ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2017
Initializing and Growing a Database of Health Information Technology (HIT) Events by Using TF-IDF and Biterm Topic Modeling.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- A Systematic Review of Topic Modeling Techniques for Electronic Health Records.Healthcare (Basel, Switzerland) · 2026Review
- A Descriptive Analysis of Capsule Endoscopy Events in the FDA Manufacturer and User Facility Device Experience (MAUDE) Database.Journal of digestive endoscopy · 2021Article
- Using convolutional neural networks to identify patient safety incident reports by type and severity.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Health information technology (HIT) events were listed in the top 10 technology-related hazards since one in six patient safety events (PSE) is related to HIT. Although it becomes a common sense that event reporting is an effective way to accumulate typical cases for learning, the lack of HIT event databases remains a challenge. Aiming to retrieve HIT events from millions of event reports related to medical devices in FDA Manufacturer and User Facility Device Experience (MAUDE) database, we proposed a novel identification strategy composed of a structured data-based filter and an unstructured data-based classifier using both TF-IDF and biterm topic. A dataset with 97% HIT events was retrieved from the raw database of 2015 FDA MAUDE, which contains approximately 0.4~0.9% HIT events. This strategy holds promise of initializing and growing an HIT database to meet the challenges of collecting, analyzing, sharing, and learning from HIT events at an aggregated level.
Indexed as
Identifiers
29854170PMC5977677What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.