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Construction of a Multi-Label Classifier for Extracting Multiple Incident Factors From Medication Incident Reports in Residential Care Facilities: Natural Language Processing Approach

Construction of a Multi-Label Classifier for Extracting Multiple Incident Factors From Medication Incident Reports in Residential Care Facilities: Natural Language Processing Approach

The prevention of medication-related incidents and the development of preventive measures are crucial for ensuring medication safety. Heinrich law suggests that for every serious accident, 29 minor accidents and 300 incidents exist [1]. Analysis of these incidents and formulation of countermeasures can help prevent serious medical accidents and enhance patient safety.

Hayato Kizaki, Hiroki Satoh, Sayaka Ebara, Satoshi Watabe, Yasufumi Sawada, Shungo Imai, Satoko Hori

JMIR Med Inform 2024;12:e58141

Visual “Scrollytelling”: Mapping Aquatic Selfie-Related Incidents in Australia

Visual “Scrollytelling”: Mapping Aquatic Selfie-Related Incidents in Australia

To help prevent this issue, this study aimed to visualize selfie-related incidents globally by initially creating a scrollable visual story overlayed on a satellite map of the incidents in Australia. This type of visual storytelling technique using a world map helps illustrate the spatial context of this public health issue. Incident data were acquired via publicly accessible news reports and a Wikipedia repository [2] and cleaned and prepared in Excel (Microsoft Corp).

Samuel Cornell, Amy E Peden

Interact J Med Res 2024;13:e53067