Amir Arnon, Beymer David, Grace Julia, Greenspan Hayit, Gruhl Daniel, Hobbs Allen, Pohl Kilian, Syeda-Mahmood Tanveer, Terdiman Joseph, Wang Fei
IBM Almaden Research Center, San Jose, CA, USA.
Stud Health Technol Inform. 2010;160(Pt 2):846-50.
Modern Electronic Medical Record (EMR) systems often integrate large amounts of data from multiple disparate sources. To do so, EMR systems must align the data to create consistency between these sources. The data should also be presented in a manner that allows a clinician to quickly understand the complete condition and history of a patient's health. We develop the AALIM system to address these issues using advanced multimodal analytics. First, it extracts and computes multiple features and cues from the patient records and medical tests. This additional metadata facilitates more accurate alignment of the various modalities, enables consistency check and empowers a clear, concise presentation of the patient's complete health information. The system further provides a multimodal search for similar cases within the EMR system, and derives related conditions and drugs information from them. We applied our approach to cardiac data from a major medical care organization and found that it produced results with sufficient quality to assist the clinician making appropriate clinical decisions.
现代电子病历(EMR)系统通常会整合来自多个不同来源的大量数据。为此,EMR系统必须对数据进行对齐,以在这些来源之间创建一致性。数据还应以一种使临床医生能够快速了解患者健康的完整状况和病史的方式呈现。我们开发了AALIM系统,使用先进的多模态分析来解决这些问题。首先,它从患者记录和医学测试中提取并计算多个特征和线索。这些额外的元数据有助于更准确地对齐各种模态,实现一致性检查,并使患者完整的健康信息能够清晰、简洁地呈现。该系统还在EMR系统中提供对相似病例的多模态搜索,并从中得出相关病症和药物信息。我们将我们的方法应用于一家大型医疗保健机构的心脏数据,发现其产生的结果质量足以协助临床医生做出适当的临床决策。