Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:2606-2610. doi: 10.1109/EMBC48229.2022.9871867.
Medical practices are engaged and motivated by new technologies and methods to enhance patient care as efficiently as possible. These new methods and technologies give way for medical practices and clinicians to have the insight, comprehension, and projections to develop better decisions and overall levels of care. In this paper, we propose a model, PatientCentered-LSTM (or PC-LSTM), using the states of the LSTM model to produce a novel, ontology-based state system for data incompleteness. The overall architecture and system design are based around utilizing the hidden and cell states of the LSTM model to produce a network of states for each of the corresponding hierarchies in an Electronic Health Record (EHR) system. The resulting methodology allows for an accurate and precise approach to predicting data incompleteness in electronic health records. Clinical relevance- The method presented uses the hierarchical nature of electronic health record systems to positively influence the analysis of its data completeness; thereby, increasing the possibility of improved healthcare outcomes.
医疗实践正在采用新技术和方法，以尽可能有效地提高患者护理水平。这些新方法和技术为医疗实践和临床医生提供了洞察力、理解和预测能力，以做出更好的决策和提高整体护理水平。在本文中，我们提出了一个模型，即基于患者的 LSTM（或 PC-LSTM），利用 LSTM 模型的状态来产生一个新颖的基于本体论的数据不完整状态系统。整体架构和系统设计基于利用 LSTM 模型的隐藏状态和细胞状态，为电子健康记录 (EHR) 系统中的每个对应层次结构生成一个状态网络。所提出的方法允许对电子健康记录中的数据不完整进行准确和精确的预测。临床相关性——所提出的方法利用电子健康记录系统的层次结构，积极影响其数据完整性的分析；从而提高改善医疗保健结果的可能性。
