Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record

By Jinghe Zhang, Kamran Kowsari, James H. Harrison, Jennifer M. Lobo, Laura E. Barnes. 2018

150 citations recorded by the source corpus.

The wide implementation of electronic health record (EHR) systems facilitates the collection of large-scale health data from real clinical settings. Despite the significant increase in adoption of EHR systems, these data remain largely unexplored, but present a rich data source for knowledge discovery from patient health histories in tasks, such as understanding disease correlations and predicting health outcomes. However, the heterogeneity, sparsity, noise, and bias in these data present many complex challenges. This complexity makes it difficult to translate potentially relevant information into machine learning algorithms. In this paper, we propose a computational framework, Patient2Vec, to learn an interpretable deep representation of longitudinal EHR data, which is personalized for each patient. To evaluate this approach, we apply it to the prediction of future hospitalizations using real EHR data and compare its predictive performance with baseline methods. Patient2Vec produces a vector space with meaningful structure, and it achieves an area under curve around 0.799, outperforming baseline methods. In the end, the learned feature importance can be visualized and interpreted at both the individual and population levels to bring clinical insights.

Subfields: Artificial Intelligence, Health Information Management

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

1. Ala Al-Fuqaha
2. Haopeng Zhang
3. Jiawei Zhang
4. Junaid Qadir
5. Khansa Rasheed
6. Mohamed Ghaly
7. Adeel Razi
8. Laura E. Barnes

In Health Information Management

1. Ala Al-Fuqaha
2. Junaid Qadir
3. Jun Xia
4. Guang Yang
5. Qinghao Ye
6. Adnan Qayyum

Citations in the corpus

• Text Classification Algorithms: A Survey (2019, background): “introduced Patient2Vec to learn an interpretable deep representation of longitudinal electronic health record (EHR) data which is personalized for each patient [246].”
• Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2021, background): “Jinghe et al. [122] presented P2V to explain the unexplored EHR dataset for predicting disease correlation, health outcome, and health history of new patients.”
• Bi-LSTM Model to Increase Accuracy in Text Classification: Combining Word2vec CNN and Attention Mechanism (2020, methodology): “Recently, text classification has been applied in specialized fields such as sentiment analysis and clinical [33–35], law [36,37], healthcare [38–40], and business marketing [41,42].”
• Text Graph Transformer for Document Classification (2020, background): “Text classification is a widely studied problem in natural language processing and has been addressed in many real-world applications such as news filtering, spam detection, and health record systems (Kowsari et al., 2019; Che et al., 2015; Zhang et al., 2018).”
• Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond (2021, methodology): “Zhang et al. [78] also built a method to learn how to represent EHR data that could document the relationship between clinical outcomes within each patient.”