Sathish Kumar L, Routray Sidheswar, Prabu A V, Rajasoundaran S, Pandimurugan V, Mukherjee Amrit, Al-Numay Mohammed S
School of Computing Science and Engineering, VIT University, Bhopal, India.
Department of Computer Science and Engineering, School of Engineering, Indrashil University, Rajpur, Mehsana, Gujarat India.
Cluster Comput. 2022 Aug 23:1-13. doi: 10.1007/s10586-022-03697-x.
Patient health record analysis models assist the medical field to understand the current stands and medical needs. Similarly, collecting and analyzing the disease features are the best practice for encouraging medical researchers to understand the research problems. Various research works evolve the way of medical data analysis schemes to know the actual challenges against the diseases. The computer-based diagnosis models and medical data analysis models are widely applied to have a better understanding of different diseases. Particularly, the field of medical electronics needs appropriate health indicator extraction models in near future. The existing medical schemes support baseline solutions but lack optimal hypothesis-based solutions. This work describes the optimal hypothesis model and Akin procedures for health record users, to aid health sectors in clinical decision-making on health indications. This work proposes Medical Hypothesis and Health Indicators Extraction from Electronic Medical Records (EMR) and International Classification of Diseases (ICD-10) patient examination database using the Akin Method and Friendship method. In this Health Indicators and Disease Symptoms Extraction (HIDSE), the evidence checking procedures find and collect all possible medical evidence from the existing patient examination report. Akin Method is making the hypothesis decision from count-based evidence principles. The health indicators extraction scheme extracts all relevant information based on the health indicators query and partial input. Similarly, the friendship method is used for making information associations between medical data attributes. This Akin-Friendship model helps to build hypothesis structures and trait-based feature extraction principles. This is called as Composite Akin Friendship Model (CAFM). This proposed model consists of various test cases for developing the medical hypothesis systems. On the other hand, it provides limited accuracy in disease classification. In this regard, the proposed HIDSE implements Deep Learning (DL) based Akin Friendship Method (DLAFM) for improving the accuracy of this medical hypothesis model. The proposed DLAFM, Convolutional Neural Networks (CNN) associated Legacy Prediction Model for Health Indicator (LPHI) is developed to tune the CAFM principles. The results show the proposed health indicator extraction scheme has 8-10% of better system performance than other existing techniques.
患者健康记录分析模型有助于医学领域了解当前状况和医疗需求。同样，收集和分析疾病特征是促使医学研究人员理解研究问题的最佳实践。各种研究工作改进了医学数据分析方案的方式，以了解针对疾病的实际挑战。基于计算机的诊断模型和医学数据分析模型被广泛应用，以更好地了解不同疾病。特别是，医学电子领域在不久的将来需要合适的健康指标提取模型。现有的医疗方案支持基线解决方案，但缺乏基于最优假设的解决方案。这项工作描述了针对健康记录用户的最优假设模型和阿金程序，以帮助卫生部门在健康指标的临床决策中提供支持。这项工作提出了使用阿金方法和友谊方法从电子病历（EMR）和国际疾病分类（ICD - 10）患者检查数据库中提取医学假设和健康指标。在这个健康指标与疾病症状提取（HIDSE）中，证据检查程序从现有的患者检查报告中查找并收集所有可能的医学证据。阿金方法基于基于计数的证据原则做出假设决策。健康指标提取方案根据健康指标查询和部分输入提取所有相关信息。同样，友谊方法用于在医学数据属性之间建立信息关联。这种阿金 - 友谊模型有助于构建假设结构和基于特征的特征提取原则。这被称为复合阿金友谊模型（CAFM）。这个提出的模型包含用于开发医学假设系统的各种测试用例。另一方面，它在疾病分类方面提供的准确性有限。在这方面，提出的HIDSE实施基于深度学习（DL）的阿金友谊方法（DLAFM）以提高这个医学假设模型的准确性。提出的DLAFM，即与用于健康指标的传统预测模型（LPHI）相关联的卷积神经网络（CNN），用于调整CAFM原则。结果表明，提出的健康指标提取方案比其他现有技术具有8 - 10%的更好系统性能。
