Modeling electronic health record data using an end-to-end knowledge-graph-informed topic model

EHR coding with hybrid attention and features propagation on disease knowledge graph

And sentences associated with these attributes and relationships have been neglected. in this paper ►We propose an end-to-end model called Knowledge Graph Enhanced neural network (KGENet) to address the above shortcomings. specifically ►We first construct a disease knowledge graph that focuses on the multi-view disease attributes of ICD codes and the disease relationships between these codes. we also use a long sequence encoder to get EHR document representation. most importantly ►KGENet leverages multi-view disease attributes and structured disease relationships for knowledge enhancement through hybrid attention and graph propagation ►Respectively. furthermore ►The above processes can provide attribute-aware and relationship-augmented explainability for the model prediction results based on our disease knowledge graph. experiments conducted on the MIMIC-III benchmark dataset show that KGENet outperforms state-of-the-art models in both model effectiveness and explainability Electronic health record (EHR) coding assigns International Classification of Diseases (ICD) codes to each EHR document. These standard medical codes represent diagnoses or procedures and play a critical role in medical applications. However, EHR is a long medical text that is difficult to represent, the ICD code label space is large, and the labels have an extremely unbalanced distribution. These factors pose challenges to automatic EHR coding. Previous studies have not explored the disease attributes (e.g., symptoms, tests, medications) of ICD codes and the disease relationships (e.g., causes, risk factors, comorbidities) between them. In addition, the important roles of medical.

MixEHR-SurG: A joint proportional hazard and guided topic model for inferring mortality-associated topics from electronic health records

Survival models can help medical practitioners to evaluate the prognostic importance of clinical variables to patient outcomes such as mortality or hospital readmission and subsequently design personalized treatment regimes. Electronic Health Records (EHRs) hold the promise for large-scale survival analysis based on systematically recorded clinical features for each patient. However, existing survival models either do not scale to high dimensional and multi-modal EHR data or are difficult to interpret. In this study, we present a supervised topic model called MixEHR-SurG to simultaneously integrate heterogeneous EHR data and model survival hazard. Our contributions are three-folds: (1) integrating EHR topic inference with Cox proportional hazards likelihood; (2) integrating patient-specific topic hyperparameters using the PheCode concepts such that each topic can be identified with exactly one PheCode-associated phenotype; (3) multi-modal survival topic inference. This leads to a highly interpretable survival topic model that can infer PheCode-specific phenotype topics associated with patient mortality. We evaluated MixEHR-SurG using a simulated dataset and two real-world EHR datasets: the Quebec Congenital Heart Disease (CHD) data consisting of 8211 subjects with 75,187 outpatient claim records of 1767 unique ICD codes; the MIMIC-III consisting of 1458 subjects with multi-modal EHR records. Compared to the baselines, MixEHR-SurG achieved a superior dynamic AUROC for mortality prediction, with a mean AUROC score of 0.89 in the simulation dataset and a mean AUROC of 0.645 on the CHD dataset. Qualitatively, MixEHR-SurG associates severe cardiac conditions with high mortality risk among the CHD patients after the first heart failure hospitalization and critical brain injuries with increased mortality among the MIMIC-III patients after their ICU discharge. Together, the integration of the Cox proportional hazards model and EHR topic inference in MixEHR-SurG not only leads to competitive mortality prediction but also meaningful phenotype topics for in-depth survival analysis. The software is available at GitHub: https://github.com/li-lab-mcgill/MixEHR-SurG.

Graph neural networks for clinical risk prediction based on electronic health records: A survey

OBJECTIVE

This study aims to comprehensively review the use of graph neural networks (GNNs) for clinical risk prediction based on electronic health records (EHRs). The primary goal is to provide an overview of the state-of-the-art of this subject, highlighting ongoing research efforts and identifying existing challenges in developing effective GNNs for improved prediction of clinical risks.

METHODS

A search was conducted in the Scopus, PubMed, ACM Digital Library, and Embase databases to identify relevant English-language papers that used GNNs for clinical risk prediction based on EHR data. The study includes original research papers published between January 2009 and May 2023.

RESULTS

Following the initial screening process, 50 articles were included in the data collection. A significant increase in publications from 2020 was observed, with most selected papers focusing on diagnosis prediction (n = 36). The study revealed that the graph attention network (GAT) (n = 19) was the most prevalent architecture, and MIMIC-III (n = 23) was the most common data resource.

CONCLUSION

GNNs are relevant tools for predicting clinical risk by accounting for the relational aspects among medical events and entities and managing large volumes of EHR data. Future studies in this area may address challenges such as EHR data heterogeneity, multimodality, and model interpretability, aiming to develop more holistic GNN models that can produce more accurate predictions, be effectively implemented in clinical settings, and ultimately improve patient care.

Clinical Feature Vector Generation using Unsupervised Graph Representation Learning from Heterogeneous Medical Records

The diversity of patient information recorded on electronic medical records generally, presents a challenge for converting it into fixed-length vectors that align with clinical characteristics. To address this issue, this study aimed to utilize an unsupervised graph representation learning method to transform the unstructured inpatient information from electronic medical records into a fixed-length vector. Infograph, one of the unsupervised graph representation learning algorithms was applied to the graphed inpatient information, resulting in embedded vectors of fixed length. The embedded vectors were then evaluated for whether the clinical information was preserved in it. The results indicated that the embedded representation contained information that could predict readmission within 30 days, demonstrating the feasibility of using unsupervised graph representation learning to transform patient information into fixed-length vectors that retain clinical characteristics.

Development of a Knowledge Graph Embeddings Model for Pain

Pain is a complex concept that can interconnect with other concepts such as a disorder that might cause pain, a medication that might relieve pain, and so on. To fully understand the context of pain experienced by either an individual or across a population, we may need to examine all concepts related to pain and the relationships between them. This is especially useful when modeling pain that has been recorded in electronic health records. Knowledge graphs represent concepts and their relations by an interlinked network, enabling semantic and context-based reasoning in a computationally tractable form. These graphs can, however, be too large for efficient computation. Knowledge graph embeddings help to resolve this by representing the graphs in a low-dimensional vector space. These embeddings can then be used in various downstream tasks such as classification and link prediction. The various relations associated with pain which are required to construct such a knowledge graph can be obtained from external medical knowledge bases such as SNOMED CT, a hierarchical systematic nomenclature of medical terms. A knowledge graph built in this way could be further enriched with real-world examples of pain and its relations extracted from electronic health records. This paper describes the construction of such knowledge graph embedding models of pain concepts, extracted from the unstructured text of mental health electronic health records, combined with external knowledge created from relations described in SNOMED CT, and their evaluation on a subject-object link prediction task. The performance of the models was compared with other baseline models.

Transforming Clinical Information Systems: Empowering Healthcare through Telemedicine, Data Science, and Artificial Intelligence Applications

OBJECTIVE

In this synopsis, the editors of the Clinical Information Systems (CIS) section of the IMIA Yearbook of Medical Informatics overview recent research and propose a selection of best papers published in 2022 in the CIS field.

METHODS

The editors follow a systematic approach to gather relevant articles and select the best papers for the section. This year, they updated the query to incorporate the topic of telemedicine and removed search terms related to geographic information systems. The revised query resulted in a larger number of identified papers, necessitating the appointment of a third section editor to handle the increased workload. The editors narrowed the initial pool of articles to 15 candidate papers through a multi-stage selection process. At least seven independent reviews were collected for each candidate paper, and a selection meeting with the IMIA Yearbook editorial board led to the final selection of the best papers for the CIS section.

RESULTS

The query was carried out in mid-January 2023 and retrieved a deduplicated result set of 5,206 articles from 1,500 journals. This year, 15 papers were nominated as candidates, and four were finally selected as the best papers in the CIS section.Including telemedicine in the query resulted in a substantial increase in the number of papers found. The analysis highlights the growing convergence between clinical information systems and telemedicine, with mobile health (mHealth) technologies and data science applications gaining prominence. The selected candidate papers emphasize the practical impact of research efforts, focusing on patient-centric outcomes and benefits, including intelligent mobile health monitoring systems and AI-assisted decision-making in healthcare.

CONCLUSIONS

Looking ahead, the field of CIS is expected to continue evolving, driven by advances in telemedicine, mHealth technologies, data science, and AI integration, leading to more efficient, patient-oriented, and intelligent healthcare systems and overall improvement of global healthcare outcomes.