Electronic health records (EHR) aggregate extensive data critical for advancing patient care and refining intervention strategies. EHR data is essential for epidemiological study, more commonly referred to as cohort study, where patients with shared characteristics or similar diseases are analyzed over time. Unfortunately, existing studies on cohort modeling are limited, struggling to derive fine-grained cohorts or effectively utilize cohort information, which hinders their ability to uncover intrinsic relationships between cohorts. To this end, we propose NeuralCohort, a cohort-aware neural representation learning method that precisely segments patients into finer-grained cohorts via an innovative cohort contextualization mechanism and captures both intra- and inter-cohort information using a Biscale Cohort Learning Module. Designed as a plug-in, NeuralCohort integrates seamlessly with existing backbone models, enhancing their cohort analysis capabilities by infusing deep cohort insights into the representation learning processes. The effectiveness and generalizability of NeuralCohort are validated across extensive real-world EHR datasets. Experimental results demonstrate that NeuralCohort consistently improves the performance of various backbone models, achieving up to an 8.1% increase in AUROC.

In healthcare analytics, addressing binary diagnosis or prognosis tasks presents unique challenges due to the inherent asymmetry between positive and negative samples. While positive samples, indicating patients with a disease, are defined based on stringent medical criteria, negative samples are defined in an open-ended manner and remain underexplored in prior research. To bridge this gap, we propose an innovative approach to facilitate cohort discovery within negative samples, leveraging a Shapley-based exploration of interrelationships between these samples, which holds promise for uncovering valuable insights concerning the studied disease, and related comorbidity and complications. We quantify each sample’s contribution using data Shapley values, subsequently constructing the Negative Sample Shapley Field to model the distribution of all negative samples. Next, we transform this field through manifold learning, preserving the essential data structure information while imposing an isotropy constraint in data Shapley values. Within this transformed space, we pinpoint cohorts of medical interest via density-based clustering. We empirically evaluate the effectiveness of our approach on the real-world electronic medical records from National University Hospital in Singapore, yielding clinically valuable insights aligned with existing knowledge, and benefiting medical research and clinical decision-making.

Cohort studies are of significant importance in the field of healthcare analytics. However, existing methods typically involve manual, labor-intensive, and expert-driven pattern definitions or rely on simplistic clustering techniques that lack medical relevance. Automating cohort studies with interpretable patterns has great potential to facilitate healthcare analytics and data management but remains an unmet need in prior research efforts. In this paper, we present a cohort auto-discovery framework for interpretable healthcare analytics. It focuses on the effective identification, representation, and exploitation of cohorts characterized by medically meaningful patterns. In the framework, we propose CohortNet, a core model that can learn fine-grained patient representations by separately processing each feature, considering both individual feature trends and feature interactions at each time step. Subsequently, it employs K-Means in an adaptive manner to classify each feature into distinct states and a heuristic cohort exploration strategy to effectively discover substantial cohorts with concrete patterns. For each identified cohort, it learns comprehensive cohort representations with credible evidence through associated patient retrieval. Ultimately, given a new patient, CohortNet can leverage relevant cohorts with distinguished importance which can provide a more holistic understanding of the patient's conditions. Extensive experiments on three real-world datasets demonstrate that it consistently outperforms state-of-the-art approaches, resulting in improvements in AUC-PR scores ranging from 2.8% to 4.1%, and offers interpretable insights from diverse perspectives in a top-down fashion.

In the metaverse the physical space and the virtual space co-exist, and interact simultaneously. While the physical space is virtually enhanced with information, the virtual space is continuously refreshed with real-time, real-world information. To allow users to process and manipulate information seamlessly between the real and digital spaces, novel technologies must be developed. These include smart interfaces, new augmented realities, and efficient data storage, management, and dissemination techniques. In this paper, we first discuss some promising co-space applications. These applications offer opportunities that neither of the spaces can realize on its own. Then, we further discuss several emerging technologies that empower the construction of metaverse. After that, we discuss comprehensively the data centric challenges. Finally, we discuss and envision what are likely to be required from the database and system perspectives.

In the metaverse the physical space and the virtual space co-exist, and interact simultaneously. While the physical space is virtually enhanced with information, the virtual space is continuously refreshed with real-time, real-world information. To allow users to process and manipulate information seamlessly between the real and digital spaces, novel technologies must be developed. These include smart interfaces, new augmented realities, and efficient data storage, management, and dissemination techniques. In this paper, we first discuss some promising co-space applications. These applications offer opportunities that neither of the spaces can realize on its own. We then discuss challenges. Finally, we discuss and envision what are likely to be required from the database and system perspectives.

Heart is the most important organ of the human body, and Electrocardiogram (ECG) is an essential tool for clinical monitoring of heart health and detecting cardiovascular diseases. Automatic detection of ECG anomalies is of great significance and clinical value in healthcare. However, performing automatic anomaly detection for the ECG data is challenging because we not only need to accurately detect the anomalies but also need to provide clinically meaningful interpretation of the results. Existing works on automatic ECG anomaly detection either rely on hand-crafted designs of feature extraction algorithms which are typically too simple to deliver good performance, or deep learning for automatically extracting features, which is not interpretable.

Disease progression modeling (DPM) analyzes patients' electronic medical records (EMR) to predict the health state of patients, which facilitates accurate prognosis, early detection and treatment of chronic diseases. However, EMR are irregular because patients visit hospital irregularly based on the need of treatment. For each visit, they are typically given different diagnoses, prescribed various medications and lab tests. Consequently, EMR exhibit irregularity at the feature level. To handle this issue, we propose a model based on the Gated Recurrent Unit by decaying the effect of previous records using fine-grained feature-level time span information, and learn the decaying parameters for different features to take into account their different behaviours like decaying speeds under irregularity. Extensive experimental results in both an Alzheimer's disease dataset and a chronic kidney disease dataset demonstrate that our proposed model of capturing feature-level irregularity can effectively improve the accuracy of DPM.

Electronic Medical Records (EMR) are the most fundamental resources used in healthcare data analytics. Since people visit hospital more frequently when they feel sick and doctors prescribe lab examinations when they feel necessary, we argue that there could be a strong bias in EMR observations compared with the hidden conditions of patients. Directly using such EMR for analytical tasks without considering the bias may lead to misinterpretation. To this end, we propose a general method to resolve the bias by transforming EMR to regular patient hidden condition series using a Hidden Markov Model (HMM) variant. Compared with the biased EMR series with irregular time stamps, the unbiased regular time series is much easier to be processed by most analytical models and yields better results. Extensive experimental results demonstrate that our bias resolving method imputes missing data more accurately than baselines and improves the performance of the state-of-the-art methods on typical medical data analytics.

Healthcare systems around the world are facing the challenge of information overload in caring for patients in an affordable, safe and high-quality manner in a system with limited healthcare resources and increasing costs. To alleviate this problem, we develop an integrative healthcare analytics system called GEMINI which allows point of care analytics for doctors where real-time usable and relevant information of their patients are required through the questions they asked about the patients they are caring for. GEMINI extracts data of each patient from various data sources and stores them as information in a patient profile graph. The data sources are complex and varied consisting of both structured data (such as, patients' demographic data, laboratory results and medications) and unstructured data (such as, doctors' notes). Hence, the patient profile graph provides a holistic and comprehensive information of patients' healthcare profile, from which GEMINI can infer implicit information useful for administrative and clinical purposes, and extract relevant information for performing predictive analytics. At the core, GEMINI keeps interacting with the healthcare professionals as part of a feedback loop to gather, infer, ascertain and enhance the self-learning knowledge base. We present a case study on using GEMINI to predict the risk of unplanned patient readmissions.