# A Message Passing Approach to Biomedical Relation Classification for Drug-Drug Interactions
## 3 Results and Discussion
### Error Analysis  
To analyze the advantages and limitations of our proposed approach, we compare and analyze the classification results of the best performing model (M3.3) on a few indicative cases (Table 4). Figure 6 shows the confusion matrix, with a total of 330 errors that were made, representing 11% of the 3057 test cases. In addition, 273 (83%) originate from the DrugBank instances and the remaining 57 (17%) from MedLine abstracts, corresponding to 9.8% and 20% of the total interactions of their respective collection.  
First, misclassifying an existing interaction is the least common type of error, accounting for 20% of the total errors. Furthermore, 48% of these correspond to the case where instances of Int are classified as Effect, which makes this the main source of confusion in  
Figure 5: Performance comparison based on Drug Name Replacement (DNR) and Negative Instance Filtering (NIF), (**a**) M3.3 with DNR, (**b**) M8.2 with DNR, (**c**) M3.3 with NIF, (**d**) M8.2 with NIF.  
Figure 6: Confusion matrix of our proposed model.  
this category. The contributing drug pairs were found in only five sentences, with one of them giving 28 of the total 32. This is S1, where although the system can recognize the existence of interactions in the long list of drugs, it fails to predict the types. This is probably due to the presence of \"enhance\", a word regularly associated with drug pairs with the interaction type Effect. Furthermore, 42% of the errors involve false positive pairs that can occur due to the following cases:  
* The possibility of an annotation error is not excluded. Indicatively, in S2, every pair containing \(e_{0}\) is of type Effect, as predicted by the model. However, any such relationship other than \((e_{0},e_{1})\) is marked as negative in the data set.
* A substance appears multiple times in the sentence, such as _mebendazole_ in S3. The pairs \(e_{0}\), \(e_{1}\) and \(e_{0}\), \(e_{2}\) are the Mechanism and Negative types, respectively, but both are predicted as Mechanism. An attempt was made to limit these occurrences when rejecting cases, but, as S3 indicates, they do not follow a specific pattern that can be expressed.
* Similar to the case of S1, confusion is caused when the description is made with expressions that refer to another type. In S4, \(e_{0}\) is the subject of the sentence and declares the simultaneous administration of the mentioned drugs as safe, since they do not interact. However, due to the wording, the system perceives the relationships as Advice.
* Cases where drug mentions are listed and are not covered by regular expressions. e.g., in S5, the existence of \"the\" excludes the match and makes it impossible to locate the negative pairs \((e_{0},e_{i}),i=1,\ldots,5\). However, as a large number of instances has been discarded from the corpus, the model is unable to handle these underrepresented patterns.  
However, the most serious form of error concerns the inability to detect existing interactions, or else the existence of false negatives. An obvious source of error is the particularly long sentences, where descriptions are highly complex. The same applies to long sentences that could have been separated into smaller sentences, occurring in 53 related instances. We define sentences as long when they have a length of \(\geq\) 40 tokens (the number of words separated by a space, having replaced drugs with \"drug\"). S6 is an example of a sentence with a length of 40, where three interactions of the Mechanism type were not detected.  
However, there are several instances where misclassification can be attributed to system errors. For example, the interaction in S7 is not found, even though the sentence contains characteristic expressions that suggest that an interaction is being described and does not include any redundant information that might cause any confusion. The existence of such phenomena causes difficulty in the holistic interpretation of the results.