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Francisco Lopez-Jimenez
Mayo Clinic
CardiologyBody mass indexObesityDiabetes mellitusMedicine
358Publications
51H-index
11.2kCitations
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Publications 363
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#2Jose R. Medina-Inojosa (Mayo Clinic)H-Index: 11
Last. Sean M. Caples (Mayo Clinic)H-Index: 27
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#1William Breen (Mayo Clinic)H-Index: 3
#2Rickey E. Carter (Mayo Clinic)H-Index: 52
Last. B.J. Stish (Mayo Clinic)H-Index: 1
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#1John A. Batsis (Dartmouth College)H-Index: 28
#2Curtis L. Petersen (Dartmouth College)H-Index: 2
Last. Stephen J. Bartels (Harvard University)H-Index: 49
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BACKGROUND: Older persons with obesity aged 65+ residing in rural areas have reduced access to weight management programs due to geographic isolation. The ability to integrate technology into health promotion interventions shows a potential to reach this underserved population. METHODS: A 12-week pilot in 28 older rural adults with obesity (body mass index (BMI)>/=30kg/m2) was conducted at a community aging center. The intervention consisted of individualized, weekly dietitian visits focusing on...
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#1Francisco Lopez-Jimenez (Mayo Clinic)H-Index: 51
#2Bernard J. Gersh (Mayo Clinic)H-Index: 132
1 CitationsSource
#1Francisco Lopez-Jimenez (Mayo Clinic)H-Index: 51
#2Zachi I. Attia (Mayo Clinic)H-Index: 8
Last. Jose R. Medina-Inojosa (Mayo Clinic)H-Index: 11
view all 17 authors...
Abstract Artificial intelligence (AI) is a nontechnical, popular term that refers to machine learning of various types but most often to deep neural networks. Cardiology is at the forefront of AI in medicine. For this review, we searched PubMed and MEDLINE databases with no date restriction using search terms related to AI and cardiology. Articles were selected for inclusion on the basis of relevance. We highlight the major achievements in recent years in nearly all areas of cardiology and under...
1 CitationsSource
#1Conor Senecal (Mayo Clinic)H-Index: 2
#2Maria L. Collazo-Clavell (Mayo Clinic)H-Index: 27
Last. Francisco Lopez-Jimenez (Mayo Clinic)H-Index: 51
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Metabolic Syndrome represents a constellation of findings known to increase cardiovascular risk and all-cause mortality and is becoming more frequent in China. Smartphone applications may provide a cost-effective method to engage in weight loss and improve metabolic syndrome components. A
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#1Šárka KunzováH-Index: 8
#2Andrea MaugeriH-Index: 13
Last. Pedro Marques-Vidal (UNIL: University of Lausanne)H-Index: 60
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Comparisons among countries can help to identify opportunities for the reduction of inequalities in cardiometabolic health. The present cross-sectional analysis and meta-analysis aim to address to what extent obesity traits, socioeconomic, and behavioral factors determine poor metabolic health across body mass index (BMI) categories in two urban population-based samples from Central Europe. Data from the CoLaus (~6,000 participants; Lausanne, Switzerland) and the Kardiovize Brno 2030 (~2,000 par...
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#1Jose R. Medina-Inojosa (Mayo Clinic)H-Index: 11
#2Michal Shelly (Mayo Clinic)
Last. Francisco Lopez-Jimenez (Mayo Clinic)H-Index: 51
view all 7 authors...
The ACC/AHA Pooled Cohort Equation (PCE) for Atherosclerotic cardiovascular disease (ASCVD) has shown modest accuracy. We assessed if machine learning algorithms (MLA) could improve PCE performance with traditional and selected enriched clinical features. We tested MLA in a community cohort of
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#1Sanskriti Shrivastava (Mayo Clinic)
#2Michal Shelly (Mayo Clinic)
Last. Paul A. Friedman (Mayo Clinic)H-Index: 54
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Dilated cardiomyopathy (DCM) can be inherited and guidelines recommend screening first-degree relatives. However, screening with echocardiography is limited due to availability and cost. Recently, artificial intelligence-enabled electrocardiography (AI-ECG) has shown promise in detecting left
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#1Conor Senecal (Mayo Clinic)H-Index: 2
#2Robert Jay Widmer (BCM: Baylor College of Medicine)
Last. Francisco Lopez-Jimenez (Mayo Clinic)H-Index: 51
view all 7 authors...
Importance. Obesity is a worsening epidemic worldwide. Effective and accessible weight loss programs to combat obesity on a large scale are warranted, but a need for frequent face-to-face care might impose a limitation. Objective. To evaluate whether individuals following a weight loss program based on a mobile application, wireless scale, and nutritional program but no face-to-face care can achieve clinically significant weight loss in a large cohort. Design. Retrospective observational analysi...
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