# Clinical Trial Classification of SNS24 Calls with Neural Networks
## 7 Analysis of Individual Predictions  
In this section, we aim to compare the labels ascribed by the neural network model and the support vector machines predictions (model from previous work) with the original labels.  
Over time, the number of different nurses working in the health line is very high, and some nurses can be biased in the selection according to their experience or other factors. For example, in the 3-month period of the collected data, 588 nurses attended calls and chose the most adequate clinical pathway according to their insight.  
This analysis was done for the ten clinical pathways with most records in the dataset, selecting ten examples from each class where the \"contact reason\" text had no common words with the clinical pathway. This resulted in a set of 100 examples.  
From the comparison between the triage given by the phone line and the two ML \"experts\", we observe that both ML models agree on 78 and all three agree on 49 examples out of the 100 examples; the SVM model agrees with the original triage in 53 examples, while the NN model agrees with the original triage in 55 examples.  
Table 7 resumes the agreements between experts for a sub-sample containing 20 examples (Table 7 presents the \"contact reason\" of each example along with the clinical pathway selected by the nurse; Table 8 presents the corresponding labels given by the ML models).  
Figure 10: Analysis among clinical pathways with similar clinical symptoms using the trained neural network model. This group includes three clinical pathways: _Diarreia_ (_Diarrhea_), _Dor abdominal_ (_Abdominal pain_), and _Nausea e vomitos_ (_Nausea and vomiting_).  
It is interesting to look at the text where both ML models agree but disagree with the triage. In example 2 both classify it as _Nasal problem_ whereas the triage classify it as _Cough_. The text mentions \"rhinorrhea\" and no cough related symptoms. Also example 3, \"Lack of appetite 6 h ago\", is classified as _Nonspecific problems_ while triage selected _Nausea and vomiting problems_. In fact, there is no word in the text that gives a hint about nausea.  
On the other hand, there are examples where the ML experts select pathways that have close connection to words appearing in the \"contact reason\": example 5 is classified by both models as _Pregnancy problem_, _puererium_ while the _Abdominal pain_ pathway was chosen on triage and the \"contact reason\" text includes the word \"pregnancy\"; examples 8 and 14, that include the words \"vaginal\" and \"blood loss\", respectively, are classified by both models as _Woman health problem_ while the pathways chosen on triage were _Oropharynx problem_ and _Uriinary problem_, respectively.  
Nonetheless, sometimes they seem to be too biased: for example, the contact reason with text \"itching starting 3 days ago, in the armpits and genital region\" (example 10) is classified as _Woman health problem_, but there's no body part mentioned specific to women.  
Sometimes we see some ambiguity, as in example 16, which both models classify as _Nausea and vomiting problems_, while the triage selected _Diarrhea_ and the text includes both the words \"vomiting\" and \"diarrhea\".  
On example 7, with text \"Canker sores 1 day ago, fever 2 days ago\", the NN agrees with the triage on the pathway _Oropharynx problem_ while NN chooses _Face problem_ for example 12, with text \"sore throat since Friday and fever 38.7\", SVM is the model that agrees with the triage on _Flu syndrome_ while NN chooses _Oropharynx problem_.  
Two examples where the three experts disagree have triage as _Chest pain_. In example 17 with text \"Easy fatigue 1h ago, with irradiation\", SVM classified as _Nonspecific problems_ while NN as _Breathing problem_; in example 18 with text \"Flink pain since 15 days pregnant\", SVM classified as _Pregnancy problem_, _puerperium_ and NN as _Lower limb problems_, _Hip_.