Query: Qiuling Suo

title: Deep Patient Similarity Learning for Personalized Healthcare
abstract: Predicting patients' risk of developing certain diseases is an important research topic in healthcare. Accurately identifying and ranking the similarity among patients based on their historical records is a key step in personalized healthcare. The electric health records (EHRs) which are irregularly sampled and have varied patient visit lengths, cannot be directly used to measure patient similarity due to the lack of an appropriate representation. Moreover, there needs an effective approach to measure patient similarity on EHRs. In this paper, we propose two novel deep similarity learning frameworks which simultaneously learn patient representations and measure pairwise similarity. We use a convolutional neural network (CNN) to capture local important information in EHRs and then feed the learned representation into triplet loss or softmax cross entropy loss. After training, we can obtain pairwise distances and similarity scores. Utilizing the similarity information, we then perform disease predictions and patient clustering. Experimental results show that CNN can better represent the longitudinal EHR sequences, and our proposed frameworks outperform state of the art distance metric learning methods.

title: Metric Learning on Healthcare Data with Incomplete Modalities
abstract: Utilizing multiple modalities to learn a good distance metric is of vital importance for various clinical applications. However, it is common that modalities are incomplete for some patients due to various technical and practical reasons in healthcare datasets. Existing metric learning methods cannot directly learn the distance metric on such data with missing modalities. Nevertheless, the incomplete data contains valuable information to characterize patient similarity and modality relationships, and they should not be ignored during the learning process. To tackle the aforementioned challenges, we propose a metric learning framework to perform missing modality completion and multi modal metric learning simultaneously. Employing the generative adversarial networks, we incorporate both complete and incomplete data to learn the mapping relationship between modalities. After completing the missing modalities, we use the nonlinear representations extracted by the discriminator to learn the distance metric among patients. Through jointly training the adversarial generation part and metric learning, the similarity among patients can be learned on data with missing modalities. Experimental results show that the proposed framework learns more accurate distance metric on real world healthcare datasets with incomplete modalities, comparing with the state of the art approaches. Meanwhile, the quality of the generated modalities can be preserved.

title: Risk Prediction on Electronic Health Records with Prior Medical Knowledge
abstract: Predicting the risk of potential diseases from Electronic Health Records (EHR) has attracted considerable attention in recent years, especially with the development of deep learning techniques. Compared with traditional machine learning models, deep learning based approaches achieve superior performance on risk prediction task. However, none of existing work explicitly takes prior medical knowledge (such as the relationships between diseases and corresponding risk factors) into account. In medical domain, knowledge is usually represented by discrete and arbitrary rules. Thus, how to integrate such medical rules into existing risk prediction models to improve the performance is a challenge. To tackle this challenge, we propose a novel and general framework called PRIME for risk prediction task, which can successfully incorporate discrete prior medical knowledge into all of the state of the art predictive models using posterior regularization technique. Different from traditional posterior regularization, we do not need to manually set a bound for each piece of prior medical knowledge when modeling desired distribution of the target disease on patients. Moreover, the proposed PRIME can automatically learn the importance of different prior knowledge with a log linear model.Experimental results on three real medical datasets demonstrate the effectiveness of the proposed framework for the task of risk prediction

title: Personalized disease prediction using a CNN based similarity learning method
abstract: Predicting patients' risk of developing certain diseases is an important research topic in healthcare. Personalized predictive modeling, which focuses on building specific models for individual patients, has shown its advantages on utilizing heterogeneous health data compared to global models trained on the entire population. Personalized predictive models use information from similar patient cohorts, in order to capture the specific characteristics. Accurately identifying and ranking the similarity among patients based on their historical records is a key step in personalized modeling. The electric health records (EHRs) which are irregular sampled and have varied patient visit lengths, cannot be directly used to measure patient similarity due to lack of an appropriate vector representation. In this paper, we build a novel time fusion CNN framework to simultaneously learn patient representations and measure pairwise similarity. Compared to a traditional CNN, our time fusion CNN can learn not only the local temporal relationships but also the contributions from each time interval. Along with the similarity learning process, the output information which is the probability distribution is used to rank similar patients. Utilizing the similarity scores, we perform personalized disease predictions, and compare the effect of different vector representations and similarity learning metrics.

title: Wave2Vec: Deep representation learning for clinical temporal data
abstract: Representation learning for time series has gained increasing attention in healthcare domain. The recent advancement in semantic learning allows researcher to learn meaningful deep representations of clinical medical concepts from Electronic Health Records (EHRs) However, existing models cannot deal with continuous physiological records, which are often included in EHRs. The major challenges for this task are to model non obvious representations from observed high resolution biosignals, and to interpret the learned features. To address these issues, we propose Wave2Vec an end to end deep representation learning model, to bridge the gap between biosignal processing and semantic learning. Wave2Vec not only jointly learns both inherent and temporal representations of biosignals, but also allows us to interpret the learned representations reasonably over time. We propose two embedding mechanisms to capture the temporal knowledge within signals, and discover latent knowledge from signals in time frequency domain, namely component based motifs. To validate the effectiveness of our model in clinical task, we carry out experiments on two real world benchmark biosignal datasets. Experimental results demonstrate that the proposed Wave2Vec model outperforms six feature learning baselines in biosignal processing. Analytical results show that the proposed model can incorporate both motif co occurrence information and time series information of biosignals, and hence provides clinically meaningful interpretation.

title: Recurrent Imputation for Multivariate Time Series with Missing Values
abstract: Multivariate time series data are ubiquitous in real world healthcare systems. It is a common issue that the data contain missing values due to various reasons, such as sensor damage, data corruption, patient dropout. There have been various works on filling the missing values in multivariate time series. Classical imputation methods include KNN based, Matrix Factorization based, and Expectation Maximization (EM) based imputation and so on. These methods are developed for general imputation purpose and rarely utilize the temporal relations between observations. Classical statistical time series models such as autoregressive (AR) models and dynamic linear models (DLM) (e.g.  can capture the temporal information, but they are essentially linear and may not be suitable for modern complex large scale data. ImputeTS  employs time dependencies on univariate time series imputation, which ignores feature correlations. Recent works [3, 4] develop the imputation framework that can take advantages of the traditional methods and resolve their drawbacks. Another trend of models is based on recurrent neural network (RNN) ] utilizing RNN to capture temporal dependencies and further considering various aspects of the data characteristics, such as time decay, feature correlation, residual link, and temporal belief gate. In this paper, we propose an RNN based imputation method for filling the missing values in multivariate time series. RNN is used to capture the temporal information of time series. We use a global RNN and variable specific RNNs to perform imputation based on historical information, and a fusion gate to combine them. At each timestamp, we use a regression layer to impute the value of a certain variable using other variables, by utilizing the relationship of variables. Bi directional imputation is adopted to improve the ability of long term memory and performance of starting timestamps.

title: A novel channel aware attention framework for multi channel EEG seizure detection via multi view deep learning
abstract: Epileptic seizure detection using multi channel scalp electroencephalogram (EEG) signals has gained increasing attention in clinical therapy. Recently, researchers attempt to employ deep learning techniques with channel selection to determine critical channels. However, existing models with such hard selection procedure do not take dynamic constraints into account, since the irrelevant channels vary significantly across different situations. To address these issues, we propose ChannelAtt, an end to end multi view deep learning model with channel aware attention mechanism, to express multi channel EEG signals in a high level space with interpretable meanings. ChannelAtt jointly learns both multi view representation and its contribution scores. We propose two attention mechanisms to learn the attentional representations of multi channel EEG signals in time frequency domain. Experimental results show that the proposed ChannelAtt model outperforms the baselines in detecting epileptic seizures. Analytical results of a case study demonstrate that the learned attentional representations are meaningful.

title: A Multi task Framework for Monitoring Health Conditions via Attention based Recurrent Neural Networks
abstract: Monitoring the future health status of patients from the historical Electronic Health Record (EHR) is a core research topic in predictive healthcare. The most important challenges are to model the temporality of sequential EHR data and to interpret the prediction results. In order to reduce the future risk of diseases, we propose a multi task framework that can monitor the multiple status ofdiagnoses. Patients' historical records are directly fed into a Recurrent Neural Network (RNN) which memorizes all the past visit information, and then a task specific layer is trained to predict multiple diagnoses. Moreover, three attention mechanisms for RNNs are introduced to measure the relationships between past visits and current status. Experimental results show that the proposed attention based RNNs can significantly improve the prediction accuracy compared to widely used approaches. With the attention mechanisms, the proposed framework is able to identify the visit information which is important to the final prediction.

title: Multi task Sparse Metric Learning for Monitoring Patient Similarity Progression
abstract: A clinically meaningful distance metric, which is learned from measuring patient similarity, plays an important role in clinical decision support applications. Several metric learning approaches have been proposed to measure patient similarity, but they are mostly designed for learning the metric at only one time point/interval. It leads to a problem that those approaches cannot reflect the similarity variations among patients with the progression of diseases. In order to capture similarity information from multiple future time points simultaneously, we formulate a multi task metric learning approach to identify patient similarity. However, it is challenging to directly apply traditional multi task metric learning methods to learn such similarities due to the high dimensional, complex and noisy nature of healthcare data. Besides, the disease labels often have clinical relationships, which should not be treated as independent. Unfortunately, traditional formulation of the loss function ignores the degree of labels' similarity. To tackle the aforementioned challenges, we propose mtTSML, a multi task triplet constrained sparse metric learning method, to monitor the similarity progression of patient pairs. In the proposed model, the distance for each task can be regarded as the combination of a common part and a task specific one in the transformed low rank space. We then perform sparse feature selection for each individual task to select the most discriminative information. Moreover, we use triplet constraints to guarantee the margin between similar and less similar pairs according to the ordered information of disease severity levels (i.e. labels) The experimental results on two real world healthcare datasets show that the proposed multi task metric learning method significantly outperforms the state of the art baselines, including both single task and multi task metric learning methods.

title: A Reliability Aware Vehicular Crowdsensing System for Pothole Profiling
abstract: Accurately profiling potholes on road surfaces not only helps eliminate safety related concerns and improve commuting efficiency for drivers, but also reduces unnecessary maintenance cost for transportation agencies. In this paper, we propose a smartphone based system that is capable of precisely estimating the length and depth of potholes, and introduce a holistic design on pothole data collection, profile aggregation and pothole warning and reporting. The proposed system relies on the built in inertial sensors of vehicle carried smartphones to estimate pothole profiles, and warn the driver about incoming potholes. Because of the difference in driving behaviors and vehicle suspension systems, a major challenge in building such system is how to aggregate conflicting sensory reports from multiple participating vehicles. To tackle this challenge, we propose a novel reliability aware data aggregation algorithm called Reliability Adaptive Truth Discovery (RATD) It infers the reliability for each data source and aggregates pothole profiles in an unsupervised fashion. Our field test shows that the proposed system can effectively estimate pothole profiles, and the RATD algorithm significantly improves the profiling accuracy compared with popular data aggregation methods.