Abstract
Determining whether correct disease codes are included in discharge summaries is important for hospital management because submission of medical receipts with incorrect disease codes can result in loss of insurance reimbursement. Because medical information managers in large hospitals must evaluate more than 1000 summaries per month, an automated determination of discharge summaries will reduce their workload, allowing information managers to focus on complicated cases. This paper proposes a method of constructing classifiers of discharge summaries. In the first step, morphological analysis generated a term matrix from text data extracted from the hospital information system. Subsequently, important keywords were selected from an analysis of correspondence, training examples were generated, and machine learning methods were applied to the training examples. Several machine learning methods were compared using discharge summaries stored in the information system of Shimane University Hospital. A random forest method was found to be the best classifier when compared with deep learning, SVM and decision tree methods. Furthermore, the random forest method had a classification accuracy greater than 90%.
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Notes
Outpatient clinics utilize action-based payment systems, even in large hospitals.
The method can also generate \(p (p\ge 3)\)-dimensional coordinates. However, higher dimensional coordinates did not provide better performance than the experiments shown below.
Darch was removed from R package. Please check the github: https://github.com/maddin79/darch.
Two-fold cross-validation was selected because its estimator resulted in the lowest estimate of parameters, such as accuracy, as well as minimizing estimates of bias.
DPC codes are a three-level hierarchical system, with each DPC code defined as a tree. The first level denotes the type of disease, the second level denotes the primary treatment selected for that patient, and the third-level shows any additional therapy. Thus, in the tables, characteristics of codes were representative of similarities.
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This research was supported by a Grant-in-Aid for Scientific Research (B) 18H03289 from the Japan Society for the Promotion of Science(JSPS).
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This research was supported by a Grant-in-Aid for Scientific Research (B) 18H03289 from the Japan Society for the Promotion of Science (JSPS). On behalf of all authors, the corresponding author states that there are no conflicts of interest.
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Tsumoto, S., Kimura, T. & Hirano, S. Determination of Disease from Discharge Summaries. Rev Socionetwork Strat 15, 49–66 (2021). https://doi.org/10.1007/s12626-021-00076-7
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DOI: https://doi.org/10.1007/s12626-021-00076-7