cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.13026/bn1x-my19
title: Safety and Preliminary Efficacy of Intranasal Insulin for Cognitive Impairment in Parkinson Disease and Multiple System Atrophy

description: A double-blinded placebo-controlled pilot study in parkinson disease and
multiple system atrophy.

Parkinson disease (PD) is associated with cognitive impairment. We aimed to
determine the effects of intranasal insulin (INI) on cognition and motor
performance in PD. This was a proof of concept, randomized, double-blinded,
placebo-controlled trial evaluating the effects of 40 international units (IU)
of insulin or saline once daily for four weeks on cognitive and functional
performance. Of 16 subjects enrolled, eight in the INI group and six in the
placebo group completed verbal fluency (FAS), Unified Parkinson Disease Scale
(UPDRS), and modified Hoehn and Yahr scale (HY, PD severity) at baseline and
post-treatment and were included in the analyses. After treatment, the INI
group had a better total FAS score (p=0.02) (41 ± 8.2 vs. 30.8 ± 7.1, mean ±SD
_,_ __ p=0.02) compared to the placebo group. The INI group also had improved
HY (p=0.04) and UPDRS-Motor (Part III) (p=0.02) scores when compared to
baseline. One INI treated patient with multiple system atrophy (MSA) remained
stable and did not show disease progression. The placebo group had no change.
INI administration was well tolerated and there were no hypoglycemic episodes
or serious study related adverse events or medications interactions.

INI is safe in PD and MSA patients and may provide clinically relevant
functional improvement. Larger studies are warranted to determine the INI
effect in treatment of cognitive and motor impairment in Parkinson disease.
NCT02064166.
DOI: 10.13026/c28679
title: Long-term Recordings of Gait Dynamics
DOI: 10.13026/c6kh-4h33
title: MedNLI - A Natural Language Inference Dataset For The Clinical Domain

description: State of the art models using deep neural networks have become very good in
learning an accurate mapping from inputs to outputs. However, they still lack
generalization capabilities in conditions that differ from the ones
encountered during training. This is even more challenging in specialized, and
knowledge intensive domains, where training data is limited. To address this
gap, we introduce MedNLI - a dataset annotated by doctors, performing a
natural language inference task (NLI), grounded in the medical history of
patients. As the source of premise sentences, we used the MIMIC-III. More
specifically, to minimize the risks to patient privacy, we worked with
clinical notes corresponding to the deceased patients. The clinicians in our
team suggested the Past Medical History to be the most informative section of
a clinical note, from which useful inferences can be drawn about the patient.
DOI: 10.13026/c8ax-aq77
title: Cerebral Vasoregulation in Diabetes

description: This observational study evaluated the effects of type 2 diabetes on cerebral
vasoregulation and functional outcomes, measured by blood flow responses to
hypocapnia and hypercapnia, Valsalva maneuver, head-up tilt, and sit-to-stand
test. This dataset contains 37 diabetic participants and 49 controls (aged  55
to 75 years) with continuous measurements of cerebral blood flow using
transcranial Doppler and MRI, heart rate, blood pressure, and respiratory
parameters, balance, walking, laboratory and retinopathy measures.
DOI: 10.13026/f73z-an96
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/g1kq-sc84
title: Computed Tomography Images for Intracranial Hemorrhage Detection and Segmentation

description: After traumatic brain injury (TBI), intracranial hemorrhage (ICH) may occur
that could lead to death or disability if it is not accurately diagnosed and
treated in a time-sensitive procedure. Currently, Computerized Tomography (CT)
scans are examined by radiologists to diagnose intracranial hemorrhage to
localize affected regions. In this work, we collected a dataset of 82 CT scans
of patients with traumatic brain injury. Intracranial hemorrhage regions in
these scans were delineated in each slice by two radiologists. The
radiologists also annotated each CT slice for the presence of different types
of intracranial hemorrhage and fracture.
DOI: 10.13026/gs83-bd50
title: MIMIC-III Waveform Database

description: The MIMIC-III Waveform Database contains 67,830 record sets for approximately
30,000 ICU patients. Almost all record sets include a waveform record
containing digitized signals (typically including ECG, ABP, respiration, and
PPG, and frequently other signals) and a "numerics" record containing time
series of periodic measurements, each presenting a quasi-continuous recording
of vital signs of a single patient throughout an ICU stay (typically a few
days, but many are several weeks in duration). A [subset of this
database](/content/mimic3wdb-matched/) contains waveform and numerics records
that have been matched and time-aligned with [MIMIC-III Clinical
Database](/content/mimiciii/) records.

  *[ICU]: Intensive care unit
  *[ECG]: Electrocardiogram
  *[ABP]: Arterial blood pressure
  *[PPG]: Photoplethysmogram
DOI: 10.13026/gtv4-g455
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 some 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 dataset was created for language inference in the medical domain. MedNLI is a derived dataset with data sourced from MIMIC-III v1.4. In order to stimulate research for this problem, a shared task on Medical Inference and Question Answering (MEDIQA) was organized at the workshop for biomedical natural language processing (BioNLP) 2019. The dataset provided herein is a test set of 405 premise hypothesis pairs for the NLI challenge in the MEDIQA shared task. Participants of the shared task are expected to use the MedNLI data for development of their models and this dataset was used as an unseen dataset for scoring each participant submission.
DOI: 10.13026/hdza-eh80
title: AMR-UTI: Antimicrobial Resistance in Urinary Tract Infections

description: AMR-UTI is a freely accessible dataset, derived from electronic health record
(EHR) information on over 80,000 patients with urinary tract infections (UTI)
treated at Massachusetts General Hospital and Brigham & Women's Hospital in
Boston, MA, USA between 2007 and 2016.

Each observation in the dataset corresponds to a urine specimen sent to the
clinical microbiology laboratory to assess for antimicrobial resistance (AMR).
Each observation includes (1) the antimicrobial susceptibility profile, (2)
the empiric antibiotic treatment decision, which is made without knowledge of
the susceptibility testing results, and (3) patient and specimen features
useful for prediction of AMR.
DOI: 10.13026/hmhs-py35
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/jbmn-w042
title: MIMIC-III Clinical Database Demo

description: MIMIC-III is a large, freely-available database comprising deidentified
health-related data associated with over 40,000 patients who stayed in
critical care units of the Beth Israel Deaconess Medical Center between 2001
and 2012 [1]. The MIMIC-III Clinical Database is available on PhysioNet (doi:
[10.13026/C2XW26](http://dx.doi.org/10.13026/C2XW26)). Though deidentified,
MIMIC-III contains detailed information regarding the care of real patients,
and as such requires credentialing before access. To allow researchers to
ascertain whether the database is suitable for their work, we have manually
curated a demo subset, which contains information for 100 patients also
present in the MIMIC-III Clinical Database. Notably, the demo dataset does not
include free-text notes.
DOI: 10.13026/krfm-4167
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/m40k-4758
title: Cerebral Vasoregulation in Diabetes

description: This observational study evaluated the effects of type 2 diabetes on cerebral vasoregulation and functional outcomes, measured by blood flow responses to hypocapnia and hypercapnia, Valsalva maneuver, head-up tilt, and sit-to-stand test. This dataset contains 37 diabetic participants and 49 controls (aged  55 to 75 years) with continuous measurements of cerebral blood flow using transcranial Doppler and MRI, heart rate, blood pressure, and respiratory parameters, balance, walking, laboratory and retinopathy measures.
DOI: 10.13026/p19w-dp86
title: PhysioZoo - mammalian NSR databases

description: The PhysioZoo database contains electrocardiographic recordings (ECG) taken
from multiple types of mammals (dogs, rabbits, mice). Each record is provided
in 3 different formats: MATLAB (.mat), simple text (.txt), PhysioNet
(.hea,.dat,.qrs). For each record the following data is available: the raw ECG
signal, reference (human corrected) R-peak annotations and the signal quality
annotations. All of the files formats are compatible with the PhysioZoo open
source software available at physiozoo.com. When the recordings were taken,
all the mammals were conscious, and no drugs were administered prior to the
recordings.
DOI: 10.13026/q0wp-m823
title: Phenotype Annotations for Patient Notes in the MIMIC-III Database

description: A crucial step within secondary analysis of electronic health records (EHRs)
is to identify the patient cohort under investigation. While EHRs contain
medical billing codes that aim to represent the conditions and treatments
patients may have, much of the information is only present in the patient
notes. Therefore, it is critical to develop robust algorithms to infer
patients' conditions and treatments from their written notes.

We introduce a dataset for patient phenotyping, a task that is defined as the
identification whether a patient has a given phenotype (also referred to as
indication) based on their patient note. Patient notes of MIMIC-III, a dataset
collected from Intensive Care Units of a large tertiary care hospital in
Boston, were manually annotated for the presence of several high-context
phenotypes relevant to treatment and risk of re-hospitalization.

Each note has been annotated by two expert human annotators (one clinical
researcher and one resident physician). Annotated phenotypes include treatment
non-adherence, chronic pain, advanced/metastatic cancer, as well as 10 other
phenotypes. This dataset can be utilized for academic and industrial research
in medicine and computer science, particularly within the field of medical
natural language processing.
DOI: 10.13026/qy76-9178
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/rma9-zb29
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/wrn5-3v80
title: Wilson Central Terminal ECG Database

description: We present a unique dataset which 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: 10.13026/05td-jn37
title: Influence of the MHD effect on 12-lead and 3-lead ECGs recorded in 1T to 7T MRI scanners

description: This ECG dataset was acquired in different magnetic resonance imaging (MRI)
scanners to study the magnetohydrodynamic (MHD) effect. The MHD effect, which
is caused an interaction of the MRI's strong static magnetic field and the
patient's blood flow, superimposes the ECG signal during MRI exams. As a
consequence, a detailed morphological analysis of the ECG (e.g. of the P wave,
ST segment or the T wave) is prevented. The MHD effect might be a useful
signal for extracting further physiological information, e.g. about blood flow
or stroke volume. The 12-lead and 3-lead ECG signals were acquired from
different subjects in various MRI scanners with magnetic field strengths
ranging from 1T up to 7T. All signals were manually annotated.
DOI: 10.13026/2572-db54
title: Safety and Preliminary Efficacy of Intranasal Insulin for Cognitive Impairment in Parkinson Disease and Multiple System Atrophy

description: A double-blinded placebo-controlled pilot study in parkinson disease and
multiple system atrophy.

Parkinson disease (PD) is associated with cognitive impairment. We aimed to
determine the effects of intranasal insulin (INI) on cognition and motor
performance in PD. This was a proof of concept, randomized, double-blinded,
placebo-controlled trial evaluating the effects of 40 international units (IU)
of insulin or saline once daily for four weeks on cognitive and functional
performance. Of 16 subjects enrolled, eight in the INI group and six in the
placebo group completed verbal fluency (FAS), Unified Parkinson Disease Scale
(UPDRS), and modified Hoehn and Yahr scale (HY, PD severity) at baseline and
post-treatment and were included in the analyses. After treatment, the INI
group had a better total FAS score (p=0.02) (41 ± 8.2 vs. 30.8 ± 7.1, mean ±SD
_,_ __ p=0.02) compared to the placebo group. The INI group also had improved
HY (p=0.04) and UPDRS-Motor (Part III) (p=0.02) scores when compared to
baseline. One INI treated patient with multiple system atrophy (MSA) remained
stable and did not show disease progression. The placebo group had no change.
INI administration was well tolerated and there were no hypoglycemic episodes
or serious study related adverse events or medications interactions.

INI is safe in PD and MSA patients and may provide clinically relevant
functional improvement. Larger studies are warranted to determine the INI
effect in treatment of cognitive and motor impairment in Parkinson disease.
NCT02064166.
DOI: 10.13026/323r-nk04
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: 10.13026/3pr8-jx39
title: Hospitalized patients with heart failure: integrating electronic healthcare records and external outcome data

description: Heart failure is a common reason for hospitalization in the elderly and it is
associated with significant mortality and morbidity. To facilitate
epidemiological studies of heart failure, there is a need for high quality
datasets to be made available to researchers. While several heart failure
datasets have been established in Western countries, there is a paucity of
data available from China. Understanding differences in patient populations
and healthcare systems between China and other countries is important in
providing optimal care. To help address this issue, we created a retrospective
heart failure dataset using electronic health data collected from patients who
were admitted to a hospital in Sichuan, China between 2016 and 2019\. The
dataset includes 168 variables for 2,008 patients with heart failure.
DOI: 10.13026/73w7-x881
title: Two-tiered response of cardiorespiratory-cerebrovascular networks to orthostatic challenge

description: The authors used this dataset to evaluate the responsiveness of the
cardiorespiratory-cerebrovascular networks by capturing linear and nonlinear
interdependencies to postural changes. Ten young healthy adults participated
in our study between December 2009 and February 2010 at Semmelweis University,
Faculty of Medicine. Non-invasive measurements of arterial blood pressure
(ABP), cardiac cycle duration (derived from ABP signals), breath-to-breath
interval (capnography), cerebral blood flow velocity (BFV, recorded by
transcranial Doppler sonography), and cerebral hemodynamics (hemoglobin
signals monitored by near-infrared spectroscopy) were performed for 30-minutes
in resting state, followed by a one-minute stand-up and a one-minute sit-down
period. BFV and ABP signals were calibrated, and the dataset contains values
in cm/s and mmHg, respectively. NIRS measurement yielded intensity values (raw
signals) from which change of HbO (oxyhemoglobin), HbR (deoxyhemoglobin) and
HbT (total hemoglobin) concentration in tissue can be calculated (in
micromol/liter). Raw NIRS signals were synchronized to BFV, ABP and
capnography time series yielding 33 minutes of resampled data (sampling
frequency: ~ 3 Hz).
DOI: 10.13026/76ht-2717
title: Body Sway When Standing and Listening to Music Modified to Reinforce Virtual Reality Environment Motion

description: The increased likelihood of falls as a consequence of aging or disease is
generally associated with rising levels of body sway during stance.  The use
of virtual reality (VR) for studying the role in vision in regulating body
sway is well established, however the extent to which music incorporated into
a virtual environment can influence sway is not understood. This dataset was
collected as part of a study to explore the hypothesis that music manipulated
to match VR motion provided by an Oculus Rift head mounted display can lead to
increased levels of body sway. Twenty-eight subjects stood for 60 s on a
balance platform that measured anterior posterior (AP) and medial lateral (ML)
center of pressure movement (indicators of body sway).  While standing, the
subjects experienced combinations of 3 visual conditions (VR translation in
the AP direction at 0.1 Hz, no translation, and eyes closed) and 4 music
conditions (Mozart's Jupiter Symphony modified to scale volume at 0.1 Hz and
0.25 Hz, unmodified music, and no music) for a total of 12 trials. Analysis of
the data using frequency domain measures may reveal the extent to which music
influenced body sway.
DOI: 10.13026/94yv-6145
title: Treadmill Maximal Exercise Tests from the Exercise Physiology and Human Performance Lab of the University of Malaga

description: The present database is an ensemble of the cardiorespiratory measurements
acquired during 992 treadmill maximal graded exercise tests (GET) performed in
the Exercise Physiology and Human Performance Lab of the University of Malaga.
Heart rate, oxygen consumption, carbon dioxide generation, and pulmonary
ventilation are measured on a breath-to-breath basis along with the treadmill
speed during maximal effort tests. Participants are amateur and professional
athletes are of ages ranging from 10 to 63 years old. The age, height, and
weight of the participants are provided, as well as the temperature and
humidity during the test.