Abstract
Prior studies have manually assessed diagnosis codes and found them to be erroneous/incomplete between 4–30% of the time. Previous methods to validate and suggest missing codes from medical notes are limited in the absence of these, or when the notes are not written in English. In this work, we propose using patients’ medication data to suggest and validate diagnosis codes. Previous attempts to assign codes using medication data have focused on a single condition. We present a proof-of-concept study using MIMIC-III prescription data to train a machine-learning-based model to predict a large collection of diagnosis codes assigned on four levels of aggregation of the ICD-9 hierarchy. The model is able to correctly recall 58.2% of the ICD-9 categories and is precise in 78.3% of the cases. We evaluate the model’s performance on more detailed ICD-9 levels and examine which codes and code groups can be accurately assigned using medication data. We suggest a specialized loss function designed to utilize ICD-9’s natural hierarchical nature. It performs consistently better than the non-hierarchical state-of-the-art.
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A Appendix - Omitted codes and detailed results
A Appendix - Omitted codes and detailed results
Table 2 details the ommitted ocdes from the diagnosis table and the reasons for omission. We omit all codes with a low number of cases. We further omit 61 codes used to describe symptoms, as these are shared by multiple causes and will, most-probably, supplant a diagnosis code following medical investigation. Injuries and foreign bodies (30 codes) are omitted as well as their treatment is usually orthopedic or surgical, rather than medicinal. We omit the codes used in ICD-9 to classify birth-age and pre-term phase for infants (14 codes) as these are more descriptive than diagnostic. Finally, we omit the E and V series of codes that are used to provide additional details for statistical reasons and which do not cause differences in medicinal treatment. We remain with 567 codes and 54,423 cases (92.4%) that contain at least one of the remaining codes. Filtering out only admissions contained in both the diagnosis and prescription tables we remain with 50,211 admissions.
Detailed results are available online [10].
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Sagi, T., Hansen, E.R., Hose, K., Lip, G.Y.H., Bjerregaard Larsen, T., Skjøth, F. (2020). Towards Assigning Diagnosis Codes Using Medication History. In: Michalowski, M., Moskovitch, R. (eds) Artificial Intelligence in Medicine. AIME 2020. Lecture Notes in Computer Science(), vol 12299. Springer, Cham. https://doi.org/10.1007/978-3-030-59137-3_19
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