Autoencoder-based prediction of ICU clinical codes

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

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 incomplete 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 consider 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 approach 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.

Original languageEnglish
Title of host publicationArtificial Intelligence in Medicine - 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Proceedings
EditorsJose M. Juarez, Mar Marcos, Gregor Stiglic, Allan Tucker
PublisherSpringer
Pages57-62
Number of pages6
Volume13897
ISBN (Electronic)9783031343445
ISBN (Print)9783031343438
DOIs
Publication statusPublished - 5 Jun 2023
Event21st International Conference on Artificial Intelligence in Medicine, AIME 2023 - Portoroz, Slovenia
Duration: 12 Jun 202315 Jun 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13897 LNAI

Conference

Conference21st International Conference on Artificial Intelligence in Medicine, AIME 2023
Country/TerritorySlovenia
CityPortoroz
Period12/06/202315/06/2023

Keywords

  • Autoencoders
  • Medical codes
  • Prediction
  • Recommender Systems

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