Development of a deep learning system for preventive detection of heart failure due to pulmonary disease

Axel Frederick Félix Jiménez, Isaul Ibarra Belmonte, Vania Stephany Sánchez Lee, Uziel Jaramillo Avila, Ezra Federico Parra González
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Abstract

In the context of addressing the problem of people who do not undergo a diagnosis of heart failure due to pulmonary conditions on time, a solution to this problem would allow early preventive detection to avoid the development of severe disease efficiently. Our approach employs the use of medical data retrieved from the user to determine and predict whether there is a likelihood of a potential condition. To solve this problem, according to a users medical measurement history, a deep learning model can be implemented to determine a preventive diagnosis or otherwise to follow up on an already detected condition. By posing the problem as a classification task, it can be taken advantage of a deep learning model focused on heart failure or pulmonary conditions to make a preliminary diagnosis and determine if there are signs of any symptomatology.
开发用于预防肺部疾病引起的心力衰竭的深度学习系统
在解决没有及时接受肺部疾病导致的心力衰竭诊断的人的问题方面,解决这一问题将使早期预防性检测成为可能,从而有效地避免严重疾病的发展。我们的方法是使用从用户那里检索到的医疗数据来确定和预测是否存在潜在疾病的可能性。为了解决这个问题,根据用户的医疗测量历史,可以实施一个深度学习模型来确定预防性诊断或以其他方式跟踪已经检测到的情况。通过将问题作为分类任务,它可以利用专注于心力衰竭或肺部疾病的深度学习模型进行初步诊断,并确定是否存在任何症状的迹象。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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