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WHY THIS WORK IS REMARKABLE
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WHAT THIS WORK DEMONSTRATES
WHAT
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WHAT THIS WORK DEMONSTRATES
WHAT
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WHAT THIS WORK DEMONSTRATES
WHAT
WHY THIS WORK IS REMARKABLE
WHAT WAS THE CONSEQUENCE
WHAT THIS WORK DEMONSTRATES
WHAT
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WHAT THIS WORK DEMONSTRATES
WHAT
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WHAT
Identification of Associated Factors in Major Adverse Cardiovascular Events in Coronary Type 2 Diabetic Patients.
WHY THIS WORK IS REMARKABLE
Because a national scientific society lead the investigation and used AI to solve a clinical question.
WHAT WAS THE CONSEQUENCE
Awareness about the role of statins in major adverse cardiovascular events among diabetic patients has been arisen.
WHAT THIS WORK DEMONSTRATES
That AI and Natural Language Processing are ideal tools for the identification of risk factors in chronic diseases.
WHAT
Prevalence of cancer among patients with hypothyroidism.
WHY THIS WORK IS REMARKABLE
Because using AI and machine learning, it could be addressed the intersection of two clinical entities which are normally separate and difficult to assess jointly.
WHAT WAS THE CONSEQUENCE
There is new awareness about the possibility of developing cancer among patients with hypothyroidism.
WHAT THIS WORK DEMONSTRATES
Natural Language Processing applied to Electronic Medical Records reuse represents an excellent tool in order to intersect the epidemiology of different clinical entities.
WHAT
Patient journey of individuals tested for HVC.
WHY THIS WORK IS REMARKABLE
Because using Natural Language Processing a complete population from 6 hospitals, including 2,440,358 patients, could be scrutinized, finding every patient tested for HVC, across all departments.
WHAT WAS THE CONSEQUENCE
It was signalled the lack of testing in certain departments, as long as the lack of RNA ang genotype determination.
WHAT THIS WORK DEMONSTRATES
Natural Language Processing on Electronic Medical Records as a unique tool for patient journey evaluation.
WHAT
Hospital prevalence and characterization of diabetes type 1 and type 2.
WHY THIS WORK IS REMARKABLE
Because an AI model was developed to be able to identify type 1 and type 2 diabetes even when it wasn’t explicitly stated in the clinical notes.
Because 638,730 individuals with diabetes could be evaluated without any human data collection.
WHAT WAS THE CONSEQUENCE
There is new evidence about the actual epidemiology of diabetes among hospital populations.
WHAT THIS WORK DEMONSTRATES
AI and Natural Language Processing as an asset to evaluate massive amounts of patients in prevalent diseases, saving countless hours of human labor.
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WHAT THIS WORK DEMONSTRATES
WHAT
WHY THIS WORK IS REMARKABLE
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WHAT THIS WORK DEMONSTRATES
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