An Interpretable Fast Model for Predicting The Risk of Heart Failure

Zhang, Xianli; Qian, Buyue; Li, Xiaoyü; Wei, Jishang; Zheng, Yingqian; Song, Lingyun; Zheng, Qinghua

doi:10.1137/1.9781611975673.65

Cited by 17 publications

(17 citation statements)

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“…neural machine translation [23] and speech recognition [25]. Recently, attention mechanisms are increasingly applied to improve not only the accuracy but also the interpretability of deep learning models [26][27][28]. In [26], the authors proposed the GRaph-based Attention Model (GRAM) for healthcare representation learning, which infuses information from medical ontologies into deep learning models via attention mechanism and the attention behavior during prediction could be explained intuitively by showing the attention weights of each node in the knowledge graph.…”

Section: Related Workmentioning

confidence: 99%

Interpretable clinical prediction via attention-based neural network

Chen

Dong

Wang³

et al. 2020

BMC Med Inform Decis Mak

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Background: The interpretability of results predicted by the machine learning models is vital, especially in the critical fields like healthcare. With the increasingly adoption of electronic healthcare records (EHR) by the medical organizations in the last decade, which accumulated abundant electronic patient data, neural networks or deep learning techniques are gradually being applied to clinical tasks by utilizing the huge potential of EHR data. However, typical deep learning models are black-boxes, which are not transparent and the prediction outcomes of which are difficult to interpret. Methods: To remedy this limitation, we propose an attention neural network model for interpretable clinical prediction. In detail, the proposed model employs an attention mechanism to capture critical/essential features with their attention signals on the prediction results, such that the predictions generated by the neural network model can be interpretable. Results: We evaluate our proposed model on a real-world clinical dataset consisting of 736 samples to predict readmissions for heart failure patients. The performance of the proposed model achieved 66.7 and 69.1% in terms of accuracy and AUC, respectively, and outperformed the baseline models. Besides, we displayed patient-specific attention weights, which can not only help clinicians understand the prediction outcomes, but also assist them to select individualized treatment strategies or intervention plans. Conclusions: The experimental results demonstrate that the proposed model can improve both the prediction performance and interpretability by equipping the model with an attention mechanism.

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Section: Related Workmentioning

confidence: 99%

Interpretable clinical prediction via attention-based neural network

Chen

Dong

Wang³

et al. 2020

BMC Med Inform Decis Mak

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“…Data set-I is the third version of Medical Information Mart for Intensive Care, { a public accessible benchmark data set for critical care that has been widely applied in a variety of researches. 8,[10][11][12]21 Data set-II is a private data set that is constructed from a real-world longitudinal EHR database. The medical events from both data sets are encoded following the ICD coding system.…”

Section: Data Descriptionmentioning

confidence: 99%

Graph Neural Network-Based Diagnosis Prediction

Qian

Zhang

et al. 2020

Big Data

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Diagnosis prediction is an important predictive task in health care that aims to predict the patient future diagnosis based on their historical medical records. A crucial requirement for this task is to effectively model the highdimensional, noisy, and temporal electronic health record (EHR) data. Existing studies fulfill this requirement by applying recurrent neural networks with attention mechanisms, but facing data insufficiency and noise problem. Recently, more accurate and robust medical knowledge-guided methods have been proposed and have achieved superior performance. These methods inject the knowledge from a graph structure medical ontology into deep models via attention mechanisms to provide supplementary information of the input data. However, these methods only partially leverage the knowledge graph and neglect the global structure information, which is an important feature. To address this problem, we propose an end-to-end robust solution, namely Graph Neural Network-Based Diagnosis Prediction (GNDP). First, we propose to utilize the medical knowledge graph as an internal information of a patient by constructing sequential patient graphs. These graphs not only carry the historical information from the EHR but also infuse with domain knowledge. Then we design a robust diagnosis prediction model based on a spatial-temporal graph convolutional network. The proposed model extracts meaningful features from sequential graph EHR data effectively through multiple spatial-temporal graph convolution units to generate robust patients' representations for accurate diagnosis predictions. We evaluate the performance of GNDP against a set of state-of-the-art methods on two real-world medical data sets, the results demonstrate that our methods can achieve a better utilization of knowledge graph and improve the accuracy on diagnosis prediction tasks.

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“…We represent each patient's demographics as one-hot vectors. Patients' ages are divided into several age groups (e.g., [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39].…”

Section: Problem Definition and Notationmentioning

confidence: 99%

“…Only the events related to some specific diseases are crucial to predict risk. Therefore, attention mechanism is introduced to automatically attend to the useful events [ 8 , 11 , 21 ].…”

Section: Related Workmentioning

confidence: 99%

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An interpretable risk prediction model for healthcare with pattern attention

Kamal

Yin

Qian

et al. 2020

BMC Med Inform Decis Mak

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Background The availability of massive amount of data enables the possibility of clinical predictive tasks. Deep learning methods have achieved promising performance on the tasks. However, most existing methods suffer from three limitations: (1) There are lots of missing value for real value events, many methods impute the missing value and then train their models based on the imputed values, which may introduce imputation bias. The models’ performance is highly dependent on the imputation accuracy. (2) Lots of existing studies just take Boolean value medical events (e.g. diagnosis code) as inputs, but ignore real value medical events (e.g., lab tests and vital signs), which are more important for acute disease (e.g., sepsis) and mortality prediction. (3) Existing interpretable models can illustrate which medical events are conducive to the output results, but are not able to give contributions of patterns among medical events. Methods In this study, we propose a novel interpretable Pattern Attention model with Value Embedding (PAVE) to predict the risks of certain diseases. PAVE takes the embedding of various medical events, their values and the corresponding occurring time as inputs, leverage self-attention mechanism to attend to meaningful patterns among medical events for risk prediction tasks. Because only the observed values are embedded into vectors, we don’t need to impute the missing values and thus avoids the imputations bias. Moreover, the self-attention mechanism is helpful for the model interpretability, which means the proposed model can output which patterns cause high risks. Results We conduct sepsis onset prediction and mortality prediction experiments on a publicly available dataset MIMIC-III and our proprietary EHR dataset. The experimental results show that PAVE outperforms existing models. Moreover, by analyzing the self-attention weights, our model outputs meaningful medical event patterns related to mortality. Conclusions PAVE learns effective medical event representation by incorporating the values and occurring time, which can improve the risk prediction performance. Moreover, the presented self-attention mechanism can not only capture patients’ health state information, but also output the contributions of various medical event patterns, which pave the way for interpretable clinical risk predictions. Availability The code for this paper is available at: https://github.com/yinchangchang/PAVE.

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An Interpretable Fast Model for Predicting The Risk of Heart Failure

Cited by 17 publications

References 0 publications

Interpretable clinical prediction via attention-based neural network

Interpretable clinical prediction via attention-based neural network

Graph Neural Network-Based Diagnosis Prediction

An interpretable risk prediction model for healthcare with pattern attention

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