The data for the smoking challenge consisted exclusively of discharge summaries from Partners HealthCare which were preprocessed and converted into XML format, and separated into training and test sets. I2B2 is a data warehouse containing clinical data on over 150k patients, including outpatient DX, lab results, medications, and inpatient procedures. ETL processes authored to pull data from EMR and finance systems Institutional review boards of Partners HealthCare approved the challenge and the data preparation process. The data were annotated by pulmonologists and classified patients into Past Smokers, Current Smokers, Smokers, Non-smokers, and unknown. Second-hand smokers were considered non-smokers. Other institutions involved include Massachusetts Institute of Technology, and the State University of New York at Albany. i2b2 is a passionate advocate for the potential of existing clinical information to yield insights that can directly impact healthcare improvement. In our many use cases (Driving Biology Projects) it has become increasingly obvious that the value locked in unstructured text is essential to the success of our mission. In order to enhance the ability of natural language processing (NLP) tools to prise increasingly fine grained information from clinical records, i2b2 has previously provided sets of fully deidentified notes from the Research Patient Data Repository at Partners HealthCare for a series of NLP Challenges organized by Dr. Ozlem Uzuner. We are pleased to now make those notes available to the community for general research purposes. At this time we are releasing the notes (~1,000) from the first i2b2 Challenge as i2b2 NLP Research Data Set #1. A similar set of notes from the Second i2b2 Challenge will be released on the one year anniversary of that Challenge (November, 2010).

Overview: This is a lab-based dataset with videos recording volunteers (medical students) washing their hands as part of a hand-washing monitoring and feedback experiment. The dataset is collected in the Medical Education Technology Center (METC) of Riga Stradins University, Riga, Latvia. In total, 72 participants took part in the experiments, each washing their hands three times, in a randomized order, going through three different hand-washing feedback approaches (user interfaces of a mobile app). The data was annotated in real time by a human operator, in order to give the experiment participants real-time feedback on their performance. There are 212 hand washing episodes in total, each of which is annotated by a single person. The annotations classify the washing movements according to the World Health Organization's (WHO) guidelines by marking each frame in each video with a certain movement code.

This dataset is part on three dataset series all following the same format:

https://zenodo.org/record/4537209 - data collected in Pauls Stradins Clinical University Hospital

https://zenodo.org/record/5808764 - data collected in Jurmala Hospital

https://zenodo.org/record/5808789 - data collected in the Medical Education Technology Center (METC) of Riga Stradins University

Note #1: we recommend that when using this dataset for machine learning, allowances are made for the reaction speed of the human operator labeling the data. For example, the annotations can be expected to be incorrect a short while after the person in the video switches their washing movements.

Application: The intention of this dataset is to serve as a basis for training machine learning classifiers for automated hand washing movement recognition and quality control.

Statistics:

Frame rate: ~16 FPS (slightly variable, as the video are reconstructed from a sequence of jpg images taken with max framerate supported by the capturing devices).

Resolution: 640x480

Number of videos: 212

Number of annotation files: 212

Movement codes (in JSON files):

1: Hand washing movement — Palm to palm

2: Hand washing movement — Palm over dorsum, fingers interlaced

3: Hand washing movement — Palm to palm, fingers interlaced

4: Hand washing movement — Backs of fingers to opposing palm, fingers interlocked

5: Hand washing movement — Rotational rubbing of the thumb

6: Hand washing movement — Fingertips to palm

0: Other hand washing movement

Note #2: The original dataset of JPG images is available upon request. There are 13 annotation classes in the original dataset: for each of the six washing movements defined by the WHO, "correct" and "incorrect" execution is market with two different labels. In this published dataset, all incorrect executions are marked with code 0, as "other" washing movement.

Acknowledgments: The dataset collection was funded by the Latvian Council of Science project: "Automated hand washing quality control and quality evaluation system with real-time feedback", No: lzp - Nr. 2020/2-0309.

References: For more detailed information, see this article, describing a similar dataset collected in a different project:

M. Lulla, A. Rutkovskis, A. Slavinska, A. Vilde, A. Gromova, M. Ivanovs, A. Skadins, R. Kadikis, A. Elsts. Hand-Washing Video Dataset Annotated According to the World Health Organization’s Hand-Washing Guidelines. Data. 2021; 6(4):38. https://doi.org/10.3390/data6040038

Contact information: atis.elsts@edi.lv

This dataset contains the results of analysis of original peer-reviewed articles in English published between 2004 and 2024 that reported quantitative data on the real-life application of pre-hospital non-medical help in snakebite incidents. The search of eligible papers was carried out in October 2024 in PubMed using the following request: ((snakebit*[Title/Abstract]) OR (snake* bit*[Title/Abstract]) OR (snake* envenom*[Title/Abstract]) OR (snake* venom*[Title/Abstract]) OR (Snake Bites[MeSH Terms])) AND (2004:2024[pdat]) AND (English[Language]). The dataset table collected the following data (sheet #1 "Dataset"): article author(s), year of publication, article source link, study design, study geography (country), study sample characteristics, sample size, types of providers of pre-hospital non-medical help, types of help-seeking and helping behaviours and practices, rates of their occurrence presented as percentages with one decimal place, and data on statistically significant associations between particular helping practices and health outcomes. Sheet #2 ("Bibliography") of the dataset file contains a complete bibliography of the articles included in the analysis (n=158). The dataset was utilised to perform the related scoping review.

Bibliographic data of biomedical systematic reviews and meta-analysis studies published between 2014 and 2019, where at least one author is affiliated with an institution in Sub-Saharan Africa was retrieved from MEDLINE via the PubMed search engine. All forty-six (46) countries in Sub-Saharan Africa were included in the search query as affiliation. The search strategy are decripted in four steps: Step #1: Nigeria[Affiliation] OR South Africa[Affiliation] OR Ghana[Affiliation] OR Tanzania[Affiliation] OR Kenya[Affiliation] OR Rwanda[Affiliation] OR Botswana[Affiliation] OR Cameroun[Affiliation] OR Senegal[Affiliation] OR Angola[Affiliation] OR Uganda[Affiliation] OR Mali[Affiliation] OR Sierra Leone[Affiliation] OR Ivory Coast[Affiliation] OR Ethiopia[Affiliation] OR Lesotho[Affiliation] OR Zambia[Affiliation] OR Zimbabwe[Affiliation] OR Namibia[Affiliation] OR Guinea[Affiliation] OR Mauritius[Affiliation] OR Mozambique[Affiliation] OR Niger[Affiliation] OR Seychelles[Affiliation] OR Burkina Faso[Affiliation] OR Burundi[Affiliation] OR Cape Verde[Affiliation] OR Cameroon[Affiliation] OR Central African Republic[Affiliation] OR Chad[Affiliation] OR Comoros[Affiliation] OR Democratic Republic of Congo[Affiliation] OR DR Congo[Affiliation] OR Djibouti[Affiliation] OR Cote D'ivoire[Affiliation] OR Congo[Affiliation] OR Equatorial Guinea[Affiliation] OR Eritrea[Affiliation] OR Gabon[Affiliation] OR Guinea-Bissau[Affiliation] OR Madagascar[Affiliation] OR Congo Republic[Affiliation] OR Sao Tome and Principe[Affiliation] OR Swaziland[Affiliation] OR Togo[Affiliation] OR Benin[Affiliation] OR Liberia[Affiliation] OR Namibia[Affiliation] OR Gambia[Affiliation] OR (Cent Afr Republ[Affiliation]) OR (Equat Guinea[Affiliation]) OR (Papua N Guinea[Affiliation]) OR (Sao Tome E Prin[Affiliation]) OR Principe[Affiliation] OR Sao Tome E Principe[Affiliation] Step #2 The filter was set to Meta-Analysis[ptyp] OR systematic[sb]Step #3: Text word search systematic review[Text Word] OR meta-analysis[Text Word] OR meta analysis[Text Word]Step #4: Set publication date to: "2014/01/01"[PDAT] : "2019/12/31"[PDAT]The search which was done on April 2nd, 2020 returned 3,171 results. The bibliographic data collected with the queries posed to PubMed were cleaned, duplicates were removed and articles that were not meta-analysis or systematic reviews were removed. MEDLINE is an authoritative and specialized biomedical database for indexing biomedical publications.Query: (Step #1) AND (Step #2 OR Step #3) AND (Step #4)