Hybrid Text Feature Modeling for Disease Group Prediction using Unstructured Physician NotesSubmitted to ICCS 2020.
Table 4: Experimental Results – Comparison with State-of-the-art
['[BOLD] Parameter', '[BOLD] Our Approach', 'Purushotham et al. ']
Next, a comparative benchmarking of the proposed approach against the state-of-the-art ICD9 disease group prediction model developed by Purushotham et al. Although the number of records under consideration for both the studies are different, it is to be noted that the labels are distributed similarly (statistics shown in Fig. Purushotham et al’s model Purushotham et al. This shows that the predictive power of a model built on unstructured patient data exceeds that of those built on structured data, where some relevant information may be lost during the coding process. To encourage comparative studies that use physician notes in MIMIC-III dataset, certain additional metrics were also considered. The Recall & F-Score performance as well as the MCC values of the proposed model over our easily reproducible patient cohort data subset are also observed and provided.