International Journal of Medical Informatics

Extract digital health and information management statements from allied health professional standards (Australia)

This contains the SQL code and methods for creating the tool we describe

The data file consists of complete set of raw data containing the determinants associated with Twitter use in psychiatric consultations.

Data for: Self-organizing knowledge management increases the quality of person-centered dementia care: a qualitative study Source code of the application CareShare.

This data set was used to perform the analyses of the article entitled 'Concept Based Auto-Assignment of Healthcare Questions to Domain Experts in Online Q&A Communities' submitted to international journal of medical informatics.

This dataset contains ranking, on importance and feasibility, of statements of requirements of an electronic medical records system for oncology in low and middle income countries, by different stakeholders in cancer care in this setting

This dataset contains clustering data for statements of requirements for an electronic medical records system for oncology in low and middle income countries (LMICs). It is a matrix with similar rows and columns. The numbers indicate how often the statement in the row was put in the same cluster as the statement in the column. The clustering exercise was completed by stakeholders in oncology in LMICs.

This study involved a systematic review of the scientific literature on randomized clinical trials that use apps to improve cancer management among patients, using the Pubmed (Medline), Latin America and the Caribbean in Health Sciences (LILACS), and Cochrane databases. The search was limited to articles written in English and Spanish published in the last 10 years. A search of the App Store for iOS devices and Google Play for Android devices was performed to find the apps identified in the included research articles.

ICD-9 codes classification in each of the groups explained in the article

This archive contains the summarization corpus generated as a result of the filtering stages (trials-final.csv), the rouge scores for the generated summaries (rouge-results-parsed.csv), the data and results of the human evaluation (evaluation/ subfolder), the code used to generate the corpus (extract.r, filter.r, and determine_similarity_threshold.r). The summaries were generated using the summarize_all.py script.