Analyzing the health status of patients based on Electronic Health Records (EHR) is a fundamental research problem in medical informatics. The presence of extensive missing values in EHR makes it challenging for deep neural networks (DNNs) to directly model the patient's health status. Existing DNNs training protocols, including Impute-then-Regress Procedure and Jointly Optimizing of Impute-n-Regress Procedure, require the additional imputation models to reconstruction missing values. However, Impute-then-Regress Procedure introduces the risk of injecting imputed, non-real data into downstream clinical prediction tasks, resulting in power loss, biased estimation, and poorly performing models, while Jointly Optimizing of Impute-n-Regress Procedure is also difficult to generalize due to the complex optimization space and demanding data requirements. Inspired by the recent advanced literature of learnable prompt in the fields of NLP and CV, in this work, we rethought the necessity of the imputation model in downstream clinical tasks, and proposed Learnable Prompt as Pseudo-Imputation (PAI) as a new training protocol to assist EHR analysis. PAI no longer introduces any imputed data but constructs a learnable prompt to model the implicit preferences of the downstream model for missing values, resulting in a significant performance improvement for all state-of-the-arts EHR analysis models on four real-world datasets across two clinical prediction tasks. Further experimental analysis indicates that PAI exhibits higher robustness in situations of data insufficiency and high missing rates. More importantly, as a plug-and-play protocol, PAI can be easily integrated into any existing or even imperceptible future EHR analysis models. The code of this work is deployed publicly available at https://github.com/MrBlankness/PAI to help the research community reproduce the results and assist the EHR analysis tasks.

Recent studies have indicated that Large Language Models (LLMs) harbor an inherent understanding of truthfulness, yet often fail to consistently express it and generate false statements. This gap between ''knowing'' and ''telling'' poses a challenge for ensuring the truthfulness of generated content. Inspired by recent work on the practice of encoding human-interpretable concepts linearly within large language models, we treat truthfulness as a specially linearly encoded concept within LLMs, and introduce Adaptive Activation Steering (ACT), a tuning-free method that adaptively shifts LLM's activations in the ''truthful'' direction during inference. ACT addresses diverse categories of hallucinations by utilizing diverse truthfulness-related steering vectors and adjusting the steering intensity adaptively. Applied as an add-on across various models, ACT significantly improves truthfulness in LLaMA (↑142%), LLaMA2 (↑24%), Alpaca (↑36%), Vicuna (↑28%), LLaMA2-Chat (↑19%), and LLaMA3(↑34%). Furthermore, we verify ACT's scalability across larger models (13B, 33B, 65B), underscoring the adaptability of ACT to large-scale language models. Our code is available at https://github.com/tianlwang/ACT.

We introduce ColaCare, a framework that enhances Electronic Health Record (EHR) modeling through multi-agent collaboration driven by Large Language Models (LLMs). Our approach seamlessly integrates domain-specific expert models with LLMs to bridge the gap between structured EHR data and text-based reasoning. Inspired by the Multidisciplinary Team (MDT) approach used in clinical settings, ColaCare employs two types of agents: DoctorAgents and a MetaAgent, which collaboratively analyze patient data. Expert models process and generate predictions from numerical EHR data, while LLM agents produce reasoning references and decision-making reports within the MDT-driven collaborative consultation framework. The MetaAgent orchestrates the discussion, facilitating consultations and evidence-based debates among DoctorAgents, simulating diverse expertise in clinical decision-making. We additionally incorporate the Merck Manual of Diagnosis and Therapy (MSD) medical guideline within a retrieval-augmented generation (RAG) module for medical evidence support, addressing the challenge of knowledge currency. Extensive experiments conducted on three EHR datasets demonstrate ColaCare's superior performance in clinical mortality outcome and readmission prediction tasks, underscoring its potential to revolutionize clinical decision support systems and advance personalized precision medicine. All code, case studies and a questionnaire are available at the project website: https://colacare.netlify.app.

Data mining and knowledge discovery are essential aspects of extracting valuable insights from vast datasets. Neural topic models (NTMs) have emerged as a valuable unsupervised tool in this field. However, the predominant objective in NTMs, which aims to discover topics maximizing data likelihood, often lacks alignment with the central goals of data mining and knowledge discovery which is to reveal interpretable insights from large data repositories. Overemphasizing likelihood maximization without incorporating topic regularization can lead to an overly expansive latent space for topic modeling. In this paper, we present an innovative approach to NTMs that addresses this misalignment by introducing contrastive learning measures to assess topic interpretability. We propose a novel NTM framework, named ContraTopic, that integrates a differentiable regularizer capable of evaluating multiple facets of topic interpretability throughout the training process. Our regularizer adopts a unique topic-wise contrastive methodology, fostering both internal coherence within topics and clear external distinctions among them. Comprehensive experiments conducted on three diverse datasets demonstrate that our approach consistently produces topics with superior interpretability compared to state-of-the-art NTMs.

With the widespread adoption of electronic health records (EHR) data, deep learning techniques have been broadly utilized for various health prediction tasks. Nevertheless, the labeled data scarcity issue restricts the prediction power of these deep models. To enhance the generalization capability of deep learning models when faced with such situations, a common trend is to train generative adversarial networks (GANs) or diffusion models for data augmentation. However, due to limitations in sample size and potential label imbalance issues, these methods are prone to mode collapse problems. This results in the generation of new samples that fail to preserve the subtype structure within EHR data, thereby limiting their practicality in health prediction tasks that generally require detailed patient phenotyping. Aiming at the above problems, we propose a Prototype-based Mixup method, dubbed ProtoMix, which combines prior knowledge of intrinsic data features from subtype centroids (i.e., prototypes) to guide the synthesis of new samples. Specifically, ProtoMix employs a prototype-guided mixup training task to shift the decision boundary away from the subtypes. Then, ProtoMix optimizes the sampling weights in different areas of the data manifold via a prototype-guided mixup sampling strategy. Throughout the training process, ProtoMix dynamically expands the training distribution using an adaptive mixing coefficient computation method. Experimental evaluations on three real-world datasets demonstrate the efficacy of ProtoMix.

Deep learning techniques are capable of capturing complex input-output relationships, and have been widely applied to the diagnosis prediction task based on web-based patient electronic health records (EHR) data. To improve the prediction and interpretability of pure data-driven deep learning with only a limited amount of labeled data, a pervasive trend is to assist the model training with knowledge priors from online medical knowledge graphs. However, they marginally investigated the label imbalance and the task-irrelevant noise in the external knowledge graph. The imbalanced label distribution would bias the learning and knowledge extraction towards the majority categories. The task-irrelevant noise introduces extra uncertainty to the model performance. To this end, aiming at by-passing the bias-variance trade-off dilemma, we introduce a new sequential learning framework, dubbed SeqCare, for diagnosis prediction with online medical knowledge graphs. Concretely, in the first step, SeqCare learns a bias-reduced space through a self-supervised graph contrastive learning task. Secondly, SeqCare reduces the learning uncertainty by refining the supervision signal and the graph structure of the knowledge graph simultaneously. Lastly, SeqCare trains the model in the bias-variance reduced space with a self-distillation to further filter out irrelevant information in the data. Experimental evaluations on two real-world datasets show that SeqCare outperforms state-of-the-art approaches. Case studies exemplify the interpretability of SeqCare. Moreover, the medical findings discovered by SeqCare are consistent with experts and medical literature.

Exploiting the correlations between medical features is essential to the success of healthcare data analysis. However, most existing methods are either suffering large estimation variance for data insufficiency or inflexible in terms of demanding task-specific medical knowledge. In this paper, we propose a novel patient health representation learning framework dubbedSAFARI.SAFARIlearns a compact representation by imposing a clinical-fact-inspired task-agnostic correlational sparsity prior to the correlations of medical feature pairs. Specifically, we learn the compact representation by solving the bi-level optimization problem, which involves solving the high-level inter-group correlations and the nested lower-level intra-group correlations. We leverage the Laplacian kernel as a robust metric for feature grouping and graph neural networks for solving the bi-level optimization problem following the optimal value reformulation paradigm. Experiments on five datasets of various inputs and tasks demonstrate the efficacy ofSAFARI. The discovered findings are also consistent with our insights and medical literature, which can provide valuable clinical explanations.

Despite the success of deep neural networks on text classification, their large capacity also leads to capturing task-irrelevant patterns such as label noise. Label noise is usually introduced into the data during label collection and causes nontrivial declines in performance due to the memorization effect. Though effort has been devoted to combating the label noise in other systems such as image classification, high-quality input features are necessary for discovering task-relevant patterns before memorizing the label noise. However, such a high-quality input feature requirement is hard to be satisfied for text classification due to the nature of natural language. To combat the label noise with low-quality input features in the text classification, we propose a novel framework that exploits external category descriptions to construct prototypes that can be used to denoise the input representation and alleviate the over-fitting. However, there still remains a challenge that the external category descriptions from other corpora could be semantically discrepant with the underlying task-specific classes in the training corpus. To align their semantics, we propose two regularizers that penalize sample-wise semantic-based deviations at the local level and class-wise structure-based deviations at the global level, respectively. Our extensive experiments across two open datasets and one real-world case study demonstrate that our method is superior to state-of-the-art baselines under various settings of label noise.

Multimodal electronic health record (EHR) data are widely used in clinical applications. Conventional methods usually assume that each sample (patient) is associated with the unified observed modalities, and all modalities are available for each sample. However, missing modality caused by various clinical and social reasons is a common issue in real-world clinical scenarios. Existing methods mostly rely on solving a generative model that learns a mapping from the latent space to the original input space, which is an unstable ill-posed inverse problem. To relieve the underdetermined system, we propose a model solving a direct problem, dubbed learning with Missing Modalities in Multimodal healthcare data (M3Care). M3Care is an end-to-end model compensating the missing information of the patients with missing modalities to perform clinical analysis. Instead of generating raw missing data, M3Care imputes the task-related information of the missing modalities in the latent space by the auxiliary information from each patient's similar neighbors, measured by a task-guided modality-adaptive similarity metric, and thence conducts the clinical tasks. The task-guided modality-adaptive similarity metric utilizes the uncensored modalities of the patient and the other patients who also have the same uncensored modalities to find similar patients. Experiments on real-world datasets show that M3Care outperforms the state-of-the-art baselines. Moreover, the findings discovered by M3Care are consistent with experts and medical knowledge, demonstrating the capability and the potential of providing useful insights and explanations.

Population health data are becoming more and more publicly available on the Internet than ever before. Such datasets offer a great potential for enabling a better understanding of the health of populations, and inform health professionals and policy makers for better resource planning, disease management and prevention across different regions. However, due to the laborious and high-cost nature of collecting such public health data, it is a common place to find many missing entries on these datasets, which challenges the utility of the data and hinders reliable analysis and understanding. To tackle this problem, this paper proposes a deep-learning-based approach, called Compressive Population Health (CPH), to infer and recover (to complete) the missing prevalence rate entries of multiple chronic diseases. The key insight of CPH relies on the combined exploitation of both intra-disease and inter-disease correlation opportunities. Specifically, we first propose a Convolutional Neural Network (CNN) based approach to extract and model both of these two types of correlations, and then adopt a Generative Adversarial Network (GAN) based prevalence inference model to jointly fuse them to facility the prevalence rates data recovery of missing entries. We extensively evaluate the inference model based on real-world public health datasets publicly available on the Web. Results show that our inference method outperforms other baseline methods in various settings and with a significantly improved accuracy (from 14.8% to 9.1%).

Due to the characteristics of COVID-19, the epidemic develops rapidly and overwhelms health service systems worldwide. Many patients suffer from life-threatening systemic problems and need to be carefully monitored in ICUs. An intelligent prognosis can help physicians take an early intervention, prevent adverse outcomes, and optimize the medical resource allocation, which is urgently needed, especially in this ongoing global pandemic crisis. However, in the early stage of the epidemic outbreak, the data available for analysis is limited due to the lack of effective diagnostic mechanisms, the rarity of the cases, and privacy concerns. In this paper, we propose a distilled transfer learning framework, which leverages the existing publicly available online Electronic Medical Records to enhance the prognosis for inpatients with emerging infectious diseases. It learns to embed the COVID-19-related medical features based on massive existing EMR data. The transferred parameters are further trained to imitate the teacher model’s representation based on distillation, which embeds the health status more comprehensively on the source dataset. We conduct Length-of-Stay prediction experiments for patients in ICUs on real-world COVID-19 datasets. The experiment results indicate that our proposed model consistently outperforms competitive baseline methods. In order to further verify the scalability of o deal with different clinical tasks on different EMR datasets, we conduct an additional mortality prediction experiment on End-Stage Renal Disease datasets. The extensive experiments demonstrate that an benefit the prognosis for emerging pandemics and other diseases with limited EMR.

Existing review-based recommendation models mainly learn long- term user and item representations from a set of reviews. Due to the ignorance of rich side information of reviews, these models suffer from two drawbacks: 1) they fail to capture short-term changes of user preferences and item features reflected in reviews and 2) they cannot accurately model high-order user-item collaborative signals from reviews. To overcome these limitations, we propose a multi-view approach named Set-Sequence-Graph (SSG), to augment existing single-view (i.e., view of set) methods by introducing two additional views of exploiting reviews: sequence and graph. In particular, with reviews organized in forms of set, sequence, and graph respectively, we design a three-way encoder architecture that jointly captures long-term (set), short-term (sequence), and collaborative (graph) features of users and items for recommendation. For the sequence encoder, we propose a short-term priority attention network that explicitly takes the order and personalized time intervals of reviews into consideration. For the graph encoder, we design a novel review-aware graph attention network to model high-order multi-aspect relations in the user-item graph. To combat the potential redundancy in captured features, our fusion module employs a cross-view decorrelation mechanism to encourage diverse representations from multiple views for integration. Experiments on public datasets demonstrate that SSG significantly outperforms state-of-the-art methods.

Unsupervised time series mining has been attracting great interest from both academic and industrial communities. As the two most basic data mining tasks, the discoveries of frequent/rare subsequences have been extensively studied in the literature. Specifically, frequent/rare subsequences are defined as the ones with the smallest/largest 1-nearest neighbor distance, which are also known as motif/discord. However, discord fails to identify rare subsequences when it occurs more than once in the time series, which is widely known as the twin freak problem. This problem is just the "tip of the iceberg" due to the 1-nearest neighbor distance based definitions. In this work, we for the first time provide a clear theoretical analysis of motif/discord as the 1-nearest neighbor based nonparametric density estimation of subsequence. Particularly, we focus on matrix profile, a recently proposed mining framework, which unifies the discovery of motif and discord under the same computing model. Thereafter, we point out the inherent three issues: low-quality density estimation, gravity defiant behavior, and lack of reusable model, which deteriorate the performance of matrix profile in both efficiency and subsequence quality.To overcome these issues, we propose Neighbor Profile to robustly model the subsequence density by bagging nearest neighbors for the discovery of frequent/rare subsequences. Specifically, we leverage multiple subsamples and average the density estimations from subsamples using adjusted nearest neighbor distances, which not only enhances the estimation robustness but also realizes a reusable model for efficient learning. We check the sanity of neighbor profile on synthetic data and further evaluate it on real-world datasets. The experimental results demonstrate that neighbor profile can correctly model the subsequences of different densities and shows superior performance significantly over matrix profile on the real-world arrhythmia dataset. Also, it is shown that neighbor profile is efficient for massive datasets.

Deep learning has demonstrated success in health risk prediction especially for patients with chronic and progressing conditions. Most existing works focus on learning disease patterns from longitudinal patient data, but pay little attention to the disease progression stage itself. To fill the gap, we propose a Stage-aware neural Network (StageNet) model to extract disease stage information from patient data and integrate it into risk prediction. StageNet is enabled by (1) a stage-aware long short-term memory (LSTM) module that extracts health stage variations unsupervisedly; (2) a stage-adaptive convolutional module that incorporates stage-related progression patterns into risk prediction. We evaluate StageNet on two real-world datasets and show that StageNet outperforms state-of-the-art models in risk prediction task and patient subtyping task. Compared to the best baseline model, StageNet achieves up to 12% higher AUPRC for risk prediction task on two real-world patient datasets. StageNet also achieves over 58% higher Calinski-Harabasz score (a cluster quality metric) for a patient subtyping task.

In modern cities, complaining has become an important way for citizens to report emerging urban issues to governments for quick response. For ease of retrieval and handling, government officials usually organize citizen complaints by manually assigning tags to them, which is inefficient and cannot always guarantee the quality of assigned tags. This work attempts to solve this problem by recommending tags for citizen complaints. Although there exist many studies on tag recommendation for textual content, few of them consider two characteristics of citizen complaints, i.e., the spatio-temporal correlations and the taxonomy of candidate tags. In this paper, we propose a novel Spatio-Temporal Taxonomy-Aware Recommendation model (STAR), to recommend tags for citizen complaints by jointly incorporating spatio-temporal information of complaints and the taxonomy of candidate tags. Specifically, STAR first exploits two parallel channels to learn representations for textual and spatio-temporal information. To effectively leverage the taxonomy of tags, we design chained neural networks that gradually refine the representations and perform hierarchical recommendation under a novel taxonomy constraint. A fusion module is further proposed to adaptively integrate contributions of textual and spatio-temporal information in a tag-specific manner. We conduct extensive experiments on a real-world dataset and demonstrate that STAR significantly performs better than state-of-the-art methods. The effectiveness of key components in our model is also verified through ablation studies.

Diagnosis prediction, which aims to predict future health information of patients from historical electronic health records (EHRs), is a core research task in personalized healthcare. Although some RNN-based methods have been proposed to model sequential EHR data, these methods have two major issues. First, they cannot capture fine-grained progression patterns of patient health conditions. Second, they do not consider the mutual effect between important context (e.g., patient demographics) and historical diagnosis. To tackle these challenges, we propose a model called Co-Attention Memory networks for diagnosis Prediction (CAMP), which tightly integrates historical records, fine-grained patient conditions, and demographics with a three-way interaction architecture built on co-attention. Our model augments RNNs with a memory network to enrich the representation capacity. The memory network enables analysis of fine-grained patient conditions by explicitly incorporating a taxonomy of diseases into an array of memory slots. We instantiate the READ/WRITE operations of the memory network so that the memory cooperates effectively with the patient demographics through co-attention mechanism. Experiments on real-world datasets demonstrate that CAMP consistently performs better than state-of-the-art methods.

This paper presents a novel time series classification algorithm. It exploits time-delay embedding to transform time series into a set of points as a distribution, and attempt to classify time series by classifying corresponding distributions. It proposes a novel geometrical feature, i.e. characteristic subspace, from embedding points for classification, and leverages class-weighted support vector machine (SVM) to learn for it. An efficient boosting strategy is also developed to enable a linear time training. The experiments show great potentials of this novel algorithm on accuracy, efficiency and interpretability.

Data quality and budget are two primary concerns in urban-scale mobile crowdsensing. Traditional research on mobile crowdsensing mainly takes sensing coverage ratio as the data quality metric rather than the overall sensed data error in the target-sensing area. In this article, we propose to leverage spatiotemporal correlations among the sensed data in the target-sensing area to significantly reduce the number of sensing task assignments. In particular, we exploit both intradata correlations within the same type of sensed data and interdata correlations among different types of sensed data in the sensing task. We propose a novel crowdsensing task allocation framework called SPACE-TA (SPArse Cost-Effective Task Allocation) , combining compressive sensing, statistical analysis, active learning, and transfer learning, to dynamically select a small set of subareas for sensing in each timeslot (cycle), while inferring the data of unsensed subareas under a probabilistic data quality guarantee. Evaluations on real-life temperature, humidity, air quality, and traffic monitoring datasets verify the effectiveness of SPACE-TA. In the temperature-monitoring task leveraging intradata correlations, SPACE-TA requires data from only 15.5% of the subareas while keeping the inference error below 0.25°C in 95% of the cycles, reducing the number of sensed subareas by 18.0% to 26.5% compared to baselines. When multiple tasks run simultaneously, for example, for temperature and humidity monitoring, SPACE-TA can further reduce ∼10% of the sensed subareas by exploiting interdata correlations.