Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph

Abstract

Increasingly large electronic health records (EHRs) provide an opportunity to algorithmically learn medical knowledge. In one prominent example, a causal health knowledge graph could learn relationships between diseases and symptoms and then serve as a diagnostic tool to be refined with additional clinical input. Prior research has demonstrated the ability to construct such a graph from over 270,000 emergency department patient visits. In this work, we describe methods to evaluate a health knowledge graph for robustness. Moving beyond precision and recall, we analyze for which diseases and for which patients the graph is most accurate. We identify sample size and unmeasured confounders as major sources of error in the health knowledge graph. We introduce a method to leverage non-linear functions in building the causal graph to better understand existing model assumptions.Finally, to assess model generalizability, we extend to a larger set of complete patient visits within a hospital system. We conclude with a discussion on how to robustly extract medical knowledge from EHRs.

Machine learning and algorithmic fairness in public and population health

“…It is essential to identify what leads to disparate health outcomes to design interventions to mitigate disparities and improve the health of high-risk populations. This involves multiple types of task, such as measuring health outcomes 78,79 and disparities across social groups [80][81][82] , as well as designing policies to mitigate the disparities 82 . Figure 2 provides a framework for analysing how societal bias can result in biased predictions and where algorithmic fairness contributes (bottom two boxes in Fig.…”

Cognitive Computing-Based CDSS in Medical Practice

“…Apparently, medicine is a typical knowledge-driven subject compared with other domains like education or finance. Thus, the knowledge graph, which represents medical knowledge in an explicit and easy-toaccess way, has been widely used in health care studies [69][70][71], especially in cognitive computingbased CDSS. A knowledge graph mainly consists of ontology and relations.…”

Medical Knowledge Graph in Chinese Using Deep Semantic Mobile Computation Based on IoT and WoT

“…In recent years, as the maturity of technology and more emphasis on healthcare, it has become more and more popular to apply knowledge graph in the medical field and has attracted much attention from researchers in computer and medical to combine these two fields [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Rotmensch et al [11] in 2017 proposed learning a health knowledge graph from electronic medical records by using probabilistic.…”

“…Li et al [27] used an imperfect knowledge graph to classify rare diseases in 2019. Chen et al [16] in 2020 proposed robustly extracting medical knowledge from EHRs, in which nonlinear functions are adopted in building the causal graph to better understand exiting model assumptions. Zheng et al [17] put forward a dedicated knowledge graph benchmark for biomedical data mining in 2020.…”