# Autoencoder-based prediction of ICU clinical codes

arXiv: https://arxiv.org/abs/2305.04992 · HTML: https://mlsanity.com/paper/2305.04992/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 8 May 2023.

## Builds on (most cited first, 7 of 7)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Semi-Supervised Learning with Deep Generative Models](https://mlsanity.com/paper/1406.5298/) (2014)
- [Time2Vec: Learning a Vector Representation of Time](https://mlsanity.com/paper/1907.05321/) (2019)
- [Multimodal Machine Learning for Automated ICD Coding](https://mlsanity.com/paper/1810.13348/) (2018)
- [Multi-Modal Adversarial Autoencoders for Recommendations of Citations and Subject Labels](https://mlsanity.com/paper/1907.12366/) (2019)
- [Dynamically Extracting Outcome-Specific Problem Lists from Clinical Notes with Guided Multi-Headed Attention](https://mlsanity.com/paper/2008.01197/) (2020)
- [Recommendations for Item Set Completion: On the Semantics of Item Co-Occurrence With Data Sparsity, Input Size, and Input Modalities](https://mlsanity.com/paper/2105.04376/) (2021)

## Abstract (arXiv)

Availability of diagnostic codes in Electronic Health Records (EHRs) is crucial for patient care as well as reimbursement purposes. However, entering them in the EHR is tedious, and some clinical codes may be overlooked. Given an in-complete list of clinical codes, we investigate the performance of ML methods on predicting the complete ones, and assess the added predictive value of including other clinical patient data in this task. We used the MIMIC-III dataset and frame the task of completing the clinical codes as a recommendation problem. We con-sider various autoencoder approaches plus two strong baselines; item co-occurrence and Singular Value Decomposition (SVD). Inputs are 1) a record's known clinical codes, 2) the codes plus variables. The co-occurrence-based ap-proach performed slightly better (F1 score=0.26, Mean Average Precision [MAP]=0.19) than the SVD (F1=0.24, MAP=0.18). However, the adversarial autoencoder achieved the best performance when using the codes plus variables (F1=0.32, MAP=0.25). Adversarial autoencoders performed best in terms of F1 and were equal to vanilla and denoising autoencoders in term of MAP. Using clinical variables in addition to the incomplete codes list, improves the predictive performance of the models.