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4,871 results for “symptoms”
Dataset of The distinct influence of different maternal mental health symptom profiles on infant sleep during the first year postpartum: a cross-sectional survey
<p>The distinct influence of different, but comorbid, maternal mental health difficulties, such as postpartum depression, anxiety, or childbirth-related posttraumatic stress disorder (CB-PTSD) on infant sleep is unknown, although maternal mental health was reported to be associated with infant sleep. This paper first aimed to associations between maternal mental health symptoms and infant sleep. Second, it aimed to exploratory obtain maternal mental health symptom profiles from maternal mental health symptoms. Finally, it aimed to investigate the distinct influence of these maternal mental health symptom profiles on infant sleep, when including mediators (i.e., maternal perception of infant temperament and method to fall asleep) and moderators (maternal educational level and infant age).</p> <p>This dataset contains data on the mental health (i.e., CB-PTSD, depression, anxiety) of 410 mothers with an infant aged between 3 to 12 months old. Information on infant sleep and temperament (negative emotionality) was collected via standardised maternal-report questionnaires (City BiTS, EPDS, HADS, BISQ, and IBQ-R very short form). Sociodemographic data such as maternal age, marital status, educational level, infant age, and week of gestation are reported.</p> <p>This dataset is related to: Sandoz, V.; Lacroix, A.; Stuijfzand, S.; Bickle Graz, M.; Horsch, A. Maternal Mental Health Symptom Profiles and Infant Sleep: A Cross-Sectional Survey. <em>Diagnostics</em> <strong>2022</strong>, <em>12</em>, 1625. https://doi.org/10.3390/diagnostics12071625. </p>
Data set supplementing "Benchmarking triage capability of symptom checkers against that of medical laypersons: Survey study"
<p>This is the de-identified data set used to conduct the analyses in the study published as Original Research in the JMIR under the title "Benchmarking triage capability of symptom checkers against that of medical laypersons: Survey study" (https://doi.org/10.2196/24475)</p> <p>The data set contains the assessments of the urgency of symptoms to 45 fictitious clinical case vignettes by 91 US participants, and the participants' age, gender and level of education. Data for the symptom checker apps is needed to fully reproduce our study and can be found in the appendix of the paper "Evaluation of symptom checkers for self diagnosis and triage: audit study" by Semigran et al. (2015) (https://doi.org/10.1136/bmj.h3480).</p>
EPOCHAL (Effects of Pollen on Cardiorespiratory Health and Allergic symptoms): Daily questionnaire (English and German)
<p>This questionnaire was developed for the EPOCHAL study (Effects of Pollen on Cardiorespiratory Health and Allergic symptoms). The study was sponsored and led by Swiss TPH in Basel, Switzerland and approved by the local ethics committee (Ethikkomission Nordwest- und Zentralschweiz EKNZ, project ID 2021-00151). Written informed consent was obtained from every participant prior to study inclusion. </p> <p>This is the "daily questionnaire" which was administered 10 times for each participant on different days during the pollen season. It includes questions about the following topics:<br> 1) Overall health status<br> 2) Allergic symptoms: nose, eyes, lungs<br> 3) Sleep, mood and quality of life<br> 4) Medication use<br> 5) Time spent outdoors<br> 6) Daily covariate information: coffee and alcohol intake, eating, smoking, vigorous exercise<br> 7) Blood pressure<br> 8) Comments</p> <p>The questionnaire is also available in German under the same DOI.</p> <p>Please note that this questionnaire was administered electronically in a browser, and contains:</p> <ul> <li>Form logic, which determines the relevance of some questions based on previous answers. Affected questions are typically shown in grey color.</li> </ul>
EPOCHAL (Effects of Pollen on Cardiorespiratory Health and Allergic symptoms): Nurse home visit form (English and German)
<p>This questionnaire was developed for the EPOCHAL study (Effects of Pollen on Cardiorespiratory Health and Allergic symptoms). The study was sponsored and led by Swiss TPH in Basel, Switzerland and approved by the local ethics committee (Ethikkomission Nordwest- und Zentralschweiz EKNZ, project ID 2021-00151). Written informed consent was obtained from every participant prior to study inclusion. </p> <p>This is the "nurse home visit form" which was administered 6 times for each participant during weekly home visits by our study nurses during the pollen season. It includes questions about the following topics:<br> 1) Potential for Covid-19 infection, changes in vaccination status<br> 2) Overall health status<br> 3) Allergic symptoms: nose, eyes, lungs<br> 4) Sleep, mood and quality of life<br> 5) Medication use<br> 6) Time spent outdoors<br> 7) Daily covariate information: coffee and alcohol intake, eating, smoking, vigorous exercise<br> 8) Blood pressure measurements<br> 9) Heart rate variability recording<br> 10) Exhaled nitric oxide measurements<br> 11) Pulmonary function testing (spirometry)<br> 12) Comments</p> <p>The questionnaire is also available in German under the same DOI.</p> <p>Please note that this questionnaire was administered electronically on a tablet, and contains:</p> <ul> <li>Form logic, which determines the relevance of some questions based on previous answers. Affected questions are typically shown in grey color.</li> <li>Instructions (in bold blue font) to the participant/study nurse to guide the process of data collection (e.g. “please hand over the tablet to the participant/nurse”).</li> <li>Warnings (in large red font) and directions (in large grey font) to warn nurses against performance of spirometry measurements if contraindications were present. For example, when the nurse entered high blood pressure in Topic #8, or when recent surgery was indicated in Topic #11. Warnings and directions also flag incidental findings (e.g., high blood pressure ≥160 mmHg (systolic) or ≥100 mmHg (diastolic) requiring urgent action.</li> </ul>
Data Set on Accuracy of Symptom Checker Apps in 2020
<p>These two data sets present the accuracy of triage (disposition) and diagnostic advice of symptom checker apps sampled in 2020. The sample consists of 22 commonly used symptom checker apps, of which 14 also provide diagnostic advice. The apps were tested on 45 case vignettes, i.e. fictitious descriptions of patients. As not every app was able to appraise every vignette our study yielded a total of 796 unique triage evaluations and 520 unique diagnostic evaluations. The data sets are a supplement to the paper "Triage Accuracy of Symptom Checker Apps: A Five-year Follow-up Evaluation" (doi: <a href="https://doi.org/10.2196/31810">10.2196/31810</a>).</p> <p>The was collected by Anna Dames as partial requirement for her MSc degree in Human Factors in the Department of Psychology and Ergonomics (IPA) at Technische Universität Berlin.</p> <p>The clinical vignettes were originally compiled and modified by Semigran et al. in 2015 (https://doi.org/10.1136/bmj.h3480), and further adapted by Hill et al. (2020) (doi: 10.5694/mja2.50600) and in the study these data sets are supplement to (doi: <a href="https://doi.org/10.2196/31810">10.2196/31810</a>).</p>
GERONTE H2020 project - GERDAT006 - Dataset of symptoms and proms for specific cancer types and gender
<p>This dataset describes a series of symptoms, potentially indicative of treatment-related complications, destabilised comorbidity or functional decline, to be used in the Geronte project for symptoms monitoring in older patients with multimorbidity during and after their cancer treatment</p>
Time series data of COVID-19 cases (rT-PCR-confirmed), hospitalisations (laboratory-confirmed), and hospital-associated deaths (laboratory confirmed) in South Africa, by imputed dates of symptom onset, from the start of the pandemic in March 2020 through April 2022.
<p>Time series data of COVID-19 cases (rT-PCR-confirmed), hospitalisations (laboratory-confirmed), and hospital-associated deaths (laboratory confirmed) in South Africa, by imputed dates of symptom onset, from the start of the pandemic in March 2020 through April 2022. These data were used to estimate the time-varying reproduction number (R) in South Africa, as described in https://www.medrxiv.org/content/10.1101/2022.07.22.22277932v1.full.</p>
Risk and symptoms of COVID-19 in health professionals according to baseline immune status and booster vaccination during the Delta and Omicron waves in Switzerland – a multicentre cohort study
<p>For details, see publication</p>
Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection
<p>Coswara is a dataset containing diverse set of respiratory sounds and rich meta-data from COVID-19 positive and Non-COVID subjects.</p>
Dautan et al 2024 " Gut-Initiated Alpha Synuclein Fibrils Drive Parkinson's Disease Phenotypes: Temporal Mapping of non-Motor Symptoms and REM Sleep Behavior Disorder"
<p><span>Parkinson’s disease (PD) is characterized by progressive motor as well as less recognized non-motor symptoms that arise often years before motor manifestation, including sleep and gastrointestinal disturbances. Despite the heavy burden on the patient’s quality of life, these non-motor manifestations are poorly understood. To elucidate the temporal dynamics of the disease, we employed a mice model involving injection of alpha-synuclein (αSyn) pre-formed fibrils (PFF) in the duodenum and antrum as a gut-brain model of Parkinsonism. Using anatomical mapping of αSyn PFF propagation and behavioral and physiological characterizations, we unveil a correlation between post-injection time the temporal dynamics of αSyn propagation and non-motor/motor manifestations of the disease. We highlight the concurrent presence of aggregates in key brain regions, expressing acetylcholine or dopamine and their functions in sleep duration, wakefulness, and particularly REM-associated atonia corresponging to REM behavioral disorder-like symptoms. This study presents a novel and in-depth exploration into the multifaceted nature of PD, unraveling the complex connections between α-synucleinopathies, gut-brain connectivity, and the emergence of non-motor phenotypes.</span></p>
The relationship between linguistic expression and symptoms of depression, anxiety, and suicidal thoughts: A longitudinal study of blog content
<p>To investigate the associations between linguistic features and symptoms of depression, generalised anxiety, and suicidal ideation, we extracted linguistic features from individuals’ blog content and correlated it with validated mental health data in a longitudinal study (n=38). Depressive symptoms were assessed using the self-report Patient Health Questionnaire (PHQ-9), anxiety symptoms using the self-report Generalised Anxiety Disorder Scale (GAD-7), and social media data was analysed using the Linguistic Inquiry and Word Count (LIWC) tool for linguistic features. Bivariate and multivariate analyses were performed to investigate the correlations between the linguistic features and mental health scores between subjects. We then used the multivariate regression model to predict longitudinal changes in mood within subjects.</p>
MeSDiCon - Medical Spanish Disease and symptom name Collection lexicon (unfiltered initial version)
<p>The MeSDiCon - (Medical Spanish Disease and symptom name Collection lexicon) consists of a list or gazetteer of candidate names of diseases and symptoms mentioned in Spanish clinical texts. Thus MeSDiCon serves as a lexical resource or dictionary for automatic detection of disease/symptom mentions, as well as indexing or classification of medical texts with such concept types.</p> <p>This collection was generated in a five step procedure:</p> <ol> <li>Automatic detection of mentions of disease/symptom terms in biomedical texts in English (including mapping/normalization to MeSH terms or OMIM identifiers).</li> <li>Generation of a unique name list from the detected concept mentions.</li> <li>Basic filtering of non- disease/symptom names or highly ambiguous mentions-abbreviations using basic characteristics like name morphology and length criteria.</li> <li>Automatic translation of name lists form English to Spanish using a medical machine translation system (see Soares, F. and Krallinger, M. BSC Participation in the WMT Translation of Biomedical Abstracts. In <em>Proceedings of the Fourth Conference on Machine Translation, Volume 3: Shared Task Papers, </em>pp. 175-178 2019; https://zenodo.org/record/3346802)</li> <li>Automatic mention lookup of translated names in a collection of 20 million Spanish clinical notes (primary care and pediatrics).</li> </ol> <p>Every term in MeSDiCon is identified by a text span (in Spanish), a target terminology namespace to which it was automatically mapped (MeSH or OMIM) and its corresponding concept identifier in that target terminology. Moreover, we provide for every text span the absolute term frequency, i.e. the number of matches in the corpus of 20 million clinical notes and the number of documents or notes in which it was automatically.</p> <p>Important note: no manual filtering of the MeSDiCon was carried out, implying that some entries might comprise errors, either due to the initial name recognition and concept mapping in English or due to wrong automatic translations into Spanish.</p> <p>The MeSDiCon resource is provided in two formats:</p> <ul> <li>TSV. Data is separated by tabs (\t). Every row of the file has the following fields:</li> </ul> <pre><code>terminology identifier translatedTerm termCount documentCount</code></pre> <ul> <li>JSON. Records are stored as a list of JSON objects. They have the following fields:</li> </ul> <pre><code>{ "terminology":"MESH", "identifier":"D025861", "translatedTerm":"Trastornos de la coagulación", "termFrequency":9, "documentFrequency":9 }</code></pre> <p> </p> <p>Copyright (c) 2019 Secretaría de Estado para el Avance Digital</p>
The Brief Symptom Inventory in the Swiss general population: Presentation of norm scores and predictors of psychological distress: Data supporting the publication
This is the dataset on which the following publication is based: • Michel G, Baenziger J, Brodbeck J, Mader L, Kuehni CE, Roser K (2024). The Brief Symptom Inventory in the Swiss general population: Presentation of norm scores and predictors of psychological distress. PLOS One. 19(7), e0305192. Doi: 10.1371/journal.pone.0305192, https://doi.org/10.1371/journal.pone.0305192 A description of the sample and the data collection procedure is available in the publication. The dataset contains the following variables: • Socio-demographic characteristics of the sample - Weights according to representative general population sample - Sex from Swiss Federal Statistical Office (SFSO) - Age at study (rounded to integer) - Age categories (10-year age groups) - Language questionnaire (German/Rumantsch, French, Italian) - Nationality from SFSO - Migration background - Education - Employment status • Original and prepared data on the Brief Symptom Inventory A detailed data dictionary is available in a separate excel file. Version • 1.0 (15 August 2024)
Covid-19 - Symptoms - Impact on quality of life and needs of affected people
<p>Dataset « Covid-19 - Symptoms - Impact on quality of life and needs of affected people ». The data came from an online study involving a sample of 639 participants resident in France affected by COVID-19 symptoms several days, weeks or months after infection. It was collected to provide characterization of a wide range of symptoms of COVID-19, their effects on quality of life and the needs of those affected.</p>
Pain Catastrophizing Predicts Alcohol Hangover Severity and Symptoms - Raw Data
<p>Raw data from investigation published as 'Pain Catastrophizing Predicts Alcohol Hangover Severity and Symptoms.'</p>
Symptoms in health care workers during the COVID-19 epidemic. A cross-sectional survey.
<p>data collected during the COVID-19 epidemics on workers of the Health Care Unit Roma4, Civitavecchia. Paper submitted.</p>
Scripts from: A framework to diagnose the causes of river ecosystem deterioration using biological symptoms
<ol> <li>River assessments are predominantly based upon biological metrics and indices selected or designed to integrate the impact of multiple causes of deterioration (stressors) operating at various spatial scales. Yet, the integrative nature of many bioassessment systems does not allow for tracing back individual stressors and their influence on the overall assessment result. Thus, river managers often fail to link bioassessment with programmes of management measures, to improve ecological quality.</li> <li>Here, we present a novel diagnostic approach that allows to estimate the probability of individual stressors being causal for biological degradation at the scale of individual riverine ecosystems. Similar to medical diagnosis, we use various <i>symptoms</i> (macroinvertebrate metrics) and probabilistically link them to various potential <i>causes</i> of ecological status degradation (stressors). Symptoms and causes are informed by a training dataset of 157 samples (stressors, taxa lists) from central European lowland rivers and are linked through a Bayesian Network (BN). Three separate BNs addressing three different spatial scales (catchment, reach and site) are presented. </li> <li>Water quality-related causes are most influential at the catchment scale, while hydromorphological causes prevail at finer scales. Causes indicating riparian degradation are most influential at the reach scale. Many symptoms show strong linkages to causes and reveal ecologically meaningful relationships, thus pointing at the potential diagnostic utility of the symptoms selected. BNs are validated using an independent dataset of 47 samples. Overall, model accuracies range 53–58% for the three BNs, while for individual nodes (causes and symptoms) up to 100% concordance of predicted and actual node states in the validation data is achieved. The BNs are implemented as interactive online diagnostic tools to allow end users an easy application. </li> <li> <i>Synthesis and applications.</i> Our results confirm that Bayesian inference can greatly assist the diagnosis of potential causes of river deterioration based upon a selection of diagnostic biological metrics. If integrated into a Bayesian Network, symptoms and potential causes can be linked and inform management decisions on appropriate measures, to improve ecological quality. Diagnostic Bayesian Networks thus support end users bridge the gap between biological monitoring and appropriate programmes of management measures. 28 July 2020</li> </ol>
Data from: A framework to diagnose the causes of river ecosystem deterioration using biological symptoms
<ol> <li>River assessments are predominantly based upon biological metrics and indices selected or designed to integrate the impact of multiple causes of deterioration (stressors) operating at various spatial scales. Yet, the integrative nature of many bioassessment systems does not allow for tracing back individual stressors and their influence on the overall assessment result. Thus, river managers often fail to link bioassessment with programmes of management measures, to improve ecological quality.</li> <li>Here, we present a novel diagnostic approach that allows to estimate the probability of individual stressors being causal for biological degradation at the scale of individual riverine ecosystems. Similar to medical diagnosis, we use various <i>symptoms</i> (macroinvertebrate metrics) and probabilistically link them to various potential <i>causes</i> of ecological status degradation (stressors). Symptoms and causes are informed by a training dataset of 157 samples (stressors, taxa lists) from central European lowland rivers and are linked through a Bayesian Network (BN). Three separate BNs addressing three different spatial scales (catchment, reach and site) are presented. </li> <li>Water quality-related causes are most influential at the catchment scale, while hydromorphological causes prevail at finer scales. Causes indicating riparian degradation are most influential at the reach scale. Many symptoms show strong linkages to causes and reveal ecologically meaningful relationships, thus pointing at the potential diagnostic utility of the symptoms selected. BNs are validated using an independent dataset of 47 samples. Overall, model accuracies range 53–58% for the three BNs, while for individual nodes (causes and symptoms) up to 100% concordance of predicted and actual node states in the validation data is achieved. The BNs are implemented as interactive online diagnostic tools to allow end users an easy application. </li> <li> <i>Synthesis and applications.</i> Our results confirm that Bayesian inference can greatly assist the diagnosis of potential causes of river deterioration based upon a selection of diagnostic biological metrics. If integrated into a Bayesian Network, symptoms and potential causes can be linked and inform management decisions on appropriate measures, to improve ecological quality. Diagnostic Bayesian Networks thus support end users bridge the gap between biological monitoring and appropriate programmes of management measures. 28-Jul-2020</li> </ol>
TweetC19SR-Eng - Manually annotated dataset of English language COVID-19 tweets containing self-reports of symptoms
<p><strong>In this work, we release two expert curated, manually annotated datasets of COVID-19 self-reported symptoms. The first dataset contains tweets in English and the second contains tweets in Spanish, both containing around 36,500 tweets in total. These datasets were used for the Sixth and Seventh Workshop on Social Media Mining For Health (2021 and 2022)</strong></p>
TweetC19SR-Spa - Manually annotated dataset of Spanish language COVID-19 tweets containing self-reports of symptoms
<p><strong>In this work, we release two expert curated, manually annotated datasets of COVID-19 self-reported symptoms. The first dataset contains tweets in English and the second contains tweets in Spanish, both containing around 36,500 tweets in total. These datasets were used for the Sixth and Seventh Workshop on Social Media Mining For Health (2021 and 2022)</strong></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.