Projects per year
Abstract
Diagnosis assignment is the process of assigning disease codes to patients. Automatic diagnosis assignment has the potential to validate code assignments, correct erroneous codes, and register completion. Previous methods build on text-based techniques utilizing medical notes but are inapplicable in the absence of these notes. We propose using patients' medication data to assign diagnosis codes. We present a proof-of-concept study using medical data from an American dataset (MIMIC-III) and Danish nationwide registers to train a machine-learning-based model that predicts an extensive collection of diagnosis codes for multiple levels of aggregation over a disease hierarchy. We further suggest a specialized loss function designed to utilize the innate hierarchical nature of the disease hierarchy. We evaluate the proposed method on a subset of 567 disease codes. Moreover, we investigate the technique's generalizability and transferability by (1) training and testing models on the same subsets of disease codes over the two medical datasets and (2) training models on the American dataset while evaluating them on the Danish dataset, respectively. Results demonstrate the proposed method can correctly assign diagnosis codes on multiple levels of aggregation from the disease hierarchy over the American dataset with recall 70.0% and precision 69.48% for top-10 assigned codes; thereby being comparable to text-based techniques. Furthermore, the specialized loss function performs consistently better than the non-hierarchical state-of-the-art version. Moreover, results suggest the proposed method is language and dataset-agnostic, with initial indications of transferability over subsets of disease codes.
Translated title of the contribution | Tildeling af diagnosekoder ved hjælp af medicin historik |
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Original language | English |
Article number | 102307 |
Journal | Artificial Intelligence in Medicine |
Volume | 128 |
Issue number | 1 |
Number of pages | 11 |
ISSN | 0933-3657 |
DOIs | |
Publication status | Published - Jun 2022 |
Projects
- 1 Active
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Poul Due Jensen Professorate in Big Data and Artificial Intelligence
Hose, K. (PI), Jendal, T. E. (Project Participant) & Hansen, E. R. (Project Participant)
01/11/2019 → 31/12/2025
Project: Research
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Representing Health Data and Medical Knowledge for Deep Learning
Hansen, E. R., 2023, Aalborg Universitetsforlag. 163 p.Research output: PhD thesis
Open AccessFile121 Downloads (Pure) -
Towards Assigning Diagnosis Codes Using Medication History
Sagi, T., Hansen, E. R., Hose, K., Lip, G. Y. H., Larsen, T. B. & Skjøth, F., 26 Sept 2020, Artificial Intelligence in Medicine: 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Minneapolis, MN, USA, August 25-28, 2020, Proceedings. Springer, p. 203-213 11 p. (Lecture Notes in Computer Science, Vol. 12299).Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review
4 Citations (Scopus)