Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time
By Tian Bai, Shanshan Zhang, Brian L. Egleston, Slobodan Vucetic. 2018
200 citations recorded by the source corpus.
Various deep learning models have recently been applied to predictive modeling of Electronic Health Records (EHR). In medical claims data, which is a particular type of EHR data, each patient is represented as a sequence of temporally ordered irregularly sampled visits to health providers, where each visit is recorded as an unordered set of medical codes specifying patient's diagnosis and treatment provided during the visit. Based on the observation that different patient conditions have different temporal progression patterns, in this paper we propose a novel interpretable deep learning model, called Timeline. The main novelty of Timeline is that it has a mechanism that learns time decay factors for every medical code. This allows the Timeline to learn that chronic conditions have a longer lasting impact on future visits than acute conditions. Timeline also has an attention mechanism that improves vector embeddings of visits. By analyzing the attention weights and disease progression functions of Timeline, it is possible to interpret the predictions and understand how risks of future visits change over time. We evaluated Timeline on two large-scale real world data sets. The specific task was to predict what is the primary diagnosis category for the next hospital visit given previous visits. Our results show that Timeline has higher accuracy than the state of the art deep learning models based on RNN. In addition, we demonstrate that time decay factors and attentions learned by Timeline are in accord with the medical knowledge and that Timeline can provide a useful insight into its predictions.
Subfields: Artificial Intelligence, Health Information Management, Representation & Self-Supervised Learning, Medical & Health AI
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Cecilia Panigutti
- Dino Pedreschi
- Alan Perotti
- Salvatore Ruggieri
- Sara Hajian
- Faisal Kamiran
- Andrea Romei
- Indrė Žliobaitė
In Representation & Self-Supervised Learning
In Medical & Health AI
Citations in the corpus
- Doctor XAI: an ontology-based approach to black-box sequential data classification explanations (2020, background): “Many interpretability approaches related to sequential data modeling focus on adding an attention mechanism [2, 44] to a sequential model and use the attention weights as a form of explanation [3, 11, 33, 47], however recent works have highlighted how this kind of explanation might lack consistency ”