Question: However, given the capacity of deep neural networks and the ease of learning convex clusters, why would the deep learning method be at a disadvantage?
Evidence: EHR data comprise complex time-series data, being high-dimensional, multi-modal and heterogeneous, and thus presenting challenges when used in machine learning models;). An important goal in a medical setting is to identify phenotypically separable clusters with distinct phenotypic profiles (which we denote as phenotypic clustering hereafter). For the purpose of this work, cluster phenotypes result from the combination of two distinct components: a) the evolution profile of patient trajectories' within the cluster, and b) the characterisation of the cluster with regards to clinical variables of interest. The latter may include features not used for clustering and may provide information about the underlying or future health status.
Traditional clustering models such as K-Means or hierarchical clustering have been shown to fail to capture the existing time-dependent feature relationships. As such, variants have been proposed to mitigate this problem. A temporal version of the K-Means algorithm, Time-Series K-Means (TSKM,), models the distance between time-series of different datapoints, using the Euclidean distance (which is equivalent to considering all temporal observations as an independent feature value for the corresponding patient admission), or time-series alignment strategies such as Dynamic-Time Warping) and soft-DTW, to infer temporal evolution within the latent space. Clustering is performed in the low dimensional latent space through the use of self-organising maps to obtain a discrete, topologically-interpretable latent representation of the learnt clusters. In a supervised setting, AC-TPC serves as the current state-of-the-art for identifying phenotypically separable clusters in patient trajectories in EHR data. AC-TPC maps EHR data into a latent space via an encoder, and uses an actor-critic network which leverages clinical outcomes to aid in cluster formation and obtaining cluster phenotypes. Neither SOM-VAE and AC-TPC provide clinically meaningful interpretation of feature-time importance or outcome of interest.
Attention mechanisms have recently been proposed to provide greater interpretability to Recurrent Neural Networks (RNN) and to aid in dealing with long-term dependencies, and have also been used in modelling EHR time-series. RETAIN proposes a two-level reverse attention mechanism to mimic physician's decision process and predict a future diagnosis. In other recent works, attention mechanisms based on bi-directional RNN and CNN outperformed standard classification models in predicting high risk vascular diseases with the addition of medication information as input data. A drawback of such attention mechanisms is the focus on temporal interpretability only, and inability to look at individual features, which is key in a medical setting. After processing, input data contained over 100,000 unique patient trajectories corresponding to 4,266 unique patient admissions (only last patients admissions were considered in our analysis).
Original trajectories for the patient cohort are shown in the Appendix in for different variables/features. A lack of clear outcome group separability can be observed across temporal and static variables. Furthermore, we note the high degree of imbalance in the dataadmissions with no event account for over 86.8% of the total number of admissions, while event classes correspond to 10.3% Death, 1.8% ICU and 1.1% Cardiac.
For the purpose of this work, cluster phenotypes result from the combination of two distinct components: a) the evolution profile of patient trajectories’ within the cluster, and b) the character_x005f_x0002_isation of the cluster with regards to clinical variables of interest. The latter may include features not used for clustering and may provide information about the underlying or future health status.
Answer: While the TSKM Baseline performs better when considering solely clustering metrics, we argue that the clusters learnt by TSKM are less relevant to our overall prediction task as well as performing worse at identifying separable cluster phenotypes.
We clarify the definition of “phenotype” in Section 2 (highlighted in yellow) – a cluster phenotype is a combination of a) trajectory evolution profile, and b) characterisation with regards to an outcome of interest. In our setting, four patient outcomes were considered, based on admission events (or discharge). Outcomes are unknown for new admissions, yet they are ultimately what hospital clinicians are interested in identifying.
TSKM Clusters are very hard to distinguish with regards to clinical outcome prediction. This can be seen in Table 4, where TSKM performs much worse than CAMELOT (0.55 AUROC for TSKM vs 0.73 AUROC). Furthermore, cluster trajectories learnt by TSKM are less separable with regards to trajectory evolution. We have added to the Appendix Figures A10 and A11 to illustrate this phenomenon, and to justify how pure standard clustering metrics, while useful, might fail to identify relevant feature trends in the input trajectories.