Modeling Missing Data in Clinical Time Series with RNNs (1606.04130v5)

Abstract: We demonstrate a simple strategy to cope with missing data in sequential inputs, addressing the task of multilabel classification of diagnoses given clinical time series. Collected from the pediatric intensive care unit (PICU) at Children's Hospital Los Angeles, our data consists of multivariate time series of observations. The measurements are irregularly spaced, leading to missingness patterns in temporally discretized sequences. While these artifacts are typically handled by imputation, we achieve superior predictive performance by treating the artifacts as features. Unlike linear models, recurrent neural networks can realize this improvement using only simple binary indicators of missingness. For linear models, we show an alternative strategy to capture this signal. Training models on missingness patterns only, we show that for some diseases, what tests are run can be as predictive as the results themselves.

• The paper demonstrates that incorporating missing data indicators in LSTM RNNs enhances diagnostic prediction accuracy in clinical time series.
• The study compares imputation strategies, showing that zero-imputed data combined with missingness indicators achieves superior AUC and F1 scores.
• The research underscores the value of treating data absences as informative signals, potentially advancing real-time clinical decision support.

Modeling Missing Data in Clinical Time Series with RNNs

The paper presented by Lipton, Kale, and Wetzel addresses the prevalent issue of missing data in clinical time series, particularly focusing on data collected from the pediatric intensive care unit (PICU) at Children's Hospital Los Angeles. The authors propose a novel methodology that leverages Recurrent Neural Networks (RNNs) to enhance prediction in multilabel classification tasks by treating missing data indicators as features rather than resorting solely to traditional imputation methods.

Methodological Framework

The paper focuses on the challenge posed by the irregular timing of clinical measurements, which often results in incomplete data sequences. Typically, such gaps are filled through imputation. The authors, however, challenge this approach by incorporating missingness itself as a feature, positing that the absence of data can encapsulate valuable information about patient conditions and the decision-making processes of healthcare providers.

The authors employ Long Short-Term Memory (LSTM) RNNs, known for their capability to handle sequence prediction tasks effectively, due to their ability to learn temporal dependencies. The novelty lies in the augmentation of input data with binary indicator variables that signal the presence of missing data. These indicators allow RNNs to interpret the absence of data points dynamically, capitalizing on the potential implicit information conveyed by such absences.

Experimental Design and Results

The research utilizes a dataset comprised of over 10,000 PICU episodes, encoded as multivariate time series with 13 standardized variables. These time series undergo preprocessing to transform them into discrete sequences aligned to hourly intervals. The paper experiments with various data handling strategies: simple forward-filling and zero imputation, alongside missing data indicators, both singularly and combined.

The results indicate a superior performance of RNNs when missingness indicators are aligned with zero-imputed values, achieving higher AUCs and F1 scores than linear baselines and multilayer perceptrons (MLPs). This finding reinforces the hypothesis that missing data, when modeled explicitly, can improve predictive accuracy for certain diagnoses.

Implications and Future Directions

The paper's findings underscore the potential to enhance diagnostic models by treating missing data as an informative signal rather than merely noise to be imputed. This approach recognizes the non-random nature of missing data in clinical contexts, reflecting decisions made by caregivers which can inform patient condition assessments.

However, the authors also raise concerns about the potential ethical and practical issues such a methodology might introduce. As predictive models become integrated into clinical practice, reliance on patterns of treatment—as inferred by missingness—could inadvertently shift medical protocols, enhancing the need for careful validation and monitoring post-deployment.

The work opens avenues for future research focusing on real-time predictive tasks like sepsis detection and trajectory modeling in clinical care. Additionally, exploration into confidence-rated predictions could pave the way for more integrated clinical decision support tools that go beyond binary predictions to provide nuanced advisories.

In conclusion, Lipton, Kale, and Wetzel propel the field towards a more refined understanding and utilization of clinical time series by reimagining missing data not as a mere gap in information, but as a valuable feature in predictive modeling.