bit.ly/WiDSdatathon2020kaggle).
DOI: 10.13026/5889-jj86
title: Permittivity of Healthy and Diseased Skeletal Muscle

description: A better understanding of the permittivity of skeletal muscle is essential for
the development of new diagnostic tools and approaches for neuromuscular
evaluation. However, there remain important knowledge gaps in our
understanding of this property in healthy and diseased skeletal muscle, which
hinder its translation into clinical application. The aim of this project is
to provide a platform for the researchers to contribute and share the
dissemination of this property in healthy and diseased muscle. Ultimately, the
normative data reported will offer the scientific community the opportunity to
improve the accuracy of existing techniques as well as developing new
diagnostic tools and therapies.
DOI: 10.13026/5bhj-nf42
title: 2018 IEEE BHI and BSN Data Challenge

description: In collaboration with the IEEE Conference on Biomedical and Health Informatics
(BHI) 2018 and the IEEE Conference on Body Sensor Networks (BSN), we are
hosting a challenge to explore real clinical questions in critically ill
patients using the MIMIC-III database. Participants in the challenge will be
invited to present at the BHI & BSN Annual Conference in Las Vegas, USA (4-7
March 2018): <https://bhi-bsn.embs.org/2018/>
DOI: 10.13026/6jx9-cy34
title: MIMIC-III - SequenceExamples for TensorFlow modeling

description: This dataset contains TensorFlow SequenceExamples derived from patient records
in MIMIC-III, a freely available set of deidentified medical records from
critical care patients at Beth Israel Deaconess Medical Center. Each
SequenceExample converts data from an individual patient encounter and any
previous encounters into a set of timestamped "feature lists" describing the
patient history up to a certain time, beyond which predictions can be made.
These data are suitable for direct input into TensorFlow modeling pipelines,
and include labels for inpatient mortality and discharge diagnosis codes for
each encounter. The intent of this release is to provide a preprocessed,
ready-to-use version of MIMIC-III to support and enable reproducible machine
learning research for electronic health records.
DOI: 10.13026/7br9-xy10
title: Brain Hemorrhage Extended (BHX): Bounding box extrapolation from thick to thin slice CT images

description: BHX is a public available dataset with bounding box annotations for 5 types of
acute hemorrhage as an extension of the qure.ai CQ500 dataset. This dataset
intends to provide data resources to help advance hemorrhage detection towards
machine learning localization tasks.

**Key Points:  **

  * BHX contains up to 39,668 bounding boxes in 23,409 images annotated for hemorrhage, out of a total of ~170k images from qure.ai CQ500 dataset. 

  * The dataset was built through an efficient method to obtain automatic annotated images (thin slices) from sparse initial labeling (thick slices).

  * We hope this dataset can contribute to further develop and validate machine learning algorithms, ultimately helping to diagnose intracranial hemorrhage.
DOI: 10.13026/8zq3-b355
title: Gout Emergency Department Chief Complaint Corpora

description: The Gout Emergency Department Chief Complaint Corpora (GED3C) consists of 2
corpora of free text triage nurse chief complaints (up to 282 characters in
length) collected from 2019 to 2020 at an academic medical center in the Deep
South. The smaller corpus "GOUT-CC-2019-CORPUS" consists of 300 chief
complaints from 2019 selected by the presence of the keyword "gout". The
larger corpus "GOUT-CC-2020-CORPUS" contains 8037 chief complaints collected
from a single month in 2020. No other selection criteria (gout or otherwise)
was used to generate GOUT-CC-2020-CORPUS, making this corpus representative
for medical conditions of interest in the underlying urban Black majority
Emergency Department patient population.

We anticipate this corpus being useful in the development of Emergency
Department alerting algorithms for gout. Both GOUT-CC-2019-CORPUS and GOUT-
CC-2020-CORPUS were annotated with respect to predicted gout flare status as
determined retrospectively by manual review of the chief complaint. A subset
of patients with these chief complaints underwent chart review by
rheumatologists to verify gout flare status guided by the Gaffo criteria.
These corpora may be useful to researchers at other institutions who want to
develop or validate existing alerting algorithms at a 2nd institution. These
corpora are also available for use in masked language model development. Its
terse, abbreviation-rich content sets it apart from more lengthy clinical text
and to our knowledge this is the only chief complaint corpus publicly
available.
DOI: 10.13026/9pk3-y617
title: MedNLI for Shared Task at ACL BioNLP 2019

description: Natural language inference (NLI) is the task of determining whether a given
hypothesis can be inferred from a given premise. Also known as Recognizing
Textual Entailment (RTE), this task has enjoyed popularity among researchers
for a long time. However, almost all datasets for this task focused on open
domain data such as as news texts, blogs, and so on. To address this gap, the
[MedNLI](http://doi.org/10.13026/C2RS98) dataset was created or language
inference in the medical domain. MedNLI was a derived dataset with data
sourced from [MIMIC-III](http://doi.org/10.13026/C2XW26) v1.4. In order to
stimulate research for this problem, the
[MEDIQA](https://aclweb.org/aclwiki/BioNLP_Workshop#MEDIQA_2019) shared task
has been organized at BioNLP 2019. The dataset provided herein is a test set
of 405 premise hypothesis pairs for the NLI challenge at the MEDIQA shared
task. Participants of the shared task are expected to use the MedNLI data for
development of their models and this dataset will be used as an unseen dataset
for scoring each participant submission.
DOI: 10.13026/9q68-n048
title: Visceral adipose tissue measurements during pregnancy

description: This database is from a cohort study of pregnant women up to 20 weeks of
pregnancy and followed until delivery. Measurement of maternal visceral
adipose tissue (VAT) was performed during routine obstetric ultrasound. At
this time, the biometric evaluation was also performed, and information
obtained from prenatal care. Gestational outcomes, including gestational
diabetes mellitus (GDM), were obtained through evaluating the patients'
medical records at the hospitals where they took place. The data was collected
as part of a study that sought to evaluate if maternal VAT could predict GDM
at time of delivery. Variables included in this dataset are maternal age,
previous DM, blood pressure (in the same day of VAT measurement), VAT (in the
periumbilical region), gestational age at time of inclusion, number of
pregnancies, level of first fasting glucose, pre-gestational body mass index
(BMI). Also, the pregnancy outcomes: gestational age at birth, type of
delivery (vaginal or caesarean section), child birth weight and the diagnosis
of GDM.
DOI: 10.13026/bcbd-4t11
title: Paediatric Intensive Care database

description: The Paediatric Intensive Care (PIC) database is a large paediatric-specific, single-center, bilingual database comprising information relating to children admitted to critical care units at a large children’s hospital in China. The database is de-identified and includes vital sign measurements, medications, laboratory measurements, fluid balance recordings, diagnostic codes, demographic information, and more. The data are publicly available after credentialing, which includes completion of a training course on research with human subjects and signing of a data use agreement mandating responsible handling of the data and adherence to the principle of collaborative research. The PIC database builds upon the success of the widely used MIMIC (Medical Information Mart for Intensive Care) database, and extends the approach into the field of paediatric critical care. The database has many unique characteristics which support academic and industrial research including the development of machine learning algorithms, clinical decision support tools, quality improvement initiatives, and international data sharing.