Journal of the American Medical Informatics Association最新文献

Objective: A proof-of-concept study aimed at designing and implementing Visual & Interactive Engagement With Electronic Records (VIEWER), a versatile toolkit for visual analytics of clinical data, and systematically evaluating its effectiveness across various clinical applications while gathering feedback for iterative improvements.

Materials and methods: VIEWER is an open-source and extensible toolkit that employs natural language processing and interactive visualization techniques to facilitate the rapid design, development, and deployment of clinical information retrieval, analysis, and visualization at the point of care. Through an iterative and collaborative participatory design approach, VIEWER was designed and implemented in one of the United Kingdom's largest National Health Services mental health Trusts, where its clinical utility and effectiveness were assessed using both quantitative and qualitative methods.

Results: VIEWER provides interactive, problem-focused, and comprehensive views of longitudinal patient data (n = 409 870) from a combination of structured clinical data and unstructured clinical notes. Despite a relatively short adoption period and users' initial unfamiliarity, VIEWER significantly improved performance and task completion speed compared to the standard clinical information system. More than 1000 users and partners in the hospital tested and used VIEWER, reporting high satisfaction and expressed strong interest in incorporating VIEWER into their daily practice.

Discussion: VIEWER provides a cost-effective enhancement to the functionalities of standard clinical information systems, with evaluation offering valuable feedback for future improvements.

Conclusion: VIEWER was developed to improve data accessibility and representation across various aspects of healthcare delivery, including population health management and patient monitoring. The deployment of VIEWER highlights the benefits of collaborative refinement in optimizing health informatics solutions for enhanced patient care.

Objective: To develop a framework that models the impact of electronic health record (EHR) systems on healthcare professionals' well-being and their relationships with patients, using interdisciplinary insights to guide machine learning in identifying value patterns important to healthcare professionals in EHR systems.

Materials and methods: A theoretical framework of EHR systems' implementation was developed using interdisciplinary literature from healthcare, information systems, and management science focusing on the systems approach, clinical decision-making, and interface terminologies.

Observations: Healthcare professionals balance personal norms of narrative and data-driven communication in knowledge creation for EHRs by integrating detailed patient stories with structured data. This integration forms 2 learning loops that create tension in the healthcare professional-patient relationship, shaping how healthcare professionals apply their values in care delivery. The manifestation of this value tension in EHRs directly affects the well-being of healthcare professionals.

Discussion: Understanding the value tension learning loop between structured data and narrative forms lays the groundwork for future studies of how healthcare professionals use EHRs to deliver care, emphasizing their well-being and patient relationships through a sociotechnical lens.

Conclusion: EHR systems can improve the healthcare professional-patient relationship and healthcare professional well-being by integrating norms and values into pattern recognition of narrative and data communication forms.

Objective: Machine learning applications for longitudinal electronic health records often forecast the risk of events at fixed time points, whereas survival analysis achieves dynamic risk prediction by estimating time-to-event distributions. Here, we propose a novel conditional variational autoencoder-based method, DySurv, which uses a combination of static and longitudinal measurements from electronic health records to estimate the individual risk of death dynamically.

Materials and methods: DySurv directly estimates the cumulative risk incidence function without making any parametric assumptions on the underlying stochastic process of the time-to-event. We evaluate DySurv on 6 time-to-event benchmark datasets in healthcare, as well as 2 real-world intensive care unit (ICU) electronic health records (EHR) datasets extracted from the eICU Collaborative Research (eICU) and the Medical Information Mart for Intensive Care database (MIMIC-IV).

Results: DySurv outperforms other existing statistical and deep learning approaches to time-to-event analysis across concordance and other metrics. It achieves time-dependent concordance of over 60% in the eICU case. It is also over 12% more accurate and 22% more sensitive than in-use ICU scores like Acute Physiology and Chronic Health Evaluation (APACHE) and Sequential Organ Failure Assessment (SOFA) scores. The predictive capacity of DySurv is consistent and the survival estimates remain disentangled across different datasets.

Discussion: Our interdisciplinary framework successfully incorporates deep learning, survival analysis, and intensive care to create a novel method for time-to-event prediction from longitudinal health records. We test our method on several held-out test sets from a variety of healthcare datasets and compare it to existing in-use clinical risk scoring benchmarks.

Conclusion: While our method leverages non-parametric extensions to deep learning-guided estimations of the survival distribution, further deep learning paradigms could be explored.

Objectives: Report the development of the patient-centered myAURA application and suite of methods designed to aid epilepsy patients, caregivers, and clinicians in making decisions about self-management and care.

Materials and methods: myAURA rests on an unprecedented collection of epilepsy-relevant heterogeneous data resources, such as biomedical databases, social media, and electronic health records (EHRs). We use a patient-centered biomedical dictionary to link the collected data in a multilayer knowledge graph (KG) computed with a generalizable, open-source methodology.

Results: Our approach is based on a novel network sparsification method that uses the metric backbone of weighted graphs to discover important edges for inference, recommendation, and visualization. We demonstrate by studying drug-drug interaction from EHRs, extracting epilepsy-focused digital cohorts from social media, and generating a multilayer KG visualization. We also present our patient-centered design and pilot-testing of myAURA, including its user interface.

Discussion: The ability to search and explore myAURA's heterogeneous data sources in a single, sparsified, multilayer KG is highly useful for a range of epilepsy studies and stakeholder support.

Conclusion: Our stakeholder-driven, scalable approach to integrating traditional and nontraditional data sources enables both clinical discovery and data-powered patient self-management in epilepsy and can be generalized to other chronic conditions.

Objectives: The objective of this study is to provide an overview of the current landscape of individualized treatment effects (ITE) estimation, specifically focusing on methodologies proposed for time-series electronic health records (EHRs). We aim to identify gaps in the literature, discuss challenges, and propose future research directions to advance the field of personalized medicine.

Materials and methods: We conducted a comprehensive literature review to identify and analyze relevant works on ITE estimation for time-series data. The review focused on theoretical assumptions, types of treatment settings, and computational frameworks employed in the existing literature.

Results: The literature reveals a growing body of work on ITE estimation for tabular data, while methodologies specific to time-series EHRs are limited. We summarize and discuss the latest advancements, including the types of models proposed, the theoretical foundations, and the computational approaches used.

Discussion: The limitations and challenges of current ITE estimation methods for time-series data are discussed, including the lack of standardized evaluation metrics and the need for more diverse and representative datasets. We also highlight considerations and potential biases that may arise in personalized treatment effect estimation.

Conclusion: This work provides a comprehensive overview of ITE estimation for time-series EHR data, offering insights into the current state of the field and identifying future research directions. By addressing the limitations and challenges, we hope to encourage further exploration and innovation in this exciting and under-studied area of personalized medicine.

Objective: To develop an electronic medical record (EMR) data processing tool that confers clinical context to machine learning (ML) algorithms for error handling, bias mitigation, and interpretability.

Materials and methods: We present Trust-MAPS, an algorithm that translates clinical domain knowledge into high-dimensional, mixed-integer programming models that capture physiological and biological constraints on clinical measurements. EMR data are projected onto this constrained space, effectively bringing outliers to fall within a physiologically feasible range. We then compute the distance of each data point from the constrained space modeling healthy physiology to quantify deviation from the norm. These distances, termed "trust-scores," are integrated into the feature space for downstream ML applications. We demonstrate the utility of Trust-MAPS by training a binary classifier for early sepsis prediction on data from the 2019 PhysioNet Computing in Cardiology Challenge, using the XGBoost algorithm and applying SMOTE for overcoming class-imbalance.

Results: The Trust-MAPS framework shows desirable behavior in handling potential errors and boosting predictive performance. We achieve an area under the receiver operating characteristic curve of 0.91 (95% CI, 0.89-0.92) for predicting sepsis 6 hours before onset-a marked 15% improvement over a baseline model trained without Trust-MAPS.

Discussions: Downstream classification performance improves after Trust-MAPS preprocessing, highlighting the bias reducing capabilities of the error-handling projections. Trust-scores emerge as clinically meaningful features that not only boost predictive performance for clinical decision support tasks but also lend interpretability to ML models.

Conclusion: This work is the first to translate clinical domain knowledge into mathematical constraints, model cross-vital dependencies, and identify aberrations in high-dimensional medical data. Our method allows for error handling in EMR and confers interpretability and superior predictive power to models trained for clinical decision support.

Objective: The integration of predictive models into live clinical care requires scientific testing before implementation to ensure patient safety. We built and technically implemented a model that predicts which patients require an electrocardiogram (ECG) to screen for heart attacks within 10 minutes of their arrival to the Emergency Department. We developed a structured framework for the in vitro to in vivo translation of the model through implementation as clinical decision support (CDS).

Materials and methods: The CDS ran as a silent pilot for 2 months. We conducted (1) a Technical Component Analysis to ensure each part of the CDS coding functioned as planned, and (2) a Technical Fidelity Analysis to ensure agreement between the CDS's in vivo and the model's in vitro screening decisions.

Results: The Technical Component Analysis indicated several small coding errors in CDS components that were addressed. During this period, the CDS processed 18 335 patient encounters. CDS fidelity to the model reflected raw agreement of 95.5% (CI, 95.2%-95.9%) and kappa of 87.6% (CI, 86.7%-88.6%). Additional coding errors were identified and were corrected.

Discussion: Our structured framework for the in vitro to in vivo translation of our predictive model uncovered ways to improve performance in vivo and the validity of risk assessment decisions. Testing predictive models on live care data and accompanying analyses is necessary to safely implement a predictive model for clinical use.

Conclusion: We developed a method for the translation of our model from in vitro to in vivo that can be utilized with other applications of predictive modeling in healthcare.

Objective: This study aims to enhance the diagnostic process for rare diseases using case-based reasoning (CBR). CBR compares new cases with historical data, utilizing both structured and unstructured clinical data.

Materials and methods: The study uses a dataset of 4295 patient cases from the University Hospital Frankfurt. Data were standardized using the OMOP Common Data Model. Three methods-TF, TF-IDF, and TF-IDF with semantic vector embeddings-were employed to represent patient records. Similarity search effectiveness was evaluated using cross-validation to assess diagnostic precision. High-weighted concepts were rated by medical experts for relevance. Additionally, the impact of different levels of ICD-10 code granularity on prediction outcomes was analyzed.

Results: The TF-IDF method showed a high degree of precision, with an average positive predictive value of 91% in the 10 most similar cases. The differences between the methods were not statistically significant. The expert evaluation rated the medical relevance of high-weighted concepts as moderate. The granularity of ICD-10 coding significantly influences the precision of predictions, with more granular codes showing decreased precision.

Discussion: The methods effectively handle data from multiple medical specialties, suggesting broad applicability. The use of broader ICD-10 codes with high precision in prediction could improve initial diagnostic guidance. The use of Explainable AI could enhance diagnostic transparency, leading to better patient outcomes. Limitations include standardization issues and the need for more comprehensive lab value integration.

Conclusion: While CBR shows promise for rare disease diagnostics, its utility depends on the specific needs of the decision support system and its intended clinical application.

Objectives: Electronic Health Records (EHRs) sampled from different populations can introduce unwanted biases, limit individual-level data sharing, and make the data and fitted model hardly transferable across different population groups. In this context, our main goal is to design an effective method to transfer knowledge between population groups, with computable guarantees for suitability, and that can be applied to quantify treatment disparities.

Materials and methods: For a model trained in an embedded feature space of one subgroup, our proposed framework, Optimal Transport-based Transfer Learning for EHRs (OTTEHR), combines feature embedding of the data and unbalanced optimal transport (OT) for domain adaptation to another population group. To test our method, we processed and divided the MIMIC-III and MIMIC-IV databases into multiple population groups using ICD codes and multiple labels.

Results: We derive a theoretical bound for the generalization error of our method, and interpret it in terms of the Wasserstein distance, unbalancedness between the source and target domains, and labeling divergence, which can be used as a guide for assessing the suitability of binary classification and regression tasks. In general, our method achieves better accuracy and computational efficiency compared with standard and machine learning transfer learning methods on various tasks. Upon testing our method for populations with different insurance plans, we detect various levels of disparities in hospital duration stay between groups.

Discussion and conclusion: By leveraging tools from OT theory, our proposed framework allows to compare statistical models on EHR data between different population groups. As a potential application for clinical decision making, we quantify treatment disparities between different population groups. Future directions include applying OTTEHR to broader regression and classification tasks and extending the method to semi-supervised learning.