2019
DOI: 10.1016/j.cardfail.2019.07.151
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A Machine Learning Model for the Systematic Identification of Wild-Type Transthyretin Cardiomyopathy

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Cited by 6 publications
(4 citation statements)
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“…Expert consensus recommendations suggest that a diagnosis of ATTR-CM should be considered in patients with signs of heart failure and one or more of these red flags [7, 8, 11, 15, 17-19, 27, 28]. Research and predictive analytics are underway to create screening and diagnostic clinical decision support tools for ATTR-CM based on these red flags [2,11,[27][28][29]. These initiatives may improve the speed and accuracy of ATTR-CM diagnosis.…”
Section: Discussionmentioning
confidence: 99%
“…Expert consensus recommendations suggest that a diagnosis of ATTR-CM should be considered in patients with signs of heart failure and one or more of these red flags [7, 8, 11, 15, 17-19, 27, 28]. Research and predictive analytics are underway to create screening and diagnostic clinical decision support tools for ATTR-CM based on these red flags [2,11,[27][28][29]. These initiatives may improve the speed and accuracy of ATTR-CM diagnosis.…”
Section: Discussionmentioning
confidence: 99%
“… 63 Moreover, work is ongoing on artificial intelligence and machine learning models using electronic health records and imaging technologies capable of identifying patients at risk of having ATTR-CM. 128 These models may eventually be used to increase the index of suspicion of ATTR-CM to support prompt diagnosis of the disease in the clinical practice setting. Finally, educational initiatives and research/decision support tools are in development for systematic adoption by health systems to help ensure that patients with rare diseases such as ATTR-CM are not overlooked.…”
Section: Discussionmentioning
confidence: 99%
“…It has been proposed that patients who have risk factors such as male gender, age ≥65 yrs, heart failure symptoms, symmetric left ventricular (LV) hypertrophy, and moderately depressed or HFpEF should undergo screening for amyloidosis [17]. Machine learning models have also been investigated and may provide novel insights into early diagnosis of ATTRwt patients [23]. ere are other clinical implications as well.…”
Section: Discussionmentioning
confidence: 99%