论文标题
使用LSTM深度学习方法分析和预测来自射血分数和血清肌酐的心脏中风
Analysis and prediction of heart stroke from ejection fraction and serum creatinine using LSTM deep learning approach
论文作者
论文摘要
大数据和深度学习的结合是一项全球惊人的技术，如果正确使用，可以极大地影响任何目标。随着深度学习技术中大量医疗保健数据集和进步的可用性，系统现在可以很好地预测任何健康问题的未来趋势。从文献调查中，我们发现SVM用于预测心力衰竭的情况，而无需关联客观因素。利用电子健康记录（EHR）中重要历史信息的强度，我们利用长期记忆（LSTM）建立了一个智能和预测的模型，并根据该健康记录预测心力衰竭的未来趋势。因此，这项工作的基本承诺是使用基于患者的电子药用信息的LSTM来预测心脏的失败。我们已经分析了一个数据集，该数据集包含在Faisalabad心脏病学研究所和Faisalabad（巴基斯坦旁遮普邦）的盟军医院收集的299例心力衰竭患者的病历。这些患者由105名女性和194名男性及其年龄组成，范围为40岁和95岁。该数据集包含13个功能，这些功能报告了负责心力衰竭的临床，身体和生活方式信息。我们发现我们的分析趋势越来越大，这将有助于促进心中预测领域的知识。
The combination of big data and deep learning is a world-shattering technology that can greatly impact any objective if used properly. With the availability of a large volume of health care datasets and progressions in deep learning techniques, systems are now well equipped to predict the future trend of any health problems. From the literature survey, we found the SVM was used to predict the heart failure rate without relating objective factors. Utilizing the intensity of important historical information in electronic health records (EHR), we have built a smart and predictive model utilizing long short-term memory (LSTM) and predict the future trend of heart failure based on that health record. Hence the fundamental commitment of this work is to predict the failure of the heart using an LSTM based on the patient's electronic medicinal information. We have analyzed a dataset containing the medical records of 299 heart failure patients collected at the Faisalabad Institute of Cardiology and the Allied Hospital in Faisalabad (Punjab, Pakistan). The patients consisted of 105 women and 194 men and their ages ranged from 40 and 95 years old. The dataset contains 13 features, which report clinical, body, and lifestyle information responsible for heart failure. We have found an increasing trend in our analysis which will contribute to advancing the knowledge in the field of heart stroke prediction.
