cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.13026/96re-n451
title: Analysis of Clinical Text: Task 14 of SemEval 2015

description: SemEval (Semantic Evaluation) is an ongoing series of evaluations of
computational semantic analysis systems, organized under the umbrella of
SIGLEX, the Special Interest Group on the Lexicon of the Association for
Computational Linguistics. This project describes "Analysis of Clinical Text"
task of the International Workshop on Semantic Evaluation 2014 and 2015
(SemEval 2014 and 2015) [2,3,4,5]. The purpose of the task is to enhance
current research in natural language processing (NLP) methods used in the
clinical domain, and to introduce clinical text processing to the broader NLP
community. The task aims to combine supervised methods for text analysis with
unsupervised approaches for entity/acronym/abbreviation recognition and
mapping to Unified Medical Language System (UMLS) Concept Unique Identifiers
(CUIs). It also evaluated systems on the task of template filling [1], which
involves the population of eight attributes of the identified disorders with
their normalized values.
DOI: 10.13026/96v3-dw72
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: 10.13026/9egj-jg49
title: Shiraz University Fetal Heart Sounds Database

description: The Shiraz University (SU) fetal heart sounds database (SUFHSDB) contains
fetal and maternal phonocardiogram (PCG) recordings from 109 pregnant women in
single and twin pregnancies. The recordings were made at Hafez Hospital of
Shiraz University of Medical Sciences, Shiraz, Iran, from mothers aged from 16
to 47 years with a digital stethoscope placed on the lower maternal abdomen.
In total, 99 subjects had one signal recorded, three subjects had two and
seven cases of twins were recorded individually, resulting in 119 recordings.
The average duration of each record is about 90 seconds. The sampling rate was
generally 16,000 Hz with 16-bit quantization and a few recordings at 44,100
Hz. The data was recorded in wide-band mode of the digital stethoscope, with a
frequency response of 20 Hz to 1 kHz.
DOI: 10.13026/ag2y-yh44
title: Kinematic dataset of actors expressing emotions

description: We produced a kinematic dataset to assist in recognizing cues from all parts
of the body that indicate human emotions (happy, sad, angry, fearful, ,
disgust, surprise) and neutral. The present dataset was created using a
portable wireless motion capture system. Twenty-two semi-professional actors
(50% female) completed performances. A total of 1402 recordings at 125 Hz were
collected, consisting of the position and rotation data of 72 anatomical
nodes. We hope this dataset will contribute to multiple fields of research and
practice, including social neuroscience, psychiatry, computer vision, and
biometric and information forensics.
DOI: 10.13026/c4n5-3b04
title: NInFEA: Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research

description: The development of algorithms for the extraction of the foetal ECG (fECG) from
non-invasive recordings is hampered by the lack of publicly-available
reference datasets, which could be used to benchmark different algorithms
while providing a ground truth on the foetal heart activity when an invasive
scalp lead is unavailable. By enriching the electrophysiological recordings
with simultaneous multimodal signals, these datasets could also help the
investigation of the foetal cardiac physiology, providing ground truth for the
analysis in early pregnancy, when the fECG is not directly accessible.

The Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal
Cardiology Research (NInFEA) is the first open-access dataset featuring
simultaneous non-invasive electrophysiological recordings, fetal pulsed-wave
Doppler (PWD) and maternal respiration signals. The dataset includes 60
entries from 39 voluntary pregnant women, between the 21st and the 27th week
of gestation. Every entry is composed of 27 electrophysiological channels
(2048 Hz, 22 bits, acquired by means of the TMSi Porti7 system), maternal
respiration signal (through a resistive thoracic belt), synchronised foetal
trans-abdominal PWD and clinical annotations provided by expert clinicians at
the time of the signal collection.
DOI: 10.13026/c9gh-n952
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.
DOI: 10.13026/ckyh-jf25
title: Kinematic dataset of actors expressing emotions

description: We produced a kinematic dataset to assist in recognizing cues from all parts
of the body that indicate human emotions (happy, sad, angry, fearful, neutral,
and disgust). The present dataset was created using a portable wireless motion
capture system. Twenty-two semi-professional actors (50% female) completed
performances. A total of 1190 recordings at 125 Hz were collected, consisting
of the position and rotation data of 72 anatomical nodes. We hope this dataset
will contribute to multiple fields of research and practice, including social
neuroscience, psychiatry, computer vision, and biometric and information
forensics.
DOI: 10.13026/e4ej-gm40
title: Pulmonary Edema Severity Grades Based on MIMIC-CXR

description: Clinical management decisions for patients with acutely decompensated heart
failure and many other diseases are often based on grades of pulmonary edema
severity, rather than its mere absence or presence. Chest radiographs are
commonly performed to assess pulmonary edema. The MIMIC-CXR dataset that
consists of 377,110 chest radiographs with free-text radiology reports offers
a tremendous opportunity to study this subject.

This dataset is curated based on MIMIC-CXR, containing 3 metadata files that
consist of pulmonary edema severity grades extracted from the MIMIC-CXR
dataset through different means: 1) by regular expression (regex) from
radiology reports, 2) by expert labeling from radiology reports, and 3) by
consensus labeling from chest radiographs.  

This dataset aims to support the algorithmic development of pulmonary edema
assessment from chest x-ray images and benchmark its performance. The metadata
files have subject IDs, study IDs, DICOM IDs, and the numerical grades of
pulmonary edema severity. The IDs listed in this dataset have the same mapping
structure as in MIMIC-CXR.
DOI: 10.13026/gdxk-7395
title: MICRO Motion capture data from groups of participants standing still to auditory stimuli (2015)

description: This project contains head movement data recorded from groups of participants
asked to stand as still as possible and presented with a series of auditory
stimuli. Data was collected in units of mm with a Qualisys motion capture
system at 100Hz. Data was collected at the University of Oslo on March 12th
2015 from a total of 108 participants. Code to read and process these files
has been made publicly available.
DOI: 10.13026/h3k0-4982
title: Transformer models trained on MIMIC-III to generate synthetic patient notes

description: Natural Language Processing can help to unlock knowledge in the vast troves of
unstructured clinical data that are collected during patient care. Patient
confidentiality presents a barrier to the sharing and analysis of such data,
however, meaning that only small, fragmented and sequestered datasets are
available for research. To help side-step this roadblock, we explore the use
of Transformer models for the generation of synthetic notes. We demonstrate
how models trained on notes from the MIMIC-III clinical database can be used
to generate synthetic data with potential to support downstream research
studies. We release these trained models to the research community to
stimulate further research in this area.
DOI: 10.13026/hvnd-ex27
title: Is the normal heart rate chaotic?

description: In its June 2008 issue, the editors of [_Chaos_](http://chaos.aip.org/)
announced a new feature, "Controversial Topics in Nonlinear Dynamics". The
first controversial topic to be aired is "Is the Normal Heart Rate Chaotic?".
Since major impediments to achieving consensus on the statistical properties
of biologic time series have included the lack of open access time series,
PhysioNet has provided a set of 15 heart beat (RR-interval) time series in
health and disease (congestive heart failure and atrial fibrillation). Each
time series is about 24 hours long (roughly 100,000 intervals), and is
provided as a text file for analysis and as an image for visual review. All of
the time series were derived from continuous ambulatory (Holter)
electrocardiograms. We encourage participants in the Chaos Controversies issue
to use the 15 RR interval time series provided here as a common focus of
analysis.
DOI: 10.13026/j0y6-bw05
title: Annotated Question-Answer Pairs for Clinical Notes in the MIMIC-III Database

description: Clinical question answering (QA) (or reading comprehension) aims to
automatically answer questions from medical professionals based on clinical
texts. We release this dataset, which contains 1287 annotated QA pairs on 36
sampled discharge summaries from MIMIC-III Clinical Notes, to facilitate the
clinical question answering task. Questions in our dataset are either verified
or directly generated by clinical experts.

Note that the primary purpose of this dataset is to test the generalizability
of a QA model, i.e., whether a QA model that is trained on other datasets can
answer questions on this dataset (which may have a different distribution
compared with the training data), rather than to train a QA model. Hence the
scale of our annotations is relatively small compared to some existing QA
datasets.
DOI: 10.13026/jb5v-w976
title: National Institutes of Health Stroke Scale (NIHSS) Annotations for the MIMIC-III Database

description: The National Institutes of Health Stroke Scale (NIHSS) is a 15-item neurologic
examination stroke scale. It quantifies the physical manifestations of
neurological deficits and provides crucial support for clinical decision
making and early-stage emergency triage. NIHSS scores stored in the free-text
of Electronic Health Records (EHRs) often lack standardization and the
expression patterns are highly dependent on the habit of the clinicians. This
can limit the potential for reusability of the data.

There is benefit in developing robust algorithms to extract NIHSS scores from
the free-text of EHRs. We developed a dataset for NIHSS score identification,
a task defined as the extraction of scale items and corresponding scores from
discharge summaries. Discharge summaries of stroke cases in the Medical
Information Mart for Intensive Care III (MIMIC-III) database were used to
create an annotated NIHSS corpus.

Each discharge summary was manually annotated for the presence of NIHSS scores
by two annotators with backgrounds in medical informatics. Annotations include
all scale items (e.g. "4\. Facial Palsy"), the corresponding score
"measurement", and their relation "has value". The dataset is intended to
support academic and industrial research in the field of medical natural
language processing (NLP).
DOI: 10.13026/kc0e-6a32
title: Electrocardiogram, skin conductance and respiration from spider-fearful individuals watching spider video clips

description: This project contains electrocardiogram, skin conductance and respiration as
raw data (unfiltered, unprocessed) recorded from consented, spider-fearful
individuals using the wearable BITalino biosignal measurement device (PLUX -
Wireless Biosignals S.A., Lisbon, Portugal) with the sampling rate set to 100
Hz per channel having 10-bit resolution.

All subjects were introduced to the exposure procedure and rationale that is
mainly based on the principles of the one-session exposure treatment. The main
focus of this study was to investigate if the use of an HRV biofeedback
intervention could be a promising therapeutic add-on to exposure therapy for
specific phobias. Biosignals were collected at the Saarland University
(Germany) from July 2017 to July 2018, and there are 57 subjects in total.

The dataset may support studies on mental health and psychophysiology related
to other anxiety disorders, such as fear of flying, social phobia and
posttraumatic stress disorder.
DOI: 10.13026/m32v-hy93
title: Mental workload during n-back task captured by TransCranial Doppler (TCD) sonography and functional Near-Infrared Spectroscopy (fNIRS) monitoring

description: The authors used this dataset to assess the neurovascular coupling responses
evoked by a cognitive paradigm with the aid of TransCranial Doppler (TCD)
sonography and functional Near-InfraRed Spectroscopy (fNIRS). Fourteen healthy
young individuals were recruited for this study conducted in 2018 at the
University of Oklahoma, Translational Geroscience Laboratory (age: 31±5.94
years, BMI: 24.9±2.95). The following physiological signals were measured non-
invasively during the examination: relative concentration of oxy- and
deoxyhemoglobin (HbO and HbR, respectively), and cerebral Blood Flow Velocity
(BFV) in the middle cerebral arteries for the entire duration of the testing
(~11 minutes).  In this study, fNIRS signals from the frontal cortex were
acquired by a NIRScout platform (NIRx Medical Technologies LLC, NY, USA) at a
sampling frequency of 3.9 Hz,  HbO and HbR time series signals can be obtained
by applying the differential modified Beer Lambert Law on the preprocessed
records of near-infrared intensities. BFV was recorded using TCD sonography
(Digi-Lite, Rimed, Raanana, Israel) by placing 2 Mhz ultrasound probes over
the left and right temporal acoustic windows. The paradigm used for cognitive
testing was an n-back task where the participant was administered a series of
alphabetical letters on the computer screen and had to provide a response
whenever a stimulus is presented that is the same as the one presented _n_
trials previously.  The examination protocol consisted of the following 134
second sessions: 0-back, 1-back, 0-back, and 2-back. This dataset was
collected for the following study: "Increased Cognitive Workload Evokes
Greater Neurovascular Coupling Responses in Healthy Young Adults".
DOI: 10.13026/m34x-fq90
title: Transformer models trained on MIMIC-III to generate synthetic patient notes

description: Natural Language Processing can help to unlock knowledge in the vast troves of
unstructured clinical data that are collected during patient care. Patient
confidentiality presents a barrier to the sharing and analysis of such data,
however, meaning that only small, fragmented and sequestered datasets are
available for research. To help side-step this roadblock, we explore the use
of Transformer models for the generation of synthetic notes. We demonstrate
how models trained on notes from the MIMIC-III clinical database can be used
to generate synthetic data with potential to support downstream research
studies. We release these trained models to the research community to
stimulate further research in this area.
DOI: 10.13026/mn2d-nq25
title: HiRID, a high time-resolution ICU dataset

description: HiRID is a freely accessible critical care dataset containing data relating to
almost 34 thousand patient admissions to the Department of Intensive Care
Medicine of the Bern University Hospital, Switzerland (ICU), an
interdisciplinary 60-bed unit admitting >6,500 patients per year. The ICU
offers the full range of modern interdisciplinary intensive care medicine for
adult patients. The dataset was developed in cooperation between the Swiss
Federal Institute of Technology (ETH) Zurich, Switzerland and the ICU.

The dataset contains de-identified demographic information and a total of 681
routinely collected physiological variables, diagnostic test results and
treatment parameters from almost 34 thousand admissions during the period from
January 2008 to June 2016. Data is stored with a uniquely high time resolution
of one entry every two minutes.
DOI: 10.13026/nv8n-1164
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/qweb-sr17
title: Lobachevsky University Electrocardiography Database

description: Lobachevsky University Electrocardiography Database (LUDB) is an ECG signal
database with marked boundaries and peaks of P, T waves and QRS complexes. The
database consists of 200 10-second 12-lead ECG signal records representing
different morphologies of the ECG signal. The ECGs were collected from healthy
volunteers and patients of the Nizhny Novgorod City Hospital No 5 in
2017-2018\. The patients had various cardiovascular diseases while some of
them had pacemakers. The boundaries of P, T waves and QRS complexes were
manually annotated by cardiologists for all 200 records. Also, each record is
annotated with the corresponding diagnosis. The database can be used for
educational purposes as well as for training and testing algorithms for ECG
delineation, i.e. for automatic detection of boundaries and peaks of P, T
waves and QRS complexes.
DOI: 10.13026/rbeh-9r20
title: Cerebral perfusion and cognitive decline in type 2 diabetes

description: This dataset was collected as part of a study to explore vasoregulation and
blood flow in patients with type 2 Diabetes mellitus. It comprises cerebral
blood, autonomic functions, laboratory and gait variables in 70 patients with
type 2 DM and 70 healthy controls (50-85 years old). Diabetes mellitus is a
prevalent, costly condition with high morbidity and mortality that affects
nearly 6.3% of the population in the United States. Diabetes mellitus alters
the permeability of the blood-brain barrier, thus affecting regional
metabolism and microcirculatory regulation. Predictors of cerebrovascular
complications of diabetes that are evidence-based on cerebral blood flow, MRI
imaging, and cognitive testing are lacking.

Blood flow velocities were measured in the anterior and middle cerebral
arteries using transcranial Doppler ultrasound. Cerebral vasoregulation was
evaluated by comparing blood flow velocity responses during hypocapnia and
hypercapnia, Valsalva maneuver, head-up tilt and sit-to-stand test using
simultaneous recordings of cardiovascular variables, blood flow velocity in
the anterior and middle cerebral artery using transcranial Doppler and
respiratory variables. We applied continuous arterial spin labeling at 3 Tesla
MRI to evaluate regional distribution of cerebral blood flow and vasomotor
reactivity to CO2. Cognitive and executive functions were assessed via a
battery of psychometric measures. Gait and balance were characterized from
dynamics of foot pressure distribution and center of pressure displacement.
DOI: 10.13026/rxa7-q798
title: ShAReCLEF eHealth 2013: Natural Language Processing and Information Retrieval for Clinical Care

description: This is the pilot 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.

The ShARe/CLEF eHealth 2013 Challenge has three tasks: Task 1 and Task 2
involve annotation of entities in a set of narrative clinical reports; Task 3
involves retrieval of web pages based on queries generated when reading the
clinical reports. The datasets for Tasks 1 and 2 comprise de-identified
clinical free-text notes from the MIMIC-II database (version 2.5). The dataset
for Task 3 consists of a set of medical-related documents provided by the
Khresmoi E.U. Consortium. This project provides data files for Task 1 and Task
2 only.