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{
"description": "Simple matching to identify duplicates in patient records produces\nnumerous errors for various reasons. To improve the identification of\nduplicates, we built an incremental model on top of an existing\nmachine learning based Python package. We made the model updatable and\nscalable to accommodate an ever increasing patient record file.\n\nObjective:\n\nTo produce an improved identification of a continuously increasing\npatient records database.\n\nProblem:\n\nProper identification of duplicated patient information remains an\narduous problem for hospitals, pharmacies and service providers.\nSimple matching of these records does not result in the correct\nidentification of existing duplicates for various reasons such as\nnoisy and incomplete records.",
"duration": 2290,
"recorded": "2016-09-15",
"speakers": [
"Jaafar Ben-Abdallah"
],
"thumbnail_url": "https://i.ytimg.com/vi/Eclyv9SiQiE/hqdefault.jpg",
"title": "Scalable Patient Records De-duplication using machine learning",
"videos": [
{
"type": "youtube",
"url": "https://www.youtube.com/watch?v=Eclyv9SiQiE"
}
]
}