Electronic health records (EHRs) are the de facto standard for analyzing comprehensive patient conditions. Existing methods mainly employ specialized neural networks to extract modality-specific information, followed by modality correlation modeling to support clinical decision-making. However, these methods generally overlook the issue of ''contaminated'' representations inherent in routine EHR data, which can undermine the model's discriminative ability, as less relevant representations associated with false positive correlations may impede the recognition of truly effective representations. To address the issue of representation contamination, we propose CLEAR, a counterfactual disparity learning model for explicit multimodal EHR analytics. The core idea is to first model the contamination in representations, and subsequently perform calibration and enhancement to construct highly discriminative representations. Specifically, CLEAR first proposes the Counterfactual Prompt Learning Module to capture the representation discrepancy to model representation contamination. Subsequently, an Adaptive Dynamic Imputation Module is devised to decouple the elementwise representations for representation calibration, while a gating mechanism is further proposed to incorporate discriminative discrepancy information for representation enhancement. Finally, the Multimodal Representation Fusion Module establishes intra- and inter-modality correlations, thereby creating a seamless integration towards downstream analytic tasks. To our knowledge, CLEAR is the first to model and resolve representation contamination in multimodal EHR analytics. Experimental results on two real-world datasets demonstrate that CLEAR consistently outperforms state-of-the-art baselines in facilitating multimodal healthcare analytics.

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.

The demand for satisfactory dental care management has attracted a great deal of attention from both dentists and patients. Reviews of existing systems and approaches reveal that they either fail to take into account patients' daily diets that are a significant risk factor for dental decay, or are too complicated for patients. To facilitate patients' tracking and management of their dietary risk factors for dental decay, and improve dentists' identification of decay-related dietary patterns, we develop a system called eDental, in collaboration with dentists and oral surgeons, as a mechanism to record users' detailed daily diet diaries by snapping food photos. The system identifies the food using a state-of-the-art deep learning model and analyzes patients' dental care conditions and potential dental risks. eDental is a full-fledged oral care system with easy-to-use user interfaces. In this demonstration, we showcase eDental's key functionalities for managing patients' dental care via diet diaries.

Interaction learning plays an essential role in learning patients' comprehensive representations that contribute to improved performance in many analytical tasks. In healthcare, interactions among medical features (i.e., feature-level interactions) can exhibit different abnormal patterns in detail, while interactions among time steps (i.e., time-level interactions) can indicate the dynamic changes in patients' health conditions. Therefore, it is necessary to capture and analyze both types of interactions when conducting healthcare analytics, In this paper, we propose a general framework ELDA that is supported by the novel model ELDA-Net to learn dual-interactions for healthcare analytics in an explicit manner. Specifically, we devise a Feature-level Interaction Learning Module that can enrich a separately processed medical feature by learned interactions among medical features, and a Time-level Interaction Learning Module that can enhance the representations of the patients' health conditions by learned interactions among time steps. In both levels, ELDA can provide explicit and intuitive interpretations via explaining through the designed attention mechanism. Further, to facilitate the feature-level interaction learning, we propose a novel Bi-directional Embedding Module in ELDA-Net which can efficiently embed the medical features recorded in numerical values. We evaluate the effectiveness and interpretability of ELDA over two public real-world clinical datasets. The experimental results confirm that ELDA consistently outperforms existing state-of-the-art methods with a significant margin, and supports fine-grained interpretability in both the feature level and the time level with medical insights.

In National University Hospital (NUH) in Singapore, we conduct healthcare analytics that analyzes heterogeneous electronic medical records (EMR) to support effective clinical decision-making on a daily basis. Existing work mainly focuses on multimodality for extracting complementary information from different modalities, and/or interpretability for providing interpretable prediction results. However, real-world healthcare analytics has presented another major challenge, i.e., the available modalities evolve or change intermittently. Addressing this challenge requires deployed models to be adaptive to such dynamic modality changes. To meet the aforementioned requirement, we develop a modular, multimodal and interpretable framework DyHealth to enable dynamic healthcare analytics in clinical practice. Specifically, different modalities are processed within their respective data modules that adhere to the interface defined by DyHealth. The extracted information from different modalities is integrated subsequently in our proposed Multimodal Fusion Module in DyHealth. In order to better handle modality changes at runtime, we further propose exponential increasing/decreasing mechanisms to support modality "hot-plug". We also devise a novel modality-based attention mechanism for providing fine-grained interpretation results on a per-input basis. We conduct a pilot evaluation of DyHealth on the patients' EMR data from NUH, in which DyHealth achieves superior performance and therefore, is promising to roll out for hospital-wide deployment. We also validate DyHealth in two public EMR datasets. Experimental results confirm the effectiveness, flexibility, and extensibility of DyHealth in supporting multimodal and interpretable healthcare analytics.

With the ever-increasing adoption of machine learning for data analytics, maintaining a machine learning pipeline is becoming more complex as both the datasets and trained models evolve with time. In a collaborative environment, the changes and updates due to pipeline evolution often cause cumbersome coordination and maintenance work, raising the costs and making it hard to use. Existing solutions, unfortunately, do not address the version evolution problem, especially in a collaborative environment where non-linear version control semantics are necessary to isolate operations made by different user roles. The lack of version control semantics also incurs unnecessary storage consumption and lowers efficiency due to data duplication and repeated data pre-processing, which are avoidable.In this paper, we identify two main challenges that arise during the deployment of machine learning pipelines, and address them with the design of versioning for an end-to-end analytics system MLCask. The system supports multiple user roles with the ability to perform Git-like branching and merging operations in the context of the machine learning pipelines. We define and accelerate the metric-driven merge operation by pruning the pipeline search tree using reusable history records and pipeline compatibility information. Further, we design and implement the prioritized pipeline search, which gives preference to the pipelines that probably yield better performance. The effectiveness of MLCask is evaluated through an extensive study over several real-world deployment cases. The performance evaluation shows that the proposed merge operation is up to 7.8x faster and saves up to 11.9x storage space than the baseline method that does not utilize history records.

Relational databases are the de facto standard for storing and querying structured data, and extracting insights from structured data requires advanced analytics. Deep neural networks (DNNs) have achieved super-human prediction performance in particular data types, e.g., images. However, existing DNNs may not produce meaningful results when applied to structured data. The reason is that there are correlations and dependencies across combinations of attribute values in a table, and these do not follow simple additive patterns that can be easily mimicked by a DNN. The number of possible such cross features is combinatorial, making them computationally prohibitive to model. Furthermore, the deployment of learning models in real-world applications has also highlighted the need for interpretability, especially for high-stakes applications, which remains another issue of concern to DNNs. In this paper, we present ARM-Net, an adaptive relation modeling network tailored for structured data, and a lightweight framework ARMOR based on ARM-Net for relational data analytics. The key idea is to model feature interactions with cross features selectively and dynamically, by first transforming the input features into exponential space, and then determining the interaction order and interaction weights adaptively for each cross feature. We propose a novel sparse attention mechanism to dynamically generate the interaction weights given the input tuple, so that we can explicitly model cross features of arbitrary orders with noisy features filtered selectively. Then during model inference, ARM-Net can specify the cross features being used for each prediction for higher accuracy and better interpretability. Our extensive experiments on real-world datasets demonstrate that ARM-Net consistently outperforms existing models and provides more interpretable predictions for data-driven decision making.

Human-in-the-loop data analysis involves both machine learning models and humans in analytic tasks. In healthcare applications, human-in-the-loop data analysis is crucial in that the model can handle "easy" tasks and hand over "hard" ones to medical experts for assistance and medical judgment, where easy tasks are the ones for which the model can provide high accuracy and hard tasks vice versa. In this process, how to decompose tasks in an effective manner is an important stage. To achieve task decomposition, classification with a reject option is a solution. However, existing studies either directly implement a reject option or dive into the theoretical details of the rejection mechanism. Different from such studies, we aim to optimize general classifiers with a reject option and hence, optimize task decomposition for healthcare applications.

In high stakes applications such as healthcare and finance analytics, the interpretability of predictive models is required and necessary for domain practitioners to trust the predictions. Traditional machine learning models, e.g., logistic regression (LR), are easy to interpret in nature. However, many of these models aggregate time-series data without considering the temporal correlations and variations. Therefore, their performance cannot match up to recurrent neural network (RNN) based models, which are nonetheless difficult to interpret. In this paper, we propose a general framework TRACER to facilitate accurate and interpretable predictions, with a novel model TITV devised for healthcare analytics and other high stakes applications such as financial investment and risk management. Different from LR and other existing RNN-based models, TITV is designed to capture both the time-invariant and the time-variant feature importance using a feature-wise transformation subnetwork and a self-attention subnetwork, for the feature influence shared over the entire time series and the time-related importance respectively. Healthcare analytics is adopted as a driving use case, and we note that the proposed TRACER is also applicable to other domains, e.g., fintech. We evaluate the accuracy of TRACER extensively in two real-world hospital datasets, and our doctors/clinicians further validate the interpretability of TRACER in both the patient level and the feature level. Besides, TRACER is also validated in a critical financial application. The experimental results confirm that TRACER facilitates both accurate and interpretable analytics for high stakes applications.

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.