Individual Treatment Effect (ITE) estimation is to evaluate the causal effects of treatment strategies on some important outcomes, which is a crucial problem in healthcare. Most existing ITE estimation methods are designed for centralized settings. However, in real-world clinical scenarios, the raw data are usually not shareable among hospitals due to the potential privacy and security risks, which makes the methods not applicable. In this work, we study the ITE estimation task in a federated setting, which allows us to harness the decentralized data from multiple hospitals. Due to the unavoidable confounding bias in the collected data, a model directly learned from it would be inaccurate. One well-known solution is Inverse Probability Treatment Weighting (IPTW), which uses the conditional probability of treatment given the covariates to re-weight each training example. Applying IPTW in a federated setting, however, is non-trivial. We found that even with a well-estimated conditional probability, the local model training step using each hospital's data alone would still suffer from confounding bias. To address this, we propose FED-IPTW, a novel algorithm to extend IPTW into a federated setting that enforces both global (over all the data) and local (within each hospital) decorrelation between covariates and treatments. We validated our approach on the task of comparing the treatment effects of mechanical ventilation on improving survival probability for patients with breadth difficulties in the Intensive Care Unit (ICU). We conducted experiments on both synthetic and real-world eICU datasets, and the results show that FED-IPTW outperforms state-of-the-art methods on all the metrics on factual prediction and ITE estimation tasks, paving the way for personalized treatment strategy design in mechanical ventilation usage.

Living environments play a vital role in the prevalence and progression of diseases, and understanding their impact on patient's health status becomes increasingly crucial for developing AI models. However, due to the lack of long-term and fine-grained spatial and temporal data in public and population health studies, most existing studies fail to incorporate environmental data, limiting the models' performance and real-world application. To address this shortage, we developed SatHealth, a novel dataset combining multimodal spatiotemporal data, including environmental data, satellite images, all-disease prevalences estimated from medical claims, and social determinants of health (SDoH) indicators. We conducted experiments under two use cases with SatHealth: regional public health modeling and personal disease risk prediction. Experimental results show that living environmental information can significantly improve AI models' performance and temporal-spatial generalizability on various tasks. Finally, we deploy a web-based application to provide an exploration tool for SatHealth and one-click access to both our data and regional environmental embedding to facilitate plug-and-play utilization. SatHealth is now published with data in Ohio, and we will keep updating SatHealth to cover the other parts of the US. With the web application and published code pipeline, our work provides valuable angles and resources to include environmental data in healthcare research and establishes a foundational framework for future research in environmental health informatics.

., the six-organ dysfunction assessment of SOFA in Figure 1) play a vital role in sepsis identification within clinicians' workflow, providing evidence-based risk assessments essential for sepsis diagnosis. However, artificial intelligence (AI) sepsis prediction models typically generate a single sepsis risk score without incorporating clinical calculators for assessing organ dysfunctions, making the models less convincing and transparent to clinicians. To bridge the gap, we propose to mimic clinicians' workflow with a novel framework SepsisCalc to integrate clinical calculators into the predictive model, yielding a clinically transparent and precise model for utilization in clinical settings. Practically, clinical calculators usually combine information from multiple component variables in Electronic Health Records (EHR), and might not be applicable when the variables are (partially) missing. We mitigate this issue by representing EHRs as temporal graphs and integrating a learning module to dynamically add the accurately estimated calculator to the graphs. Experimental results on real-world datasets show that the proposed model outperforms state-of-the-art methods on sepsis prediction tasks. Moreover, we developed a system to identify organ dysfunctions and potential sepsis risks, providing a human-AI interaction tool for deployment, which can help clinicians understand the prediction outputs and prepare timely interventions for the corresponding dysfunctions, paving the way for actionable clinical decision-making support for early intervention.

Sepsis is the leading cause of in-hospital mortality in the USA. Early sepsis onset prediction and diagnosis could significantly improve the survival of sepsis patients. Existing predictive models are usually trained on high-quality data with few missing information, while missing values widely exist in real-world clinical scenarios (especially in the first hours of admissions to the hospital), which causes a significant decrease in accuracy and an increase in uncertainty for the predictive models. The common method to handle missing values is imputation, which replaces the unavailable variables with estimates from the observed data. The uncertainty of imputation results can be propagated to the sepsis prediction outputs, which have not been studied in existing works on either sepsis prediction or uncertainty quantification. In this study, we first define such propagated uncertainty as the variance of prediction output and then introduce uncertainty propagation methods to quantify the propagated uncertainty. Moreover, for the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm to increase confidence by actively recommending clinicians to observe the most informative variables. We validate the proposed models in both publicly available data (i.e., MIMIC-III and AmsterdamUMCdb) and proprietary data in The Ohio State University Wexner Medical Center (OSUWMC). The experimental results show that the propagated uncertainty is dominant at the beginning of admissions to hospitals and the proposed algorithm outperforms state-of-the-art active sensing methods. Finally, we implement a SepsisLab system for early sepsis prediction and active sensing based on our pre-trained models. Clinicians and potential sepsis patients can benefit from the system in early prediction and diagnosis of sepsis.

Despite intense efforts in basic and clinical research, an individualized ventilation strategy for critically ill patients remains a major challenge. Recently, dynamic treatment regime (DTR) with reinforcement learning (RL) on electronic health records (EHR) has attracted interest from both the healthcare industry and machine learning research community. However, most learned DTR policies might be biased due to the existence of confounders. Although some treatment actions non-survivors received may be helpful, if confounders cause the mortality, the training of RL models guided by long-term outcomes (e.g., 90-day mortality) would punish those treatment actions causing the learned DTR policies to be suboptimal. In this study, we develop a new deconfounding actor-critic network (DAC) to learn optimal DTR policies for patients. To alleviate confounding issues, we incorporate a patient resampling module and a confounding balance module into our actor-critic framework. To avoid punishing the effective treatment actions non-survivors received, we design a short-term reward to capture patients' immediate health state changes. Combining short-term with long-term rewards could further improve the model performance. Moreover, we introduce a policy adaptation method to successfully transfer the learned model to new-source small-scale datasets. The experimental results on one semi-synthetic and two different real-world datasets show the proposed model outperforms the state-of-the-art models. The proposed model provides individualized treatment decisions for mechanical ventilation that could improve patient outcomes.

Age-related macular degeneration (AMD) is the leading cause of irreversible blindness in developed countries. Identifying patients at high risk of progression to late AMD, the sight-threatening stage, is critical for clinical actions, including medical interventions and timely monitoring. Recently, deep-learning-based models have been developed and achieved superior performance for late AMD prediction. However, most existing methods are limited to the color fundus photography (CFP) from the last ophthalmic visit and do not include the longitudinal CFP history and AMD progression during the previous years' visits. Patients in different AMD subphenotypes might have various speeds of progression in different stages of AMD disease. Capturing the progression information during the previous years' visits might be useful for the prediction of AMD progression. In this work, we propose a C ontrastive-A ttention-based T ime-aware L ong S hort-T erm M emory network (CAT-LSTM ) to predict AMD progression. First, we adopt a convolutional neural network (CNN) model with a contrastive attention module (CA) to extract abnormal features from CFPs. Then we utilize a time-aware LSTM (T-LSTM) to model the patients' history and consider the AMD progression information. The combination of disease progression, genotype information, demographics, and CFP features are sent to T-LSTM. Moreover, we leverage an auto-encoder to represent temporal CFP sequences as fixed-size vectors and adopt k-means to cluster them into subphenotypes. We evaluate the proposed model based on real-world datasets, and the results show that the proposed model could achieve 0.925 on area under the receiver operating characteristic (AUROC) for 5-year late-AMD prediction and outperforms the state-of-the-art methods by more than 3%, which demonstrates the effectiveness of the proposed CAT-LSTM. After analyzing patient representation learned by an auto-encoder, we identify 3 novel subphenotypes of AMD patients with different characteristics and progression rates to late AMD, paving the way for improved personalization of AMD management. The code of CAT-LSTM can be found at https://github.com/yinchangchang/CAT-LSTM .

Complication risk profiling is a key challenge in the healthcare domain due to the complex interaction between heterogeneous entities (e.g., visit, disease, medication) in clinical data. With the availability of real-world clinical data such as electronic health records and insurance claims, many deep learning methods are proposed for complication risk profiling. However, these existing methods face two open challenges. First, data heterogeneity relates to those methods leveraging clinical data from a single view only while the data can be considered from multiple views (e.g., sequence of clinical visits, set of clinical features). Second, generalized prediction relates to most of those methods focusing on single-task learning, whereas each complication onset is predicted independently, leading to suboptimal models. We propose a multi-view multi-task network (MuViTaNet) for predicting the onset of multiple complications to tackle these issues. In particular, MuViTaNet complements patient representation by using a multi-view encoder to effectively extract information by considering clinical data as both sequences of clinical visits and sets of clinical features. In addition, it leverages additional information from both related labeled and unlabeled datasets to generate more generalized representations by using a new multi-task learning scheme for making more accurate predictions. The experimental results show that MuViTaNet outperforms existing methods for profiling the development of cardiac complications in breast cancer survivors. Furthermore, thanks to its multi-view multi-task architecture, MuViTaNet also provides an effective mechanism for interpreting its predictions in multiple perspectives, thereby helping clinicians discover the underlying mechanism triggering the onset and for making better clinical treatments in real-world scenarios.

Disease progression modeling (DPM) involves using mathematical frameworks to quantitatively measure the severity of how certain disease progresses. DPM is useful in many ways such as predicting health state, categorizing disease stages, and assessing patients’ disease trajectory, etc. Recently, with the wider availability of electronic health records (EHR) and the broad application of data-driven machine learning methods, DPM has attracted much attention yet remains two major challenges: (i) Due to the existence of irregularity, heterogeneity, and long-term dependency in EHRs, most existing DPM methods might not be able to provide comprehensive patient representations. (ii) Lots of records in EHRs might be irrelevant to the target disease. Most existing models learn to automatically focus on the relevant information instead of explicitly capture the target-relevant events, which might make the learned model suboptimal. To address these two issues, we propose Temporal Clustering with External Memory Network (TC-EMNet) for DPM that groups patients with similar trajectories to form disease clusters/stages. TC-EMNet uses a variational autoencoder (VAE) to capture internal complexity from the input data and utilizes an external memory work to capture long-term distance information, both of which are helpful for producing comprehensive patient health states. Last but not least, the k-means algorithm is adopted to cluster the extracted comprehensive patient representation to capture disease progression. Experiments on two real-world datasets show that our model demonstrates competitive clustering performance against state-of-the-art methods and is able to identify clinically meaningful clusters. The visualization of the patient representations shows that the proposed model can generate better patient health states than the baselines.

Estimating the individual treatment effect (ITE) from observational data is meaningful and practical in healthcare. Existing work mainly relies on the strong ignorability assumption that no hidden confounders exist, which may lead to bias in estimating causal effects. Some studies considering the hidden confounders are designed for static environment and not easily adaptable to a dynamic setting. In fact, most observational data (e.g., electronic medical records) is naturally dynamic and consists of sequential information. In this paper, we propose Deep Sequential Weighting (DSW) for estimating ITE with time-varying confounders. Specifically, DSW infers the hidden confounders by incorporating the current treatment assignments and historical information using a deep recurrent weighting neural network. The learned representations of hidden confounders combined with current observed data are leveraged for potential outcome and treatment predictions. We compute the time-varying inverse probabilities of treatment for re-weighting the population. We conduct comprehensive comparison experiments on fully-synthetic, semi-synthetic and real-world datasets to evaluate the performance of our model and baselines. Results demonstrate that our model can generate unbiased and accurate treatment effect by conditioning both time-varying observed and hidden confounders, paving the way for personalized medicine.

Sepsis is a heterogeneous clinical syndrome that is the leading cause of mortality in hospital intensive care units (ICUs). Identification of sepsis subphenotypes may allow for more precise treatments and lead to more targeted clinical interventions. Recently, sepsis subtyping on electronic health records (EHRs) has attracted interest from healthcare researchers. However, most sepsis subtyping studies ignore the temporality of EHR data and suffer from missing values. In this paper, we propose a new sepsis subtyping framework to address the two issues. Our subtyping framework consists of a novel Time-Aware Multi-modal auto-Encoder (TAME) model which introduces time-aware attention mechanism and incorporates multi-modal inputs (e.g., demographics, diagnoses, medications, lab tests and vital signs) to impute missing values, a dynamic time wrapping (DTW) method to measure patients' temporal similarity based on the imputed EHR data, and a weighted k-means algorithm to cluster patients. Comprehensive experiments on real-world datasets show TAME outperforms the baselines on imputation accuracy. After analyzing TAME-imputed EHR data, we identify four novel subphenotypes of sepsis patients, paving the way for improved personalization of sepsis management.

Medical images are widely used in the medical domain for the diagnosis and treatment of diseases. Reading a medical image and summarizing its insights is a routine, yet nonetheless time-consuming task, which often represents a bottleneck in the clinical diagnosis process. Automatic report generation can relieve the issues. However, generating medical reports presents two major challenges: (i) it is hard to accurately detect all the abnormalities simultaneously, especially the rare diseases; (ii) a medical image report consists of many paragraphs and sentences, which are longer than natural image captions. We present a new framework to accurately detect the abnormalities and automatically generate medical reports. The report generation model is based on hierarchical recurrent neural network (HRNN). We introduce a topic matching mechanism to HRNN, so as to make generated reports more accurate and diverse. The soft attention mechanism is also introduced to HRNN model. Experimental results on two image-paragraph pair datasets show that our framework outperforms all the state-of-art methods.

Due to their promising performance in clinical risk prediction with Electronic Health Records (EHRs), deep learning methods have attracted significant interest from healthcare researchers. However, there are 4 challenges: (i) Data insufficiency. Many methods require large amounts of training data to achieve satisfactory results. (ii) Interpretability. Results from many methods are hard to explain to clinicians (e.g., why the models make particular predictions and which events cause clinical outcomes). (iii) Domain knowledge integration. No existing method dynamically exploits complicated medical knowledge (e.g., relations such as cause and is-caused-by between clinical events). (iv) Time interval information. Most existing methods only consider the relative order of visits from EHRs, but ignore the irregular time intervals between neighboring visits. In the study, we propose a new model, Domain Knowledge Guided Recurrent Neural Networks (DG-RNN), by directly introducing domain knowledge from the medical knowledge graph into an RNN architecture, as well as taking the irregular time intervals into account. Experimental results on heart failure risk prediction tasks show that our model not only outperforms state-of-the-art deep-learning based risk prediction models, but also associates individual medical events with heart failure onset, thus paving the way for interpretable accurate clinical risk predictions.

Thanks to the widespread adoption of Electronic Health Record (EHR) systems, a variety of data-driven clinical risk prediction approaches have been spawned in recent years. However, there remain three challenges, which if addressed would improve the performance and applicability of such models. (i) Due to the limited data sharing between different health care institutions, the EHR data collected by a single institution is often inadequate or missing some visits records. The limited number of data cannot meet the large sample required of recent approaches especially deep learning models. In addition, the missing records (due to visiting different institution) may contain important health condition of the patient, which if ignored would cause prediction bias. (ii) Few existing approaches take clinical knowledge into account. The auxiliary knowledge if included can greatly reduce the data dependency of many modern learning algorithms. (iii) Most existing deep learning based methods are unable to identify the contribution of each medical event to the final results, which prohibits such models from being widely accepted in practical clinical applications. In this paper, we propose an interpretable and knowledge-guided deep model to address these challenges. Specifically, we distill knowledge from a clinical knowledge graph both explicitly and implicitly, which can not only supplement inadequate patient records but also guide the predicting process of the model. Furthermore, skip-connections and attention mechanisms are adopted to improve the interpretability of our model. In the context of heart failure prediction task, our model outperforms several state-of-the-art methods. Finally, a series of case studies are presented to prove the interpretability of our model.

The need for short-text classification arises in many text mining applications particularly health care applications. In such applications shorter texts mean linguistic ambiguity limits the semantic expression, which in turns would make typical methods fail to capture the exact semantics of the scarce words. This is particularly true in health care domains when the text contains domain-specific or infrequently appearing words, whose embedding can not be easily learned due to the lack of training data. Deep neural network has shown great potentials in boost the performance of such problems according to its strength on representation capacity. In this paper, we propose a bidirectional long short-term memory (BI-LSTM) recurrent network to address the short-text classification problem that can be used in two settings. Firstly when a knowledge dictionary is available we adopt the well-known attention mechanism to guide the training of network using the domain knowledge in the dictionary. Secondly, to address the cases when domain knowledge dictionary is not available, we present a multi-task model to jointly learn the domain knowledge dictionary and do the text classification task simultaneously. We apply our method to a real-world interactive healthcare system and an extensively public available ATIS dataset. The results show that our model can positively grasp the key point of the text and significantly outperforms many state-of-the-art baselines.

Active learning aims to reduce manual labeling efforts by proactively selecting the most informative unlabeled instances to query. In real-world scenarios, it's often more practical to query a batch of instances rather than a single one at each iteration. To achieve this we need to keep not only the informativeness of the instances but also their diversity. Many heuristic methods have been proposed to tackle batch mode active learning problems, however, they suffer from two limitations which if addressed would significantly improve the query strategy. Firstly, the similarity amongst instances is simply calculated using the feature vectors rather than being jointly learned with the classification model. This weakens the accuracy of the diversity measurement. Secondly, these methods usually exploit the decision boundary by querying the data points close to it. However, this can be inefficient when the labeled set is too small to reveal the true boundary. In this paper, we address both limitations by proposing a deep neural network based algorithm. In the training phase, a pairwise deep network is not only trained to perform classification, but also to project data points into another space, where the similarity can be more precisely measured. In the query selection phase, the learner selects a set of instances that are maximally uncertain and minimally redundant (exploitation), as well as are most diverse from the labeled instances (exploration). We evaluate the effectiveness of the proposed method on a variety of classification tasks: MNIST classification, opinion polarity detection, and heart failure prediction. Our method outperforms the baselines with both higher classification accuracy and faster convergence rate.

Evaluating the clinical similarities between pairwisepatients is a fundamental problem in healthcare informatics. Aproper patient similarity measure enables various downstreamapplications, such as cohort study and treatment comparative effectiveness research. One major carrier for conductingpatient similarity research is the Electronic Health Records(EHRs), which are usually heterogeneous, longitudinal, andsparse. Though existing studies on learning patient similarityfrom EHRs have shown being useful in solving real clinicalproblems, their applicability is limited due to the lack of medicalinterpretations. Moreover, most previous methods assume avector based representation for patients, which typically requiresaggregation of medical events over a certain time period. As aconsequence, the temporal information will be lost. In this paper, we propose a patient similarity evaluation framework based ontemporal matching of longitudinal patient EHRs. Two efficientmethods are presented, unsupervised and supervised, both ofwhich preserve the temporal properties in EHRs. The supervisedscheme takes a convolutional neural network architecture, andlearns an optimal representation of patient clinical recordswith medical concept embedding. The empirical results on real-world clinical data demonstrate substantial improvement overthe baselines.