# Using Enhanced Representations to Predict Medical Procedures from Clinician Notes
## 5 Conclusions and Future Work  
This work proposed a novel approach to ICD procedure prediction supported by diagnoses. To achieve this, we developed a strategy to combine information about known diagnoses with textual annotations by concatenating their respective vectorized representations, and using the result as input to the prediction task. The hierarchical structure of the ICD scheme was also considered in this study. Diagnosis ICD codes are described using varying levels of detail. Specifically, we use the fully detailed code and its primary category, which captures its general characteristics. In addition, this work also performed set expansion over the resulting representations by aggregating both levels of detail into a single vector. The evaluation was supported by F1 as the main performance metric, due to its popularity in the research community in this domain [3; 10; 11; 49]. However, other metrics could be considered. Some authors [50; 51], proposed using additional metrics that lead to more relevant results compared to the commonly used F1-based metric. Other authors [15; 52] proposed metrics that evaluate the impact of mispredictions, which is especially important in healthcare, given the consequences of incorrect predictions [53; 54]. An extended evaluation will be explored in the future to take these considerations into account. The results show that incorporating the information about diagnosis produced better results in the prediction task than using medical annotations alone. The method proposed in this paper provided competitive results, outperforming comparable alternatives in the SOTA. We emphasized the use of language models to support text representation, due to their ability to generate highly relevant yet relatively small embedding vectors and because of the active research on explainability, an important quality in healthcare. Nevertheless, the proposed approach to ICD prediction is not limited to BERT-based architectures, as it can be implemented in other prediction pipelines. One of this study's limitations is the reduced number of architectures used to test our assumptions. The work focused on validating the idea of implementing a procedure prediction task supported by diagnoses. Still, this does not give a complete picture of how the method would behave when combined with other DL architectures, such as CNNs, which currently hold the SOTA in ICD prediction. Future iterations of this work aim to address this gap by validating the results in a broader variety of architectures. Model optimization also needs to be addressed, given the community's interest in greener DL solutions [55] and the impact of computational cost in real-world use cases. Finally, another limitation is that this work did not consider using rating scales [56; 57], which are useful for patient assessment. Rating scales are especially important in the context of mental health [58], a topic that has gained relevance since the COVID-19 pandemic [59; 60]. Future work aims to adopt newer revisions of the ICD standard to facilitate the analysis of these types of scales [61; 62]. The representation method proposed in this article shows that it is possible to improve the performance in the ICD procedure prediction task by considering the clinical nuances between diagnosis and procedure codes. Furthermore, the approach presented in this study aims to reproduce how medical professionals draw their conclusions from observations, by establishing a diagnosis and then assigning relevant procedures to the case. Because of this, the approach can potentially improve the overall interpretability of the prediction task. Conceptualization: R.M., O.A. and C.A.I. ; methodology: R.M., O.A. and C.A.I. ; validation: R.M. ; writing, review, and editing: R.M., O.A. and C.A.I. All authors have read and agreed to the published version of the manuscript. This research was partially funded by the Spanish Ministry of Science and Innovation through the MIRATAR project (TED2021-132149B-C42) and by the European Union with NextGeneration EU funds.