Home >> Savana’s Reality Checks.
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WHY THIS WORK IS REMARKABLE
WHAT WAS THE CONSEQUENCE
WHAT THIS WORK DEMONSTRATES
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Prevalence and comorbidities of rheumatoid arthritis-associated interstitial lung disease.
WHY THIS WORK IS REMARKABLE
WHAT WAS THE CONSEQUENCE
WHAT THIS WORK DEMONSTRATES
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WHY THIS WORK IS REMARKABLE
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WHAT THIS WORK DEMONSTRATES
WHAT
WHY THIS WORK IS REMARKABLE
WHAT WAS THE CONSEQUENCE
WHAT THIS WORK DEMONSTRATES
WHAT
WHY THIS WORK IS REMARKABLE
WHAT WAS THE CONSEQUENCE
WHAT THIS WORK DEMONSTRATES
WHAT
WHY THIS WORK IS REMARKABLE
WHAT WAS THE CONSEQUENCE
WHAT THIS WORK DEMONSTRATES
WHAT
WHY THIS WORK IS REMARKABLE
WHAT WAS THE CONSEQUENCE
WHAT THIS WORK DEMONSTRATES
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WHY THIS WORK IS REMARKABLE
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WHAT THIS WORK DEMONSTRATES
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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 WAS THE CONSEQUENCE
WHAT THIS WORK DEMONSTRATES
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WHY THIS WORK IS REMARKABLE
WHAT WAS THE CONSEQUENCE
WHAT THIS WORK DEMONSTRATES
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Impact of Advanced Age on the Incidence of Major Adverse Cardiovascular Events in Patients with Type 2 Diabetes Mellitus and Stable Coronary Artery Disease in a Real-World Setting.
WHY THIS WORK IS REMARKABLE
Because it shows how the analysis of remarkable amounts of chronic patients using AI can help tell untold stories about the variables behind their evolution.
WHAT WAS THE CONSEQUENCE
Now patients with MACE and T2DM+CAD are better known at older age and in the presence of further comorbidities.
WHAT THIS WORK DEMONSTRATES
The generation of RWE using AI is an excellent tool for scientific tools to better know the populations they handle.
WHAT
Real-World Evidence on the Clinical Characteristics and Management of Patients with Chronic Lymphocytic Leukemia in Spain Using Natural Language Processing: The SRealCLL Study.
WHY THIS WORK IS REMARKABLE
Because it shed light about facts such as the increased use of precise target therapies and the decreased reliance on chemoimmunotherapy in both first-line and relapsed/refractory cases.
WHAT WAS THE CONSEQUENCE
Thanks to AI, there is now new evidence to help professionals move away from one-size-fits-all strategies and instead, optimize clinical management, tailoring treatments for each patient.
WHAT THIS WORK DEMONSTRATES
That in malignant hematological disease, the use of Natural Language Processing for Electronic Medical Records analysis helps the pathway towards precision medicine.
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