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128 results for “eHealth”
CodiEsp corpus: gold standard Spanish clinical cases coded in ICD10 (CIE10) - eHealth CLEF2020
<p><strong>Introduction</strong></p> <p>These are the train, development and test sets of the CodiEsp corpus. Train, development and test have gold standard annotations. In addition, the unannotated background set is also distributed. All documents are released in the context of the CodiEsp track for CLEF ehealth 2020 (<a href="http://temu.bsc.es/codiesp/">http://temu.bsc.es/codiesp/</a>).</p> <p>The CodiEsp corpus contains manually coded clinical cases. All documents are in Spanish language and CIE10 is the coding terminology (it is the Spanish version of ICD10-CM and ICD10-PCS). The CodiEsp corpus has been randomly sampled into three subsets: the train, the development, and the test set. The train set contains 500 clinical cases, and the development and test set 250 clinical cases each. CodiEsp participants must submit predictions for the test and background set, but they will only be evaluated on the test set.</p> <p> </p> <p><strong>Please cite if you use this dataset:</strong></p> <p>Antonio Miranda-Escalada, Aitor Gonzalez-Agirre, Jordi Armengol-Estapé and Martin Krallinger. Overview of automatic clinical coding: annotations, guidelines, and solutions for non-English clinical cases at CodiEsp track of CLEF eHealth 2020. In CLEF (Working Notes). 2020</p> <pre><code>@inproceedings{miranda2020overview, title={Overview of automatic clinical coding: annotations, guidelines, and solutions for non-english clinical cases at codiesp track of CLEF eHealth 2020}, author={Miranda-Escalada, Antonio and Gonzalez-Agirre, Aitor and Armengol-Estap{\'e}, Jordi and Krallinger, Martin}, booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings}, year={2020} }</code></pre> <p> </p> <p><strong>Annotation quality</strong></p> <p>Inter-annotator agreement: 88.6% for diagnosis coding, 88.9% for procedure coding and 80.5% for the textual reference annotation. For more information, see the <a href="http://ceur-ws.org/Vol-2696/paper_263.pdf">paper</a>.</p> <p><br> <strong>Zip structure</strong><br> Four folders: train, dev, test and background. Each one of them contains the files for the train, development, test and background corpora, respectively.</p> <ul> <li><strong>train, dev and test</strong> folders have: <ul> <li>3 tab-separated files with the annotation information relevant for each of the 3 sub-tracks of CodiEsp. </li> <li>A subfolder named <em>text_files</em> with the plain text files of the clinical cases.</li> <li>A subfolder named <em>text_files_en</em> with the plain text files machine-translated to English. Due to the translation process, the text files are sentence-splitted.</li> </ul> </li> <li>The <strong>background</strong> folder has only <em>text_files</em> and <em>text_files_en</em> subfolders with the plain text files.</li> </ul> <p><br> <strong>Format</strong><br> The CodiEsp corpus is distributed in plain text in UTF8 encoding, where each clinical case is stored as a single file whose name is the clinical case identifier. Annotations are released in a tab-separated file. Since the CodiEsp track has 3 sub-tracks, every set of documents (train and test) has 3 tab-separated files associated with it. </p> <p>For the sub-tracks CodiEsp-D and CodiEsp-P, the file has the following fields:</p> <pre>articleID ICD10-code </pre> <p>Tab-separated files for the sub-track CodiEsp-X contain extra fields that provide the text-reference and its position:</p> <pre>articleID label ICD10-code text-reference reference-position</pre> <p><br> <strong>Corpus summary statistics</strong><br> The final collection of 1000 clinical cases that make up the corpus had a total of 16504 sentences, with an average of 16.5 sentences per clinical case. It contains a total of 396,988 words, with an average of 396.2 words per clinical case.</p> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><strong><a href="https://temu.bsc.es/codiesp/">Web</a></strong></li> <li><strong><a href="http://ceur-ws.org/Vol-2696/paper_263.pdf">Citation</a>: </strong>Antonio Miranda-Escalada, Aitor Gonzalez-Agirre, Jordi Armengol-Estapé and Martin Krallinger. Overview of automatic clinical coding: annotations, guidelines, and solutions for non-English clinical cases at CodiEsp track of CLEF eHealth 2020. In CLEF (Working Notes). 2020</li> <li><strong><a href="https://doi.org/10.5281/zenodo.3859869">Silver Standard corpus</a></strong></li> <li><strong><a href="https://doi.org/10.5281/zenodo.3730566">Annotation guidelines</a></strong></li> <li><a href="https://www.youtube.com/playlist?list=PL5uSCzf1azhA0crlSVCYMPqMUWd4mXc4x"><strong>YouTube presentations</strong></a></li> <li><a href="https://temu.bsc.es/codiesp/index.php/participants-systems/"><strong>Participant codes</strong></a></li> </ul> <p> </p> <p>For more information, visit the track webpage: http://temu.bsc.es/codiesp/ or email us at encargo-pln-life@bsc.es</p> <p> </p> <p>Copyright (c) 2019 Secretaría de Estado para el Avance Digital</p>
TERMINET eHealth post-operation complications synthetic dataset
<p><strong>1. Introduction</strong></p> <p>Older adults with cancer often need to undergo operations. Post-surgery complications may arise, and Real-World Data (RWD) collected from such patients during a pre-operation monitoring period of two weeks can help identify risk for post-surgery complications. The involved RWD span behavioral data (measured or reported) as well as clinical data (collected during clinical tests). This dataset is synthesized by <a href="https://innovationsprint.eu/">Innovation Sprint</a>, using actual data collected from eligible <a href="https://www.policlinicogemelli.it/en/">Fondazione Policlinico Gemelli</a> patients participating to the SUPERO study. The clinical data is collected by the hospital, while the behavioral is collected using <a href="https://innovationsprint.eu/healthentia/">Healthentia</a>, a medical decision support software developed by Innovation Sprint, facilitating the collection, analysis and presentation of behavioral data.</p> <p><strong>2. Dataset description</strong></p> <p>The provided TERMINET eHealth post-operation complications synthetic dataset contains 10,000 synthetic patients, provided in an equal number of rows in the CSV file containing the dataset. The different attributes of the dataset are organized in columns.</p> <p>The attributes are summarized as follows:</p> <ul> <li>6 columns of step data statistics</li> <li>20 columns of clinical attributes</li> <li>2 columns of demographics attributes</li> <li>12 columns of questionnaire attributes</li> <li>1 column of outcome attribute</li> </ul> <p><em>2.1. Step statistics</em></p> <p>Step data is collected per day of the pre-hospitalization period. The final two weeks of that period are used to derive the step statistics. For each of the weeks, the mean, standard deviation and slope of the linear regression of the step data is reported, 3 attributes per week, 6 attributes in total.</p> <p><em>2.2. Clinical attributes</em></p> <p>The 20 clinical attributes collected at the hospital are ALT, Hematocrit (%), AST, Lymphocytes, Hepatitis B, Neutrophils (%), Hepatitis C, Neutrophils, INR (%), INR, White Blood Cells, INR (seconds), Platelets, Sodium, Hemoglobin, Potassium, Lymphocytes (%), Creatinine, Bilirubin and Urea Nitrogen.</p> <p><em>2.3. Demographic attributes</em></p> <p>The sex and age are the two demographic attributes collected.</p> <p><em>2.4. Questionnaire attributes</em></p> <p>Three questionnaires are involved in the SUPERO study are:</p> <ul> <li>G8, spanning the categories of food intake, weight loss, movement, neuropsychological, BMI, multiple medication, health and age.</li> <li>SPPB, spanning the categories of balance, speed and strength</li> <li>MiniCog, where only the clock drawing capabilities are assessed</li> </ul> <p>2.5. Outcome attribute</p> <p>The single outcome attribute is the existence of any post-surgery complications. Please note that the dataset is quite imbalanced, since complications are very rare.</p> <p><strong>3. Data synthesis</strong></p> <p>This dataset is synthesized from the early data of the SUPERO study. Currently there are 21 patients registered, with the decision to operate them being reached for 20 of them. 16 of the patients have already been operated, 2 of them having exhibited post-surgery complications. More vectors have been generated by adding Gaussian noise to the original 16 vectors, resulting to 128 vectors. The resulting vectors have been clustered into 16 clusters using Agglomerative clustering. Every cluster has been modelled via Gaussian Mixture Models. The resulting set of GMMs has been used to generate the 10,000 synthetic vectors of the dataset.</p> <p><strong>4. Acknowledgement</strong></p> <p>The development of this dataset has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 957406 (TERMINET).</p>
Datasets containing the results from the analysis on SDGS and eHealth inside the Citizen Science Community on Twitter
<p>This datasets contain the results from our analyses of the Citizen Science Community on Twitter. These analyses have been done to better understand the discussion about SDGs, eLearning and eHealth.</p> <p><strong>T</strong>he purpose of sharing these datasets is to provide the basis to reproduce the results reported in the associated deliverable. These files are not raw data, since due to privacy concerns we can not share personal information from Twitter.</p> <p><strong>dominant_topics_anonym.xlsx</strong>: Excel datasheet. This dataset contians the distribution of the most discussed topics inside the SDGs discussion.</p> <p><strong>Edges_Hashtag_connected.csv</strong>: CSV file. This dataset contains the edges to build the network of connected hashtags. This edges can be used to build a network and explore the connections or to statiscally analyse the results.</p> <p><strong>hashtags.csv</strong>: CSV file. This dataset contains the results of the most used hashtags in the analysis about eLearning. <br> </p> <p><strong>hashtags_treemap_health.xlsx</strong>: Excel datasheet. This dataset contains the results of the most frequent hashtags in the eHealth analysis.</p> <p><strong>ldavis_prepared_ieee17.html</strong>: HTML file. This file contains the Intertopic distance map and most salient terms from the topic modelling analysis done in the SDGs conversation study.</p> <p><strong>Most_retweeted_accounts.xlsx</strong>: Excel datasheet. This dataset contains the top 20 users that receive more retweets in the conversation around eHealth. The column called Indegree refers to the topological value calculated from the network of retweets. This indegree is equivalent to the number of retweets received. On the other hand, Outdegree is the opposite, so number of retweets given to others.</p> <p><strong>Most_retweeting_account.xlsx</strong>: Excel datasheet. This dataset presents the opposite part of the previous one, the accounts that retweet the most from the eHealth analysis. The columns contain the same indicators: Indegree and Outdegree.</p> <p><strong>sdgs_count_publish.csv</strong>: CSV file. This dataset contains the number of tweets assigned to the different SDGs from the analysis done on the conversation about these Goals.</p> <p><strong>sdgs_tweets_sdgsaccess.xlsx</strong>: Excel datasheet. Same file as the previous one in other format to ease the handling in Excel.</p> <p><strong>top_hash_health.xlsx</strong>: Excel datasheet. The most used hashtags inside the conversation about eHealth.</p> <p><strong>topics_tweets_sdgsaccess.xlsx</strong>: Excel datasheet. Tweets by topic extracted using Machine Learning in the SDGs analysis.</p> <p> </p> <p>This repository will receive updates in the future in order to present all the data available and publishable from the different analysis that were described.</p>
eHealth solutions in Africa
<p>Information about eHealth solutions and in African countries, mainly from the countries participating in the BETTEReHEALTH project (Ghana, Malawi, Ethiopia, Tunisia). From the BETTEReHEALTH registry: <a href="https://registry.betterehealth.eu/ehealth-solution">https://registry.betterehealth.eu/ehealth-solution</a></p>
eHealth-KD v2
<p>The eHealth-KD v2 Corpus is a set of 1,050 sentences in Spanish language from the biomedical domain, annotated with a general-purpose entity and relation model that covers broad sentence-level semantics. </p>
Dataset: eHealth, Inc. (EHTH) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
eHealth policies in Africa
<p>Information about eHealth policies and in African countries, including strategies, frameworks, guideslines and standards. Also policy documents in related fields such as healthcare, ICT, privacy, and finance. From the BETTEReHEALTH registry: <a href="https://registry.betterehealth.eu/ehealth-policies">https://registry.betterehealth.eu/ehealth-policies</a></p>
RCT for Innovating Stress-related eHealth
ClinicalTrials.gov study NCT05305235. IPD Sharing: YES. Countries: 1. Publications: 2.
The workload of manual data entry for integration between mobile health applications and eHealth infrastructure
<div> <p>In this study, we conducted a time-motion study observing healthcare workers (HCWs) completing data management activities including monitoring and evaluation (M&E) and manual data linkage of individual-level app data to electronic medical records (EMRS). This study served as a baseline study for an open-source app to mirror EMRS and reduce HCW workload while improving care in the Nurse-led Community-based Antiretroviral therapy Program (NCAP) in Lilongwe, Malawi.</p> </div>
eHealth Partnered Evaluation Initiative - Evaluation of the Initial Deployment of VA's Annie Texting System
ClinicalTrials.gov study NCT03898349. IPD Sharing: NO. Countries: 1. Publications: 2.
eHealth Weight Loss Program in African American Breast Cancer Survivors
ClinicalTrials.gov study NCT02699983. IPD Sharing: Not stated. Countries: 1. Publications: 1.
eHealth Partnered Evaluation Initiative
ClinicalTrials.gov study NCT02665468. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Personalized Nutrition and EHealth: Lifestyle Intervention (LION) Study for Weight Loss Maintenance
ClinicalTrials.gov study NCT04023942. IPD Sharing: YES. Countries: 1. Publications: 4.
Evaluating the Effectiveness of the GILL eHealth Intervention to Improve Physical Health and Lifestyle Behaviours in Patients With Severe Mental Illness
ClinicalTrials.gov study NCT05533749. IPD Sharing: YES. Countries: 1. Publications: 1.
Evaluating an eHealth Solution for Screening in Pediatric Care
ClinicalTrials.gov study NCT04506294. IPD Sharing: YES. Countries: 1. Publications: 1.
Effect of eHealth Cardiac Rehabilitation Incorporating Mindfulness for Patients With Coronary Heart Disease
ClinicalTrials.gov study NCT07109752. IPD Sharing: NO. Countries: 1. Publications: 1.
The workload of manual data entry for integration between mobile health applications and eHealth infrastructure
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CodiEsp Silver Standard: Participant predictions in eHealth CLEF2020 - Spanish clinical cases coded in ICD10 (CIE10)
<p><strong>Introduction</strong></p> <p>Predictions in the background set of <a href="https://temu.bsc.es/codiesp/">eHealth CLEF 2020 Task 1</a> participants.</p> <p> </p> <p><strong>Zip structure</strong></p> <p>One directory per CodiEsp subtask. Within each CodiEsp subtask directory, there is one directory per team that contains the prediction runs.</p> <p> </p> <p><strong>Format</strong><br> The text documents are distributed in plain text files, UTF-8 encoding.<br> The CodiEsp Silver Standard annotations have the following format:</p> <p>For the sub-tracks CodiEsp-Diagnostic and CodiEsp-Procedure, the file files have the following fields:</p> <pre>articleID ICD10-code </pre> <p>Tab-separated files for the sub-track CodiEsp-X (explainability) contain extra fields that provide the text-reference and its position:</p> <pre>articleID label ICD10-code text-reference reference-position</pre> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><strong><a href="https://temu.bsc.es/codiesp/">Web</a></strong></li> <li><strong>Citation: </strong>Miranda-Escalada, A., Farré, E., & Krallinger, M. (2020). Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results. In <em>Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings</em>.</li> <li><strong><a href="https://zenodo.org/record/3837305#.X7T9KVlKg5k">Gold Standard corpus</a></strong></li> <li><strong><a href="https://doi.org/10.5281/zenodo.3878178">Annotation guidelines</a></strong></li> <li><a href="https://www.youtube.com/playlist?list=PL5uSCzf1azhC24g5dsp5eVMp8BZFWCraX"><strong>YouTube presentations</strong></a></li> <li><a href="https://temu.bsc.es/cantemist/?p=4606"><strong>Participant codes</strong></a></li> </ul> <p> </p> <p>All credit to CodiEsp participants</p>
eHealth-KD 2020
<p>Docs, data and development scripts for the eHealth-KD Challenge at IberLEF 2020</p>
Efectividad de intervenciones eHealth para reducir síntomas depresivos en personas con Diabetes Mellitus Tipo 1 y 2: Una revisión sistemática.
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