mlsanityData to 7 Oct 2026

Continuous Predictive Modeling of Clinical Notes and ICD Codes in Patient Health Records

arXiv 2405.11622 ↗First posted 19 May 2024cs.CL cs.LGcited by 1

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Abstract (arXiv)

Electronic Health Records (EHR) serve as a valuable source of patient information, offering insights into medical histories, treatments, and outcomes. Previous research has developed systems for detecting applicable ICD codes that should be assigned while writing a given EHR document, mainly focusing on discharge summaries written at the end of a hospital stay. In this work, we investigate the potential of predicting these codes for the whole patient stay at different time points during their stay, even before they are officially assigned by clinicians. The development of methods to predict diagnoses and treatments earlier in advance could open opportunities for predictive medicine, such as identifying disease risks sooner, suggesting treatments, and optimizing resource allocation. Our experiments show that predictions regarding final ICD codes can be made already two days after admission and we propose a custom model that improves performance on this early prediction task.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 28 references that are in the map. First posted on arXiv 19 May 2024.

Builds on

15 of 28 references.

References · 15

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  7. 2019ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Networkcited by 73
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  12. 2023MDACE: MIMIC Documents Annotated with Code Evidencecited by 19
3 more
  1. 2023Modelling Temporal Document Sequences for Clinical ICD Codingcited by 7
  2. 2022An Automatic ICD Coding Network Using Partition-Based Label Attentioncited by 2
  3. 2023Echocardiography Segmentation Using Neural ODE-based Diffeomorphic Registration Fieldcited by 2

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Predictive Multimodal Modeling of Diagnoses and Treatments in EHRcited by 0