Attend and Diagnose: Clinical Time Series Analysis using Attention Models (1711.03905v2)

Abstract: With widespread adoption of electronic health records, there is an increased emphasis for predictive models that can effectively deal with clinical time-series data. Powered by Recurrent Neural Network (RNN) architectures with Long Short-Term Memory (LSTM) units, deep neural networks have achieved state-of-the-art results in several clinical prediction tasks. Despite the success of RNNs, its sequential nature prohibits parallelized computing, thus making it inefficient particularly when processing long sequences. Recently, architectures which are based solely on attention mechanisms have shown remarkable success in transduction tasks in NLP, while being computationally superior. In this paper, for the first time, we utilize attention models for clinical time-series modeling, thereby dispensing recurrence entirely. We develop the \textit{SAnD} (Simply Attend and Diagnose) architecture, which employs a masked, self-attention mechanism, and uses positional encoding and dense interpolation strategies for incorporating temporal order. Furthermore, we develop a multi-task variant of \textit{SAnD} to jointly infer models with multiple diagnosis tasks. Using the recent MIMIC-III benchmark datasets, we demonstrate that the proposed approach achieves state-of-the-art performance in all tasks, outperforming LSTM models and classical baselines with hand-engineered features.

• The paper presents the SAnD architecture, which uses masked self-attention to effectively capture dependencies in clinical time series data.
• The paper bypasses traditional RNN limitations by leveraging parallel processing, achieving superior performance in tasks like mortality prediction and length of stay forecasting.
• The paper demonstrates enhanced computational efficiency and accuracy on the MIMIC-III dataset, highlighting its potential for real-time clinical decision support.

Overview of "Attend and Diagnose: Attention Models for Clinical Time Series Analysis"

The paper "Attend and Diagnose: Clinical Time Series Analysis using Attention Models" introduces an innovative approach for analyzing clinical time-series data utilizing attention mechanisms, bypassing traditional recurrent neural network (RNN) architectures. This paper is particularly relevant given the proliferation of electronic health records (EHR) and the resultant data-rich environments, necessitating advanced methodologies for predictive clinical data analysis. The authors present the SAnD (Simply Attend and Diagnose) architecture, which employs masked self-attention mechanisms to model the dependencies within clinical sequences, providing an alternative to state-of-the-art LSTM-based RNNs without the hindrance of sequential computation.

Research Context and Motivation

The paper highlights the limitations of RNNs, particularly Long Short-Term Memory (LSTM) units, in handling long sequences due to their sequential nature, which restricts parallel computation and hampers efficiency. In contrast, transformer architectures, leveraging attention mechanisms without recurrence, have demonstrated significant advantages in computational efficiency and performance for sequence-related tasks in NLP. This paper explores their application in the domain of clinical time-series for the first time, seeking improvements over traditional RNN-based methods.

Methodology

The authors design the SAnD architecture around several key components:

• Masked Self-Attention Mechanism: By capturing dependencies within a sequence, this mechanism applies masking to ensure causality is preserved.
• Positional Encoding and Dense Interpolation: These strategies are employed to integrate temporal order into the sequence representation, countering the absence of sequential modeling found in RNNs.
• Multi-Task Learning: The SAnD architecture also includes a multi-task variant, which performs joint modeling of multiple clinical diagnosis tasks, leveraging the shared structure to enhance performance across all tasks.

Empirical Results

Extensive evaluations were conducted on the MIMIC-III dataset, which includes a diverse selection of clinical tasks such as mortality prediction, physiologic decompensation detection, length of stay forecasting, and disease phenotyping. Across all tasks, SAnD either met or exceeded the performance of LSTMs with evidence of superior computational efficiency. For instance, in the case of physiologic decompensation, SAnD achieved commendable results, outperforming LSTMs based on metrics such as AUPRC.

Contributions and Implications

The SAnD framework marks a significant contribution in adapting attention-based models, specifically transformers, to the healthcare domain. This paper challenges the prevailing prominence of RNNs in clinical data analysis by illustrating that attention models can capture sequence dependencies more efficiently and accurately. The implication is profound, suggesting potential improvements in real-time clinical decision support systems where computational efficiency and accuracy are paramount.

Future Directions

The paper opens numerous avenues for future research, including exploring further optimization of attention mechanisms in clinical contexts, developing more nuanced hybrid architectures that blend attention with existing models, and extending this framework to other domains characterized by complex time-series data. Given the burgeoning growth in AI and computational healthcare, leveraging attention models may lead to significant advancements in predictive medicine and personalized healthcare solutions.

In conclusion, this paper delivers a comprehensive demonstration of the capabilities of attention mechanisms in clinical time-series analysis, providing a credible alternative to recurrence-based strategies, and underscoring the importance of continued innovation in the intersection of AI and healthcare.