Relaxed Weight Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series

by   Jeeheh Oh, et al.
University of Michigan

Recurrent neural networks (RNNs) are commonly applied to clinical time-series data with the goal of learning patient risk stratification models. Their effectiveness is due, in part, to their use of parameter sharing over time (i.e., cells are repeated hence the name recurrent). We hypothesize, however, that this trait also contributes to the increased difficulty such models have with learning relationships that change over time. Conditional shift, i.e., changes in the relationship between the input X and the output y, arises if the risk factors for the event of interest change over the course of a patient admission. While in theory, RNNs and gated RNNs (e.g., LSTMs) in particular should be capable of learning time-varying relationships, when training data are limited, such models often fail to accurately capture these dynamics. We illustrate the advantages and disadvantages of complete weight sharing (RNNs) by comparing an LSTM with shared parameters to a sequential architecture with time-varying parameters on three clinically-relevant prediction tasks: acute respiratory failure (ARF), shock, and in-hospital mortality. In experiments using synthetic data, we demonstrate how weight sharing in LSTMs leads to worse performance in the presence of conditional shift. To improve upon the dichotomy between complete weight sharing vs. no weight sharing, we propose a novel RNN formulation based on a mixture model in which we relax weight sharing over time. The proposed method outperforms standard LSTMs and other state-of-the-art baselines across all tasks. In settings with limited data, relaxed weight sharing can lead to improved patient risk stratification performance.



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1 Introduction

Recurrent neural networks (RNNs) capture temporal dependencies between past inputs and output in addition to the relationship between current input and . Their successful application to date is due in part to their explicit parameter sharing over time (Harutyunyan et al., 2017; Rajkomar et al., 2018). However, while advantageous in many settings, such parameter sharing could hinder the ability of the model to accurately capture time-varying relationships, i.e., tasks that exhibit temporal conditional shift.

In healthcare, temporal condition shift may arise in clinical prediction tasks when the factors that put a patient at risk for a particular adverse outcome at the beginning of a hospital visit differ from those that put a patient at risk at the end of their stay. Failure to recognize conditional shift when building risk stratification models could lead to temporal biases in learned models; models may capture the average trend at the cost of decreased performance at specific points in time. This could be especially detrimental to models deployed and evaluated in real time.

More formally, conditional shift refers to the change in the conditional distribution across tasks. In particular, we consider temporal conditional shift, i.e., the setting in which the relationship between and is a function of both and time (). We hypothesize that RNN’s complete sharing of parameters across time steps makes it difficult to accurately model temporal conditional shift. To address this, one could jointly learn a different cell for each time step, but such an architecture may easily lead to overfitting. More importantly, such an approach does not leverage the fact that many of the relationships may be shared across time.

On synthetic data, in which we can control the amount of conditional shift, we explore the trade-offs in performance between models that share weights across time versus models that do not. Beyond synthetic data, we illustrate the presence of temporal conditional shift in real clinical prediction tasks. To tackle this issue, we propose a novel RNN framework based on a mixture approach that relaxes weight sharing over time, without sacrificing generalization performance. Applied to three clinically relevant patient risk stratification tasks, our proposed approach leads to significantly better performance relative to an LSTM. Moreover, the proposed approach can help shed light on task relatedness across time.

Technical Significance.

Our technical contributions can be summarized as follows:

  • we formalize the problem setting of temporal conditional shift,

  • we illustrate the presence of temporal conditional shift in three clinically relevant tasks

  • we propose a novel approach for relaxed weight sharing within an RNN framework, and

  • we explore situations in which relaxed weight sharing can help.

In theory, given enough data, RNNs should be able to accurately model relationships governed by temporal conditional shift. However, oftentimes in clinical applications, we have a limited amount of data from which to learn. Going forward, researchers should check for the presence of conditional shift by comparing the proposed approach with an LSTM. If conditional shift is detected, then one may be able to more accurately model temporal dynamics by relaxing weight sharing.

Clinical Relevance.

Though tasks involving time-varying relationships are common in healthcare, current techniques rarely explicitly model temporal conditional shift. In this work, we investigate the extent to which temporal conditional shift impacts clinical prediction tasks. We consider three clinical tasks: prediction of acute respiratory failure (ARF), shock, and in-hospital mortality during an ICU admission. These tasks were selected based on their clinical relevance. ARF contributes to over 380,000 deaths in the US per year (Stefan et al., 2013) and represents a challenging prediction task due to its multi-factorial etiology. Shock refers to the inadequate perfusion of blood oxygen to organs or tissues and can result in severe organ dysfunction and death when not recognized and treated immediately (Gaieski and Mikkelsen, 2016). Both of these conditions are upstream events that contribute to our third prediction task, in-hospital mortality. The ability to identify patients at risk of developing ARF or shock could facilitate improved patient triage, timely intervention, preventing irreversible damage and ultimately better patient outcomes. Finally, though we consider only these three tasks here, we hypothesize that time-varying risk factors may arise in other clinical prediction tasks.

2 Background & Related Work

We focus on developing techniques that can handle temporal conditional shift, a type of data shift, in tasks involving clinical time-series data. There are two main types of data shift: i) covariate shift and ii) conditional shift. Covariate shift is the problem scenario where varies across datasets (Reddi et al., 2015; Sugiyama et al., 2007), e.g., patient demographics may differ across study populations if they come from different hospitals. In contrast, conditional shift, our main focus, occurs when changes (Zhang et al., 2013; Gong et al., 2016), e.g., two hospitals have similar patient populations but different risk factors due to differences in clinical protocols. In conditional shift, the relationship between input and output

has shifted, not the population. For some time, the study of data shift has driven research in the fields of domain adaptation, transfer learning, and multitask learning

(Daumé III, 2007; Pan and Yang, 2010; Ding et al., 2017; Thiagarajan et al., 2018).

Methods for dealing with conditional shift are largely driven by the problem setting. Researchers have explored the use of pre-trained features (Sharif Razavian et al., 2014), generalizable representations (Glorot et al., 2011; Zhuang et al., 2015), and applying importance re-weighting techniques (Zhang et al., 2013). In contrast to these works, we focus on techniques for tackling conditional shift in which the shift is driven by changes in time. In this setting, there is no clear distinction between tasks, because the change occurs gradually. Though related, this differs from ‘data drift’ (i.e., the setting in which relationships change longitudinally) since we consider time on a local/relative scale as opposed to a global/absolute scale (dos Reis et al., 2016; Soemers et al., 2018). That is, instead of focusing on differences between 2018 and 2019, we focus on changes within an admission or a patient. Though such local shift is expected to occur (Bellera et al., 2010; Dekker et al., 2008), it is often overlooked when modeling patient risk.

In the linear setting, past work has explored the use of multitask learning to model the temporal evolution of risk factors within a patient admission, where each day corresponds to a different model but models are learned jointly (Wiens et al., 2016). Related, Dekker et al. (2008)

proposed a variation to Cox regression analysis, studying different time windows separately and using time specific hazard ratios. A complementary approach to dealing with conditional shift also frames the problem as a multitask problem, but focuses on a mixture of experts.

Kohlmorgen et al. (1998)

proposed a two-step approach in which first, a hidden Markov model (HMM) learns a segmentation of the time series, so that each segment is assigned to an expert, and second, the learned experts are mixed at the segmentation boundaries. More recently, mixture of experts have been investigated in other settings

(Eigen et al., 2014; Tan et al., 2016; Ma et al., 2018). In particular, Eigen et al. (2014) stacked experts to form a deep mixture of experts, Tan et al. (2016) mixed the weights of fully connected layers, stacking them to account for training and test set difference in audio processing, and Ma et al. (2018) learned a gating function to mix the output of experts in a multitask learning setting. Building on these approaches, we investigate the utility of a mixture of LSTMs. This facilitates end-to-end learning and allows more than two experts to flexibly contribute to any time step’s prediction.

Nonlinear methods designed to explicitly deal with temporal conditional shift have been more recently explored, focusing primarily on modifications to RNN architectures (Ha et al., 2017; Park and Yoo, 2017). For example Ha et al. (2017) relax weight sharing in Hypernetworks by learning an auxiliary model that sets the primary model’s weights at each time step. Specifically, the auxiliary network can change the main network’s weights through a scalar multiplier. Like Hypernetworks, we also consider a variation on the LSTM. However, in contrast to these variations, we make fewer assumptions about the nature of weight sharing over time.

3 Methods

In this section, we propose an extension to long short-term memory networks (LSTMs) that facilitates learning in the presence of temporal conditional shift. Building off of an LSTM architecture, we present two variations that relax weight sharing across time:

shiftLSTM and mixLSTM. The first approach, shiftLSTM, represents a simple baseline in which different parameters are learned for different time steps (i.e., separate LSTM cells for different time periods). The second approach, mixLSTM addresses the shortcomings of this simple baseline though a mixture approach. But first, we formalize the problem setting of temporal conditional shift and review the architecture of an LSTM.

3.1 Problem Setup - Temporal Conditional Shift & LSTMs

Given time-series data , representing patient covariates over time, where , we consider the task of predicting a sequence of outcomes , where for . We consider a scenario in which the relationship between and varies over time, i.e., , where represent model parameters at time . Because is measured with respect to a patient-specific fiducial marker, we restrict ourselves to conditional shift within a patient-specific time scale (e.g., within an admission).

LSTMs are commonly applied to health data, in part because these data are often represented as time series and LSTMs can (with enough data) capture complex temporal dynamics (Fiterau et al., 2017; Lipton et al., 2016; Harutyunyan et al., 2017). In the sequence-to-sequence setting described above, these models take as input time-varying patient covariates and output a prediction at each time step. Dynamics are captured in part through a cell state that is maintained over time. A standard LSTM cell is described below, where represents element-wise multiplication. Here, and represent the hidden state and the update to the cell state, respectively.


Importantly, each of the learned parameters and in equations (1)-(3), (5) and (7) do not vary with time. To capture time-varying dynamics, the hidden and cell states (, ) must indirectly model conditional shift.

3.2 Relaxing Parameter Sharing in LSTMs

We hypothesize that in settings where the amount of training data is limited – this is often the case in health applications – an approach that more directly models conditional shift through time-varying parameters will outperform a standard LSTM. To this end, we explore two variations on the LSTM: the shiftLSTM and the mixLSTM, illustrated in Figure LABEL:fig:overview.

3.2.1 shiftLSTM - learning abrupt transitions

As a baseline, we consider an approach that naïvely minimizes weight sharing across cells, by learning different parameters and at each time step , instead of the time-invariant parameters in equations (1)-(3), (5), and (7). This mimics a feed-forward network, with the hidden state and cell state propagating forward at each time step, but computes the output sequentially. This naïve approach to relaxed parameter sharing assumes no shared relationships across time. As a result, its capacity is significantly greater than that of an LSTM. Given the same hidden state size, the number of parameters scales linearly with the number of time steps. We hypothesize that this naïve approach will result in overfitting and poor generalization in settings with limited data. To strike a balance between the two extremes, complete sharing and no sharing, we explore a variation of this approach that assumes weights are shared across a subset of adjacent time steps: shiftLSTM-.

shiftLSTM-. This approach combines different LSTM cells over time, where the cells are combined sequentially, resulting in different model parameters every time steps (Figure LABEL:fig:overview_a).

is a hyperparameter, with

shiftLSTM- being no different than an LSTM with complete parameter sharing, and shiftLSTM-

corresponding to different parameters at each time step. All parameters are learned jointly using backpropagation.


Figure 1: An illustrative plot comparing LSTM, shfitLSTM, and mixLSTM, with 4 time steps. Each square denotes an LSTM cell. Cells with the same color share the same weights. Arrows denote transitions between time steps. (a) shiftLSTM- is similar to an LSTM, except it uses different cells for the first two time steps compared to the last two. (b) mixLSTM- has two independent underlying cells, and at each time step, it generates a new cell by mixing a convex combination of the underlying cells. For illustrative purposes, the weights at each time step are drawn from sequential locations on the continuum, but in reality, the weight combination is independent of relative position of the time step.

3.2.2 mixLSTM - learning smooth transitions

As described above, the shiftLSTM approach is restricted to sharing parameters within a certain number of contiguous time steps. This not only leads to a substantial increase in the number of parameters, but also results in possibly abrupt transitions. We hypothesize that changes in health data, and risk factors specifically, are smooth. To allow for smooth transitions in time, we propose a mixture-based approach: mixLSTM- (Figure LABEL:fig:overview_b).

mixLSTM-. Given independent LSTM cells with the same architecture, let and represent the model’s weight parameters from equations (1)-(3), (5) and (7). The parameters of the resulting mixLSTM at time step are


where are the mixing weights and each represents the relevance of the model for time step . The mixing weights are learnable parameters (initialized randomly) and are constrained such that and . Similar to above, is a hyperparameter, but here it can take on any positive integer value. Note that for every , all possible shiftLSTM- models can be learned by mixLSTM-.

By mixing models, instead of abruptly transitioning from one model to another, mixLSTM can learn to share weights over time. Moreover, though we do not constrain the mixing weights to change smoothly, their continuous nature allows for smooth transitions. We verify these properties in our experiments.

4 Experimental Setup

We explore the effects of conditional shift in both synthetic and real data. Here, we describe i) these datasets, ii) multiple additional baselines to which we compare our proposed approach, and iii) the details of our experimental setup.

4.1 Synthetic Data

We begin by considering a scenario in which we can control the extent of conditional shift in the problem. This allows us to test model performance in a setting where the amount of temporal conditional shift is known. Specifically, we consider a multitask variation of the ‘copy memory task’ (Arjovsky et al., 2016), with input sequence where , and output sequence , (we start generating output once we have accumulated values). The output at each time step is some predetermined, weighted combination of inputs from the previous

time steps, described by two probability vectors,

and . and are used for weighting the time steps and feature dimensions respectively. The weights change gradually at every time step , so that each time step’s weighting (or task) is similar to the task from the previous time step. The parameter controls amount of change between temporally adjacent tasks. The generation process of these weights is described below, followed by the generation process of the datasets. Here, is the concatenation of the last inputs. Inputs are generated to be sparse. refers to a renormalization process that ensures the weights are positive and sum to 1.

Procedure SampleWeights(, , ):                      for  do                                  end for       return = = Procedure SampleData():        for ,  do                                               end for       for  do                     end for       return ,

Our goal is then to learn to predict based on input from the current and all preceding time steps. For each , we generated five sets of temporal weights, and then used each set to create five different synthetic dataset tasks where , , and . These twenty-five tasks were kept the same throughout synthetic data testing. Train, validation and test sets all had size unless otherwise specified.

4.2 Clinical Prediction Tasks

In addition to exploring conditional shift in synthetic data, we sought to test our hypotheses using real clinical data from MIMIC-III (Johnson et al., 2016). Below we describe the three clinical prediction tasks we considered, three study populations, patient covariates, and evaluation criteria.

4.2.1 Outcomes

Throughout the first 48 hours of each ICU visit, we sought to make predictions regarding a patient’s risk of experiencing three different outcomes: acute respiratory failure (ARF), shock, and in-hospital mortality, each described in turn below.

ARF. Acute respiratory failure is defined as the need for respiratory support with positive pressure mechanical ventilation (Stefan et al., 2013; Meduri et al., 1996). Onset time of ARF was determined by either the documented receipt of invasive mechanical ventilation (ITEMID: 225792) or non-invasive mechanical ventilation (ITEMID: 225794) as recorded in the PROCEDURESEVENTS_MV table, or documentation of positive end-expiratory pressure (PEEP) (ITEMID: 220339) in the CHARTEVENTS table, whichever occurs earlier. Ventilator records and PEEP settings that are explicitly marked as ERROR did not count towards an event.

Shock. Shock is defined as inadequate perfusion of blood oxygen to organs or tissues (Gaieski and Mikkelsen, 2016). Onset time of shock was determined by earliest receipt of vasopressor therapy (Avni et al., 2015). Using the INPUTEVENTS_MV table, we considered receipt of the following vasopressors:

  • norepinephrine (ITEMID: 221906),

  • epinephrine (ITEMID: 221289),

  • dopamine (ITEMID: 221662),

  • vasopressin (ITEMID: 222315), and

  • phenylephrine (ITEMID: 221749).

Drug administration records with the status of REWRITTEN, incorrect units, or non-positive amounts/durations did not count towards an event.

In-hospital mortality. As in Harutyunyan et al. (2017), the time of in-hospital mortality was determined by comparing patient date of death (DOD column) from the PATIENTS table with hospital admission and discharge times from the ADMISSIONS table.

4.2.2 Cohort Selection

We considered adult admissions with a single, unique ICU visit. This excludes patients with transfers between different ICUs. Patients without labels or observations in the ICU were excluded. Since we are interested in how relationships between covariates and outcome change over time, we focused our analysis on patients who remained in the ICU for at least 48 hours. In addition, for ARF and shock prediction tasks, patients who experienced the event of interest before 48 hours were also excluded. Using the full 48 hours allows us to focus on the temporal trends that are more likely to be present in longer visits. Table 1 shows the number patient admissions and positive labels for the three tasks after applying exclusion criteria.

Task Number of ICU Admissions (%positive)
ARF 3,789 (6.01)
shock 5,481 (5.98)
in-hospital mortality 21,139 (13.23)
Table 1: We considered three clinical prediction tasks. The study population varied in size across tasks, as did the fraction of positive cases (i.e., the portion of patients who experienced the outcome of interest.)

4.2.3 Data Extraction and Feature Choices

We used the same feature extraction procedure as detailed in

Harutyunyan et al. (2017)111 For completeness, we briefly describe the feature extraction process here. For each ICU admission, we extracted 17 physiological features (e.g., heart rate, respiratory rate, Glasgow coma scale, see Table 4

in Appendix) from the first 48 hours of their ICU visit. We applied mean normalization for continuous values and discretized categorical values (mapping each category to a binary feature), resulting in 59 features. We resampled the time series with a uniform sampling rate of once per hour with carry-forward imputation. Mask features, indicating if a value had been imputed resulted in 17 additional features. After preprocessing, each example was represented by

time-series (see Table 5 in Appendix for the complete list of features) of length and a binary label indicating whether or not the patient developed ARF, developed shock or died during the remainder of the hospital visit.

4.2.4 Evaluation

Given these data, the goal was to learn a mapping from the features to a sequence of probabilities for each outcome: ARF, shock or in-hospital mortality. We split the data into training, validation, and test as in Harutyunyan et al. (2017). We used target replication when training the model (Lipton et al., 2016)

. For example, if a patient eventually experiences ARF, then every hour of the first 48 hours is labeled as positive (negative otherwise). We used the validation set for hyperparameter tuning, and report model performance as evaluated on the held-out test set. Since we consider a sequence-to-sequence setting, each model makes a prediction for every hour during the first 48 hours. The accuracy of these predictions was evaluated based on whether or not at least one prediction exceeds a given threshold. This threshold was swept across all ranges to generate a receiver operating characteristics curve (ROC) and precision-recall curve (PR). This resembles how the model is likely to be used in practice. With the goal of making early predictions, as soon as the real-time risk score exceeds some specified threshold, clinicians could be alerted to a patient’s increased risk of the outcome. It should be noted that this differs from the evaluation used in

Harutyunyan et al. (2017)

where a single prediction was made during the 48 hour period. We report performance in terms of the area under the ROC and PR curves, computing 95% confidence intervals using 1,000 bootstrapped samples on the test set.

4.3 Baselines for Comparison

In addition to the relaxed weight sharing approaches described in the Section 3, we considered a number of baselines, described here.


We considered a standard LSTM in which parameters are completely shared across time. Synthetic tests used the default Pytorch v0.4.1 implementation (

torch.nn.LSTM()). In our experiments on the clinical data, we implemented an LSTM that employed orthogonal weight initialization and layer normalization, in order to match the settings used in the original HyperLSTM implementation (see below).

LSTM+t. shiftLSTM and mixLSTM intrinsically have an additional signal regarding the current time step (captured through the use of time-specific parameters). In order to test whether this was driving differences in performance, we tested LSTM+t, an LSTM with an additional input feature at every time step, representing the relative temporal position.

LSTM+TE. Given that positional encoding has recently been shown to provide an advantage over simply providing position (Vaswani et al., 2017), we also explored a temporal encoding as additional input. We used a 24-dimensional encoding for each time step. We tested encoding sizes of 12, 24, 36 and 48 on the in-hospital mortality task, and found 24 to result in the best validation performance. We calculated the temporal encoding as: if is even, and if

is odd, where

represents the time step and the position in the encoding indexed from .

HyperLSTM. This approach first proposed by Ha et al. (2017) uses a smaller, auxiliary LSTM to modify the weights of a larger, primary LSTM at each time step. Since the weights at each time step are effectively different, this is a form of relaxed weight sharing. As in the original implementation, we used orthogonal weight initialization and layer normalization. The two networks were trained jointly using backpropagation.

4.4 Model Training

Except in the case of the LSTM applied to synthetic data, all models all consisted of single layer recurrent cells that were orthogonally initialized followed by a fully connected layer and a softmax nonlinearity. To compensate for the lower capacity of the LSTM compared to mixLSTM and shiftLSTM which have multiple cells, we allowed it to use an additional layer. This was done for all experiments involving synthetic data. Capacity was less of an issue in the experiments involving real data. We tuned the size of the hidden state(s) in all methods based on validation performance.

We trained all models using the Adam optimizer (Kingma and Ba, 2015)

(Pytorch implementation) with the default learning rate of 0.001. On synthetic data we aimed to minimize the mean squared error (MSE) loss, and for the clinical prediction tasks, we aimed to minimize the cross entropy loss with target replication. We used early stopping based on validation performance – MSE loss on synthetic data tasks, AUC on real data tasks – with a patience of 5 epochs. Models for synthetic datasets were trained with 40 random initializations/hyperparameter settings for a maximum of 30 epochs. We used a batch size of 100 and performed a random search over hidden state sizes of {100, 150, 300, 500, 700, 900, 1100}. For learning models on clinical tasks, we used a batch size of 8, because it was the optimal LSTM batch size setting used in the MIMIC-III benchmark paper on the in-hospital mortality task

(Harutyunyan et al., 2017). When learning models for ARF and shock, we considered 20 random initializations, and trained for a maximum of 30 epochs. We performed a random search over hidden state and auxiliary hidden states sizes of {25, 50, 75, 100, 125, 150}. When learning a model for in-hospital mortality, we considered 10 random initializations and trained for a maximum of 10 epochs, in part because of the larger training set size. For this task, we performed a a random search over hidden state size {100, 150, 300, 500, 700} and the same auxiliary hidden state size search as for ARF and shock. To facilitate comparisons, upon publication, code for all of our experiments will be made publicly available on Github.

5 Results & Discussion

In this section, we first show that as temporal conditional shift increases, the performance of the LSTM decreases. Next, we provide evidence that suggests that conditional shift exists in the three clinical prediction tasks. Then, on both the synthetic and real datasets, we show that the proposed method consistently outperforms the baselines. Finally, we present a follow-up analysis focusing on the patterns by which mixLSTM learns to mix the weights, and the robustness of mixLSTM when training data are limited.

5.1 Exploring the Effects of Temporal Conditional Shift

Does parameter sharing hinder the ability of an LSTM to capture time-varying relationships? The data generation process described in Section 4.1 allows us to control the amount of temporal conditional shift present in the task. Specifically, by increasing , we increase the variability between two temporally adjacent tasks. This allows us to test the effects of conditional shift on the performance of an LSTM. We hypothesize that because the LSTM shares parameters over time, it will struggle to adapt to temporal conditional shift. To test our hypothesis, we compare the performance of an LSTM with shiftLSTM across a range of values (Figure LABEL:Figure1a). Here, the shiftLSTM approach learns different parameters for each time step (30 in total). We observe a clear trend – as temporal conditional shift increases, the performance of the LSTM decreases. In contrast, shiftLSTM results in steady performance across the range of . At low , the LSTM outperforms the shiftLSTM, in terms of MSE on the test set. In this experiment, we limited the amount of training data to 1,000 samples. Theoretically, given enough training data, LSTM should be capable of accurately modeling time-varying relationships. To verify this, we show that the loss associated with the LSTM approach approaches zero as the training set size increases (Figure LABEL:Figure1b). These results support our initial hypothesis that in settings with limited data, temporal conditional shift negatively impacts LSTM performance and that this impact is in part due to the sharing of parameters.


Figure 2: (a.1): LSTM performance decreases as conditional shift increases. With increasing shift, the time-varying architecture outperforms the LSTM, suggesting that weight sharing hurts LSTM performance. (a.2) mixLSTM bridges the performance tradeoff between LSTM and shiftLSTM. As conditional shift increases, mixLSTM’s ability to relax weight sharing helps it increasingly outperform LSTM. By assuming that tasks are unique but related it outperforms shiftLSTM. (b) This issue is only apparent when training data are limited; LSTMs can adapt to temporal conditional shift given enough training data. Error bars represent 95% confidence intervals based on bootstrapped samples of the test set.

Is there any evidence of time-varying relationships in the three clinical prediction tasks of interest? We tested for the presence of temporal conditional shift in three clinical prediction tasks: ARF, shock, and in-hospital mortality. For these tasks, the underlying parameters that govern the amount of temporal conditional shift (e.g., ) are unknown. Instead, we indirectly measure temporal conditional shift by applying shiftLSTM- varying from , where is a standard LSTM, and implies a new set of parameters for each time step. Increasing the number of cells or reduces sharing. As the difference between sequential tasks increases, we expect the benefit of learning different LSTM cells to increase. Empirically, we observe that less parameter sharing results in better performance (Figure LABEL:Figure2). This supports our hypothesis that architectures for solving clinical prediction tasks could benefit from relaxed weight sharing.


Figure 3: As we increase the number of independent cells in shiftLSTM-, we reduce the amount of parameter sharing and effectively reduce the time period over which any one cell makes predictions. As parameter sharing decreases, there is an increase in performance supporting our hypothesis that temporal conditional shift is present in our real data tasks. Error bars represent the IQR based on bootstrapped samples of the test set.

5.2 Comparing the Proposed Approach to Baselines

In this section, we explore the performance of the proposed approach, mixLSTM, relative to the other baselines. Again, we hypothesize that it will outperform the other approaches due to a) smooth sharing of weights and b) the ability to learn which cells to share. mixLSTM strikes a balance between complete weight sharing (LSTM) and no weight sharing (shiftLSTM-). In addition, compared to shiftLSTM, mixLSTM can share weights between distant time steps and the learns how to accomplish this.

How does the proposed approach perform on synthetic data? mixLSTM

has the ability to continuously interpolate between

independent cell parameters, but has 15 times fewer parameters relative to shiftLSTM. On the synthetic data tasks, mixLSTM consistently outperforms shiftLSTM at all levels of temporal shift (Figure LABEL:Figure1a). Moreover, mixLSTM outperforms LSTM at low , except when no temporal shift exist. This agrees with our intuition that smart sharing is better than not sharing (shiftLSTM) and indiscriminate sharing (LSTM).

ARF shock mortality
Model (n=549) (n=786) (n=3,236)
LSTM 0.47 [0.35, 0.58] 0.04 [0.02, 0.07] 0.59 [0.49, 0.69] 0.09 [0.05, 0.16] 0.80 [0.78, 0.83] 0.39 [0.33, 0.43]
LSTM+t 0.42 [0.30, 0.54] 0.04 [0.02, 0.07] 0.62 [0.53, 0.70] 0.08 [0.05, 0.15] 0.81 [0.79, 0.83] 0.41 [0.36, 0.47]
LSTM+TE 0.48 [0.35, 0.61] 0.05 [0.03, 0.10] 0.60 [0.50, 0.69] 0.10 [0.06, 0.20] 0.82 [0.80, 0.85] 0.43 [0.38, 0.48]
HyperLSTM 0.57 [0.44, 0.68] 0.06 [0.03, 0.10] 0.63 [0.54, 0.72] 0.08 [0.05, 0.12] 0.82 [0.80, 0.84] 0.42 [0.37, 0.47]
shiftLSTM 0.61 [0.49, 0.70] 0.10 [0.03, 0.21] 0.61 [0.52, 0.70] 0.09 [0.05, 0.16] 0.81 [0.79, 0.84] 0.43 [0.37, 0.48]
mixLSTM [0.62, 0.80] [0.06, 0.27] [0.58, 0.76] [0.06, 0.16] [0.81, 0.85] [0.40, 0.50]
Table 2: Performance on ARF, shock, & mortality with 95% confidence intervals. Though the differences are small, mixLSTM consistently outperforms the other approaches across all tasks in terms of both AUC-ROC and AUC-PR. The number of test samples for each task is reported in parentheses.

How does the proposed approach perform on the clinical prediction tasks? Applied to the three clinical prediction tasks, mixLSTM consistently performs the best (Table 2). Compared to the LSTM baseline, LSTM+t and LSTM+TE performed better, given sufficient training data, suggesting that having direct access to time either as a feature or a temporal encoding is beneficial. Relaxing weight sharing further improves performance. As shown earlier, shiftLSTM consistently improves performance over the standard LSTM.

HyperLSTM, similar to mixLSTM, bridges the dichotomy of completely shared vs. completely independent weights, and outperforms both LSTM and shiftLSTM in some cases but not consistently. mixLSTM outperforms all other baselines on all three tasks, though the differences are not statistically significant in all cases. Both HyperLSTM and mixLSTM achieve high performance and both models relax weight sharing. This supports our hypothesis that relaxed weight sharing is beneficial in some settings.

In these experiments, we selected for each task based on validation performance, sweeping from to for mixLSTM and testing for shiftLSTM. For shiftLSTM the best for ARF, shock, and mortality respectively. For mixLSTM the best , respectively. For shiftLSTM, represents the optimal number of sequential tasks to segment the input sequence into. For mixLSTM, indicates the optimal number of operational or characteristic modes in the data. It appears that for shiftLSMT, the chosen is correlated with the amount of training data available. Both ARF and shock have significantly smaller training set sizes compared to mortality. In contrast, mixLSTM learns more cells for ARF and shock. This indicates that the structure of mixLSTM is better suited to the problem setting than shiftLSM because it is able to train twice as many cells as shiftLSM and attain a higher test performance. The converse also supports this claim. mixLSTM is able to train the number of cells as shiftLSTM for mortality and still attain better performance. The optimal for mixLSTM appears to be less indicative of training set size, and more a reflection of the true number of operational or characteristic modes in the data. When we visualize the mixing ratios learned by mixLSTM-2 in later sections (Figure 5) we see that while mortality smoothly interpolates between cell1 and cell2 as time passes, ARF and shock both display an initial peak followed by a gradual interpolation. This suggests that the dynamics are more complex for ARF and shock.

5.3 Robustness and Sensitivity Analyses

In this section, we further analyze mixLSTM, focusing on its robustness in settings when training data are limited and investigate what it has learned in terms of mixing trends and changing feature importance.

5.3.1 Performance with Limited Training Data

Does the proposed approach still perform well when training data are limited? We hypothesized that mixLSTM will continue to outperform LSTM, even when training data are limited because mixLSTMs are better suited to problem settings exhibiting temporal conditional shift. To test our hypothesis, we compared the performance of mixLSTM- and LSTM trained using different training set sizes for the task of predicting in-hospital mortality. We chose to focus on the task of in-hospital mortality, since it had the most training data (training set size ). We subsampled the training set repeatedly for . The test set was held constant across all experiments and to limit the capacity of the model. mixLSTM consistently outperforms LSTM across all ranges of training set sizes (Figure 4). As one might expect, differences are subtle at smaller training set sizes, where parameter sharing is most likely to help.

Figure 4: mixLSTM- is consistently better than LSTM at different training set sizes. Error bars represent 95% confidence intervals bootstrapped from the test set.

5.3.2 What has mixLSTM learned?

To dive deeper into what exactly the mixLSTM has learned, we visualize the learned mixing weights and the most important factors.

Are mixLSTM’s learned mixing weights smooth? In our learning objective function, mixLSTM’s mixing weights are not constrained to be smooth. However, we hypothesize that this behavior reflects the underlying dynamics in clinical data. Figure 5 plots the mixing weights () over time for mixLSTM-2 on the three clinical prediction tasks. Since there are only two independent cells (), we can infer . The trend indicates that one cell captures the dynamics associated with the beginning of a patient’s stay, while the second cell captures the dynamics 48 hours into the stay.

Figure 5: Visualization of the mixing weights learned by mixLSTM-2. is shown on the y axis. can be inferred (). Although not constrained to be smooth, we observe a smooth transition of mixing weights between time steps indicative of one cell that is specialized for the beginning of an ICU stay while the other is specialized for 48 hours into a patient’s ICU stay.

What time-varying relationships does the mixLSTM learn to recognize? When attempting to understand which features drive a model’s predictions, the focus is often put on the importance of certain features. However, because mixLSTM was designed to and has been shown to excel in situations with temporal conditional shift, we focus on identifying the features whose influence changes over time. To identify such features, we must first measure the effect of each feature at each time step. Here, we use the input gradient as a proxy for feature importance and visualize importance over time (Van Hasselt et al., 2016; Selvaraju et al., 2016; Graves, 2012) (Figure LABEL:fig:saliency). More specifically, we traversed the test set, accumulating the input gradient with respect to the target class. One of the most noticeable patterns is the large amount of variation in feature importance in the first 6 hours of an ICU admission. This pattern is most apparent for the task of predicting shock (Figure LABEL:fig:saliencyShock). This may reflect the significant physiological changes a patient may experience at the beginning of their ICU stay as interventions are administered in an effort to stabilize them.


Figure 6: Input gradient based saliency map of mixLSTM- on three tasks. Each plot shows a proxy of importance of each feature across time steps. Some noticeable temporal patterns include high variability during the first six hours, which may be a reflection of increased physiological change a patient may experience at the beginning of their ICU stay when interventions are more frequent.
ARF shock mortality
pH Respiratory rate Respiratory rate
Oxygen saturation Height Heart Rate
Weight Mean blood pressure Glucose
Respiratory rate Heart Rate Fraction inspired oxygen
Fraction inspired oxygen Fraction inspired oxygen Height
Heart Rate pH Weight
Height Weight Systolic blood pressure
Glucose Glucose pH
Systolic blood pressure Oxygen saturation Mean blood pressure
Mean blood pressure Systolic blood pressure Diastolic blood pressure
Temperature Diastolic blood pressure Oxygen saturation
Diastolic blood pressure Temperature Temperature
Table 3: Physiological data ranked by overall importance as identified by mixLSTM- on ARF, shock, and mortality using input gradient. The table is color coded. Pink denotes features that are initially risk factors, where risk decreases over time. Red denotes features that are initially risk factors, but where risk increases over time. Light green denotes features that are initially protective, but become less protective over time. Dark green denotes features that are initially protective, and becomes more protective over time.

We list the continuous features ranked by importance in Table 3. Feature importance was calculated by summing importance over time and taking the absolute value. Here positive importance values are associated with increased risk, while negative importance values are associated with protection. The color scheme reflects the overall direction of association and the change over time. Dark red and dark green represent ‘risk’ and ‘protective’ factors that lead to increased and decreased risk over time, respectively. That is their effects become amplified over time. Light red and light green represent ‘risk’ and ‘protective’ factors that lead to decreased and increased risk over time, respectively. That is their effects diminish over time. For example, in all three tasks ‘fraction of inspired oxygen,’ representing whether or not a patient is on supplemental oxygen, is a risk factor initially, and becomes more important over time. This suggests that if a patient is still on high levels of oxygen 48 hours into their ICU admission, their risk is elevated for all three outcomes. For ARF and shock a similar pattern holds for heart rate, where sustained high heart rate results in greater risk over time. This suggests that some features, when persistently abnormal, further amplify a patient’s risk.

It is important to note that interpreting neural networks, and LSTMs in particular, remains an open challenge. Though the approach considered here is frequently used for interpreting LSTMs, it relies on the local effect of a feature and thus ignores the global trends (Ross et al., 2017; Ghorbani et al., 2019; Graves, 2012). Moreover, these methods merely identify associations and not causation.

Given the limitations of using the input gradient to model the importance of discrete features, we also investigated feature importance using a permutation based sensitivity analysis (Fisher et al., 2018; Breiman, 2001). In the test set, we randomly permuted each covariate at each specific time period and measured predictive performance. By permuting each covariate in turn, we destroy any information that a particular covariate provides. If performance then drops significantly relative to a non-permuted baseline, we conclude the feature was important. To prevent correlated variables from leaking information, we simultaneously permuted variables with a correlation coefficient . We permuted grouped features within periods of 12 hours to encourage consistency of perturbation along time. Figure LABEL:fig:perm_saliency plots this measure of feature importance over time. Overall, we observe similar trends to the input gradient analysis. In addition to there being greater variability in the first part of the visit, we also observed significant changes in the importance of certain features (measured by sum of importance across time). For example, for the task of predicting in-hospital mortality, respiratory rate is initially the most important feature, but then temperature becomes more important as the patient state evolves. For ARF, a variable pertaining to the Glasgow coma scale is initially most important, before yielding to respiratory rate.


Figure 7: Permutation based saliency map of mixLSTM- on three tasks. Each plot shows AUC degradation for permuting a feature. That is the more positive the change, the more important the feature affect the output. Some noticeable temporal patterns include an increased variability during the first 12 hours which may be a reflection of increased physiological change a patient may experience at the beginning of their ICU stay when interventions are more frequent.

6 Conclusion

In this work, we present and explore the issue of temporal conditional shift in clinical time-series data. In addition, we propose a mixture of LSTM model (mixLSTM) and demonstrate that it effectively adapts to scenarios exhibiting temporal conditional shift, consistently outperforming baselines on synthetic and clinical data tasks. We also show that the mixLSTM model can adapt to settings with limited training data and learns meaningful, time-varying relationships from the data.

While mixLSTM achieves consistently better performance on all tasks considered, we note some important limitations. First, we only considered fixed-length datasets. It would be beneficial to compare LSTM and mixLSTM’s ability to generalize to variable length data. Second, our features are largely physiological (e.g., HR). We hypothesize that other types of features such as medications may exhibit stronger time-varying relationships. Third, while it is reasonable to set time zero as the time of ICU admission, patients are admitted to the ICU at different points during their natural history of their illness. Future work should consider the alignment of patient time steps (e.g., learning an individualized model). Lastly, our current implementation of mixLSTM dynamically creates a new cell at each time step, resulting in an inefficient use of memory.

Despite these limitations, our results suggest that temporal conditional shift is an important aspect of clinical time-series prediction and future work could benefit from considering this problem setting. Our proposed mixLSTM presents a strong starting point from which future work can build.


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Appendix A. Details of Data & Features

Index Variable Name Table(s) ITEMID(s)
1 Capillary refill rate CHARTEVENTS 3348, 115, 8377
2 Diastolic blood pressure CHARTEVENTS 8368, 220051, 225310, 8555, 8441, 220180, 8502, 8440, 8503, 8504, 8507, 8506, 224643
3 Fraction inspired oxygen CHARTEVENTS 3420, 223835, 3422, 189, 727
4 Glascow coma scale eye opening CHARTEVENTS 184, 220739
5 Glascow coma scale motor response CHARTEVENTS 454, 223901
6 Glascow coma scale total CHARTEVENTS 198,
7 Glascow coma scale verbal response CHARTEVENTS 723, 223900
8 Glucose CHARTEVENTS/LABEVENTS 50931, 807, 811, 1529, 50809, 51478, 3745, 225664, 220621, 226537
9 Heart Rate CHARTEVENTS 221, 220045
10 Height CHARTEVENTS 226707, 226730, 1394
11 Mean blood pressure CHARTEVENTS 52, 220052, 225312, 224, 6702, 224322, 456, 220181, 3312, 3314, 3316, 3322, 3320
12 Oxygen saturation CHARTEVENTS/LABEVENTS 834, 50817, 8498, 220227, 646, 220277
13 Respiratory rate CHARTEVENTS 618, 220210, 3603, 224689, 614, 651, 224422, 615, 224690
14 Systolic blood pressure CHARTEVENTS 51, 220050, 225309, 6701, 455, 220179, 3313, 3315, 442, 3317, 3323, 3321, 224167, 227243
15 Temperature CHARTEVENTS 3655, 677, 676, 223762, 3654, 678, 223761, 679
16 Weight CHARTEVENTS 763, 224639, 226512, 3580, 3693, 3581, 226531, 3582
17 pH CHARTEVENTS/LABEVENTS 50820, 51491, 3839, 1673, 50831, 51094, 780, 1126, 223830, 4753, 4202, 860, 220274
Table 4: The 17 physiological features extracted from MIMIC-III database, the source tables, and the corresponding ITEMIDs
Index Feature Name Type
0 Capillary refill rate->0.0 Binary
1 Capillary refill rate->1.0 Binary
2 Diastolic blood pressure Numeric
3 Fraction inspired oxygen Numeric
4 Glascow coma scale eye opening->To Pain Binary
5 Glascow coma scale eye opening->3 To speech Binary
6 Glascow coma scale eye opening->1 No Response Binary
7 Glascow coma scale eye opening->4 Spontaneously Binary
8 Glascow coma scale eye opening->None Binary
9 Glascow coma scale eye opening->To Speech Binary
10 Glascow coma scale eye opening->Spontaneously Binary
11 Glascow coma scale eye opening->2 To pain Binary
12 Glascow coma scale motor response->1 No Response Binary
13 Glascow coma scale motor response->3 Abnorm flexion Binary
14 Glascow coma scale motor response->Abnormal extension Binary
15 Glascow coma scale motor response->No response Binary
16 Glascow coma scale motor response->4 Flex-withdraws Binary
17 Glascow coma scale motor response->Localizes Pain Binary
18 Glascow coma scale motor response->Flex-withdraws Binary
19 Glascow coma scale motor response->Obeys Commands Binary
20 Glascow coma scale motor response->Abnormal Flexion Binary
21 Glascow coma scale motor response->6 Obeys Commands Binary
22 Glascow coma scale motor response->5 Localizes Pain Binary
23 Glascow coma scale motor response->2 Abnorm extensn Binary
24 Glascow coma scale total->11 Binary
25 Glascow coma scale total->10 Binary
26 Glascow coma scale total->13 Binary
27 Glascow coma scale total->12 Binary
28 Glascow coma scale total->15 Binary
29 Glascow coma scale total->14 Binary
30 Glascow coma scale total->3 Binary
31 Glascow coma scale total->5 Binary
32 Glascow coma scale total->4 Binary
33 Glascow coma scale total->7 Binary
34 Glascow coma scale total->6 Binary
35 Glascow coma scale total->9 Binary
36 Glascow coma scale total->8 Binary
37 Glascow coma scale verbal response->1 No Response Binary
38 Glascow coma scale verbal response->No Response Binary
39 Glascow coma scale verbal response->Confused Binary
40 Glascow coma scale verbal response->Inappropriate Words Binary
41 Glascow coma scale verbal response->Oriented Binary
42 Glascow coma scale verbal response->No Response-ETT Binary
43 Glascow coma scale verbal response->5 Oriented Binary
44 Glascow coma scale verbal response->Incomprehensible sounds Binary
45 Glascow coma scale verbal response->1.0 ET/Trach Binary
46 Glascow coma scale verbal response->4 Confused Binary
47 Glascow coma scale verbal response->2 Incomp sounds Binary
48 Glascow coma scale verbal response->3 Inapprop words Binary
49 Glucose Numeric
50 Heart Rate Numeric
51 Height Numeric
52 Mean blood pressure Numeric
53 Oxygen saturation Numeric
54 Respiratory rate Numeric
55 Systolic blood pressure Numeric
56 Temperature Numeric
57 Weight Numeric
58 pH Numeric
59 mask->Capillary refill rate Binary
60 mask->Diastolic blood pressure Binary
61 mask->Fraction inspired oxygen Binary
62 mask->Glascow coma scale eye opening Binary
63 mask->Glascow coma scale motor response Binary
64 mask->Glascow coma scale total Binary
65 mask->Glascow coma scale verbal response Binary
66 mask->Glucose Binary
67 mask->Heart Rate Binary
68 mask->Height Binary
69 mask->Mean blood pressure Binary
70 mask->Oxygen saturation Binary
71 mask->Respiratory rate Binary
72 mask->Systolic blood pressure Binary
73 mask->Temperature Binary
74 mask->Weight Binary
75 mask->pH Binary
Table 5: The 76 time-series features used as input to all the models.