Extending electronic medical records vector models with knowledge graphs to improve hospitalization prediction

Electronic health records (EHRs) are an increasingly important source of information for healthcare professionals and researchers. However, EHRs are often fragmented, unstructured, and difficult to analyze due to the heterogeneity of the data sources and the sheer volume of information. Knowledge graphs have emerged as a powerful tool for capturing and representing complex relationships within large datasets. In this study, we explore the use of knowledge graphs to capture and represent complex relationships within EHRs. Specifically, we address the following research question: Can a knowledge graph created using the MIMIC III dataset and GraphDB effectively capture semantic relationships within EHRs and enable more efficient and accurate data analysis? We map the MIMIC III dataset to an ontology using text refinement and Protege; then, we create a knowledge graph using GraphDB and use SPARQL queries to retrieve and analyze information from the graph. Our results demonstrate that knowledge graphs can effectively capture semantic relationships within EHRs, enabling more efficient and accurate data analysis. We provide examples of how our implementation can be used to analyze patient outcomes and identify potential risk factors. Our results demonstrate that knowledge graphs are an effective tool for capturing semantic relationships within EHRs, enabling a more efficient and accurate data analysis. Our implementation provides valuable insights into patient outcomes and potential risk factors, contributing to the growing body of literature on the use of knowledge graphs in healthcare. In particular, our study highlights the potential of knowledge graphs to support decision-making and improve patient outcomes by enabling a more comprehensive and holistic analysis of EHR data. Overall, our research contributes to a better understanding of the value of knowledge graphs in healthcare and lays the foundation for further research in this area.

BackgroundIntensive Care Unit (ICU) readmissions represent both a health risk for patients,with increased mortality rates and overall health deterioration, and a financial burden for healthcare facilities. As healthcare became more data-driven with the introduction of Electronic Health Records (EHR), machine learning methods have been applied to predict ICU readmission risk. However, these methods disregard the meaning and relationships of data objects and work blindly over clinical data without taking into account scientific knowledge and context. Ontologies and Knowledge Graphs can help bridge this gap between data and scientific context, as they are computational artefacts that represent the entities of a domain and their relationships to each other in a formalized way.Methods and resultsWe have developed an approach that enriches EHR data with semantic annotations to ontologies to build a Knowledge Graph. A patient’s ICU stay is represented by Knowledge Graph embeddings in a contextualized manner, which are used by machine learning models to predict 30-days ICU readmissions. This approach is based on several contributions: (1) an enrichment of the MIMIC-III dataset with patient-oriented annotations to various biomedical ontologies; (2) a Knowledge Graph that defines patient data with biomedical ontologies; (3) a predictive model of ICU readmission risk that uses Knowledge Graph embeddings; (4) a variant of the predictive model that targets different time points during an ICU stay. Our predictive approaches outperformed both a baseline and state-of-the-art works achieving a mean Area Under the Receiver Operating Characteristic Curve of 0.827 and an Area Under the Precision-Recall Curve of 0.691. The application of this novel approach to help clinicians decide whether a patient can be discharged has the potential to prevent the readmission of 40% of Intensive Care Unit patients, without unnecessarily prolonging the stay of those who would not require it.ConclusionThe coupling of semantic annotation and Knowledge Graph embeddings affords two clear advantages: they consider scientific context and they are able to build representations of EHR information of different types in a common format. This work demonstrates the potential for impact that integrating ontologies and Knowledge Graphs into clinical machine learning applications can have.

Electronic medical records (EMRs) of patients with lung cancer (LC) capture a variety of health factors. Understanding the distribution of these factors will help identify key factors for risk prediction in preventive screening for LC. We aimed to generate an integrated biomedical graph from EMR data and Unified Medical Language System (UMLS) ontology for LC, and to generate an LC health factor distribution from a hospital EMR of approximately 1 million patients. The data were collected from 2 sets of 1397 patients with and those without LC. A patient-centered health factor graph was plotted with 108,000 standardized data, and a graph database was generated to integrate the graphs of patient health factors and the UMLS ontology. With the patient graph, we calculated the connection delta ratio (CDR) for each of the health factors to measure the relative strength of the factor's relationship to LC. The patient graph had 93,000 relations between the 2794 patient nodes and 650 factor nodes. An LC graph with 187 related biomedical concepts and 188 horizontal biomedical relations was plotted and linked to the patient graph. Searching the integrated biomedical graph with any number or category of health factors resulted in graphical representations of relationships between patients and factors, while searches using any patient presented the patient's health factors from the EMR and the LC knowledge graph (KG) from the UMLS in the same graph. Sorting the health factors by CDR in descending order generated a distribution of health factors for LC. The top 70 CDR-ranked factors of disease, symptom, medical history, observation, and laboratory test categories were verified to be concordant with those found in the literature. By collecting standardized data of thousands of patients with and those without LC from the EMR, it was possible to generate a hospital-wide patient-centered health factor graph for graph search and presentation. The patient graph could be integrated with the UMLS KG for LC and thus enable hospitals to bring continuously updated international standard biomedical KGs from the UMLS for clinical use in hospitals. CDR analysis of the graph of patients with LC generated a CDR-sorted distribution of health factors, in which the top CDR-ranked health factors were concordant with the literature. The resulting distribution of LC health factors can be used to help personalize risk evaluation and preventive screening recommendations.

INTRODUCTION: Postpartum depression (PPD) affects 1 in 7 women with devastating effects when untreated. To improve compliance with screening recommendations, we used data from patient electronic health records (EHRs) and developed machine learning (ML) algorithms to identify women at high risk of developing PPD who can be preemptively screened. METHODS: We performed a retrospective study using the EHR data from an academic medical center in New York City (NYC) between January 2015 and June 2018. ML algorithms were applied to calculate the risk for PPD at 12, 18, 24, and 30 weeks of pregnancy gestation, and to compare with real life development of PPD. The algorithms were further validated using the EHR data from the NYC Clinical Data Research Network (NYC-CDRN) between 2004–2017. RESULTS: Mental Health history and active symptoms, marital status, race, use of beta blockers and antihistamines, and the number of ED visits were among the most predictive features. A total of 15,97 deliveries in the WCM and 53,92 deliveries in the NYC-CDRN were included. The highest area under the curve (AUC) was 0.93 with a sensitivity of 0.83 ad specificity of 0.96. The AUC computed using NYC-CDRN data was 0.853, with the sensitivity at 0.84 and 0.77. CONCLUSION: Our study validates ML algorithms to predict the risk of developing PPD based on information in the EHR. Our ongoing work is focused on using this approach to facilitate patient-provider communication and decision-making.

The effective analysis of high-dimensional Electronic Health Record (EHR) data, with substantial potential for healthcare research, presents notable methodological challenges. Employing predictive modeling guided by a knowledge graph (KG), which enables efficient feature selection, can enhance both statistical efficiency and interpretability. While various methods have emerged for constructing KGs, existing techniques often lack statistical certainty concerning the presence of links between entities, especially in scenarios where the utilization of patient-level EHR data is limited due to privacy concerns. In this paper, we propose the first inferential framework for deriving a sparse KG with statistical guarantee based on a dynamic log-linear topic model. Within this model, the KG embeddings are estimated by performing singular value decomposition on the empirical pointwise mutual information matrix, offering a scalable solution. We then establish entrywise asymptotic normality for the KG low-rank estimator, enabling the recovery of sparse graph edges with controlled type I error. Our work uniquely addresses the under-explored domain of statistical inference about non-linear statistics under the low-rank temporal dependent models, a critical gap in existing research. We validate our approach through extensive simulation studies and then apply the method to real-world EHR data in constructing clinical KGs and generating clinical feature embeddings.

Cardiovascular conditions (eg, cardiac and coronary conditions, hypertensive disorders of pregnancy, and cardiomyopathies) were the leading cause of maternal mortality between 2017 and 2019. The United States has the highest maternal mortality rate of any high-income nation, disproportionately impacting those who identify as non-Hispanic Black or Hispanic. Novel clinical approaches to the detection and diagnosis of cardiovascular conditions are therefore imperative. Emerging research is demonstrating that machine learning (ML) is a promising tool for detecting patients at increased risk for hypertensive disorders during pregnancy. However, additional studies are required to determine how integrating ML and big data, such as electronic health records (EHRs), can improve the identification of obstetric patients at higher risk of cardiovascular conditions. This study aimed to evaluate the capability and timing of a proprietary ML algorithm, Healthy Outcomes for all Pregnancy Experiences-Cardiovascular-Risk Assessment Technology (HOPE-CAT), to detect maternal-related cardiovascular conditions and outcomes. Retrospective data from the EHRs of a large health care system were investigated by HOPE-CAT in a virtual server environment. Deidentification of EHR data and standardization enabled HOPE-CAT to analyze data without pre-existing biases. The ML algorithm assessed risk factors selected by clinical experts in cardio-obstetrics, and the algorithm was iteratively trained using relevant literature and current standards of risk identification. After refinement of the algorithm's learned risk factors, risk profiles were generated for every patient including a designation of standard versus high risk. The profiles were individually paired with clinical outcomes pertaining to cardiovascular pregnancy conditions and complications, wherein a delta was calculated between the date of the risk profile and the actual diagnosis or intervention in the EHR. In total, 604 pregnancies resulting in birth had records or diagnoses that could be compared against the risk profile; the majority of patients identified as Black (n=482, 79.8%) and aged between 21 and 34 years (n=509, 84.4%). Preeclampsia (n=547, 90.6%) was the most common condition, followed by thromboembolism (n=16, 2.7%) and acute kidney disease or failure (n=13, 2.2%). The average delta was 56.8 (SD 69.7) days between the identification of risk factors by HOPE-CAT and the first date of diagnosis or intervention of a related condition reported in the EHR. HOPE-CAT showed the strongest performance in early risk detection of myocardial infarction at a delta of 65.7 (SD 81.4) days. This study provides additional evidence to support ML in obstetrical patients to enhance the early detection of cardiovascular conditions during pregnancy. ML can synthesize multiday patient presentations to enhance provider decision-making and potentially reduce maternal health disparities.

In recent years, with the better availability of medical data such as Electronic Health Records (EHR), more and more data mining models have been developed to explore the data-driven insights for better human health. However, there are many challenges for analyzing EHR such as high-dimensionality, temporality, sparsity, etc., which make the data-driven models less reliable. Medical knowledge graph (MKG), which encodes comprehensive knowledge about the medical concepts and relationships extracted from medical literature, holds great promise to regularize the data-driven models as prior knowledge. Nonetheless, the MKGs are typically not complete, which limits its utility in helping with the data mining process. In this paper, we propose a mutual enhancement framework MendMKG for predictive modeling of clinical events with both EHR and MKG. In particular, MendMKG first conducts a self-supervised learning strategy to simultaneously pre-train a graph attention network for embedding nodes and complete the MKG. It iteratively performs (1) an embedding-based knowledge graph completion module to derive missing edges, (2) and a reconstruction module of unlabeled EHR data to select high-quality ones from these edges, which would be further appended to the MKG to update the embedding model. Through the iterations, the two modules mutually benefit each other. Then, MendMKG uses the pre-trained graph attention network and the updated MKG to generate the visit embeddings to represent patient’s historical visits, and predict the diagnosis in future visit, through a fine-tuning approach. Experimental results on real world EHR corpus are provided to demonstrate the superiority of the proposed framework, compared to a series of state-of-the-art baselines. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The source code and knowledge graph data have been anonymously uploaded to https://github.com/1317375434/MendMKG.

Web Semantics is one of the prominent domain in the current era of web. This web comprises of linked open data, which is an interconnection of related data over web and the base of data representation is Resource Description Framework (RDF). Applying machine learning algorithms will enhance the capabilities of semantic web over the concepts to visualize and analyze data over web. It will integrate the capabilities of web semantics and machine learning. In this paper, classification on a sample RDF dataset has been implemented and their results have been analyzed. This will give researcher a baseline to work with machine learning algorithms on semantic web data. The machine learning algorithms like decision tree classifier, random forest classifier along with artificial neural network classification has been analyzed in this paper along with their accuracy measures. The integration of machine learning and semantic web, based on RDF based knowledge graphs is the singularity of the proposed work and represented using experimental results.

Machine learning algorithms are occupying a larger space in medical and urology applications. However, typical medical physicians are not trained on these technologies and do not master the possibilities offered by these tools, to imagine their applications in the medical field. This manuscript is indented to be a guide in the use of machine learning in different urology applications, and to demystify the available machine learning and artificial intelligence algorithms. This manuscript reviews some of their applications and potential applications to the medical and urology field. Multiple works are published on the use of machine learning in urology, with performance demonstrated to be noninferior to human experts on multiple occasions. However, the major part of the machine learning publications in urology applications are concentrated on diagnosis and/or prognosis. Advanced machine learning algorithms based on agentic artificial intelligence, able to perform decisions and causality-based treatment optimization, are rarely put to use in urology. The democratization of advanced machine learning technologies in the medical fields can accelerate the adoption of these techniques, and potentially improve the patient care through relevant suggestive decision making. This work aims to demystify the machine learning tools for medical applications, facilitate decision making and adoption of the correct tools for the correct applications, and places a roadmap for the future of machine learning in the enhancement of patient care in urology.

Racial and Ethnic Disparities in Predictive Accuracy of Machine Learning Algorithms Developed Using a National Database for 30-Day Complications Following Total Joint Arthroplasty

Current approaches for early identification of individuals at high risk for autism spectrum disorder (ASD) in the general population are limited, and most ASD patients are not identified until after the age of 4. This is despite substantial evidence suggesting that early diagnosis and intervention improves developmental course and outcome. The aim of the current study was to test the ability of machine learning (ML) models applied to electronic medical records (EMRs) to predict ASD early in life, in a general population sample. We used EMR data from a single Israeli Health Maintenance Organization, including EMR information for parents of 1,397 ASD children (ICD-9/10) and 94,741 non-ASD children born between January 1st, 1997 and December 31st, 2008. Routinely available parental sociodemographic information, parental medical histories, and prescribed medications data were used to generate features to train various ML algorithms, including multivariate logistic regression, artificial neural networks, and random forest. Prediction performance was evaluated with 10-fold cross-validation by computing the area under the receiver operating characteristic curve (AUC; C-statistic), sensitivity, specificity, accuracy, false positive rate, and precision (positive predictive value [PPV]). All ML models tested had similar performance. The average performance across all models had C-statistic of 0.709, sensitivity of 29.93%, specificity of 98.18%, accuracy of 95.62%, false positive rate of 1.81%, and PPV of 43.35% for predicting ASD in this dataset. We conclude that ML algorithms combined with EMR capture early life ASD risk as well as reveal previously unknown features to be associated with ASD-risk. Such approaches may be able to enhance the ability for accurate and efficient early detection of ASD in large populations of children.

Rationale: Interstitial lung disease (ILD) affects 40-75% of patients with systemic sclerosis (SSc) and is the leading cause of death in this population. SSc-ILD is a heterogeneous disease with a variable clinical course. Current available therapies preserve lung function; however, benefits appear to be modest and are counterbalanced by toxicity. While biomarkers have been reported for progressive disease, their utility is limited. We hypothesize that machine learning (ML) could improve mortality prediction in patients with SSc and SSc-ILD by leveraging readily available electronic health record (EHR) data. Methods: We used data from participants with SSc recruited to the Northwestern University Scleroderma Registry from 1996-2024. EHR data—clinical, laboratory, and spirometric—were extracted, and features were selected. ILD diagnosis was assigned by adjudication of chest CT reports. Multiple ML algorithms were tested to build models to predict mortality (or lung transplant) using EHR features in the entire SSc cohort and a subgroup of those with SSc-ILD. A 70:10:20 patient-wise split for training:validation:testing was implemented. Optuna was employed for hyperparameter optimization on the validation set, and the best model was selected. Receiver operating characteristic (ROC) analysis was used to evaluate each model on the held-out test set. Feature importance was assessed through ablation analysis. Results: 1,170 participants with SSc were available for analysis, encompassing 9,191 person-years of observation. 193 (16%) participants died during the observation period. 709 (61%) had CT data available, and 454 (64%) of these had SSc-ILD for subgroup analysis. 109 (24%) participants with SSc-ILD died during the observation period. EHR features predicted mortality in SSc patients within one (AUC=0.91), three (AUC=0.89), and five years (AUC=0.82) (Figure 1A). Ablation analysis identified features highly predictive of one-year mortality in SSc that are routinely assessed but rarely utilized, including blood counts and chemistries (Figure 1B). Similarly, ML algorithms used EHR features to predict mortality in a subgroup of patients with SSc-ILD within one (AUC=0.71), three (AUC=0.73), and five years (AUC=0.80) (Figure 1C). Ablation analysis again identified predictive features for one-year mortality in those with SSc-ILD (Figure 1D). Conclusions: ML analysis of readily available EHR data predicts mortality in those with SSc and SSc-ILD with high sensitivity and specificity. Ablation analyses identified features predictive of one-year mortality that are routinely collected but rarely assessed to ascertain risk. These models could assist in clinical decision-making, particularly regarding treatment. Future research will integrate quantitative imaging features and employ deep learning models to further improve model performance.

Artificial Intelligence (AI) methods, which are often based on Machine Learning (ML) algorithms, are also applied in the healthcare domain to provide predictions to physicians and patients based on electronic health records (EHRs), such as history of laboratory values, applied procedures and diagnoses. The question about these predictions “Why Should I Trust You?” encapsulates the issue with ML black boxes. Therefore, explaining the reasons for these ML predictions to physicians and patients is crucial to allow them to decide whether the prediction is applicable or not. In this paper, we explained and evaluated two prediction explanation methods for healthcare professionals (physicians and nurses). We compared two model-agnostic explanation methods based on global feature importance and local feature importance. We evaluated the user trust and reliance (UTR) for the explanation results of each method in a user study based on real patients’ electronic health records (EHR) and the feedback of healthcare professionals. Based on the user study, we observed that both methods have strengths and weaknesses according to the patients’ data, especially based on the data size of the patient. When the amount of data is small, global feature importance is enough to use. However, when the patient’s data size is big, using a local feature importance method makes more sense. As future work, we will develop a hybrid explanation method (by combining these methods automatically with a smart setting) to obtain higher and more stable performance results in terms of user trust and reliance.

Clinical decision support system for hypertension medication based on knowledge graph

This systematic literature review investigates how evolvable knowledge graphs (KGs) enhance manufacturing. Evolvable KGs adapt to changing knowledge by leveraging machine learning algorithms and human expertise to improve decision-making, operational efficiency, and predictive maintenance capabilities beyond the capabilities of prevailing solutions enabled by dynamic KGs. This review maps the existing literature based on the stages of the KG development process. The main results are an updated model of the creation and maintenance of evolvable KGs in manufacturing and a literature-based synthesis of learning approaches for enabling KG evolution through knowledge processing and knowledge utilization. The results contribute to updating the KG development process model and synthesizing an understanding of evolvable KGs that utilize the observed learning approaches on knowledge processing to change KG relations and nodes, and three learning approaches through knowledge utilization (human-guided, machine-driven, and human-machine collaborative knowledge updates). Despite emerging advances, challenges persist in quality assurance, process planning, and integration of human expertise. The findings advocate addressing these issues to promote greater adoption and optimization of KGs in manufacturing. By deepening the understanding of how KGs can evolve through learning, this review sets a conceptual basis for future research to develop more dynamic and intelligent KG-based manufacturing systems.