## 6 Analysis of Experiment Results
### NLP Transformation Accuracy  
Table 9 demonstrates the results obtained via NLP-assisted transformation. It is worth noting several assumptions that were made while taking this table into account. It is visible that mostly numerical qualities are parsed correctly but seems to make frequent mistakes in recognizing features represented by discrete numerical values, such as gender. This is likely caused by _sense2 vec_ model's general purpose, rather than it being geared towards scientific language, which was evident during debug when it assigned informal terms as synonyms for column labels. While the presence of these patterns in the program does not necessarily reduce parsing quality, it greatly slows initialization time and forces us to take more samples to have actually helpful words among them. Another problem it appears to face is the detection of negation and uncertainty in the case of boolean values, which is one of the challenges in contemporary medical NLP. There is also a slight discrepancy in days between the parsed results and actual results, caused by the values in text being expressed in years, rather than days. This discrepancy is ignored when calculating accuracy, with the number being considered correctly parsed if it is not far from the original value. With the highest accuracy per sample at 100%, and lowest accuracy per sample at 45%, we can see that the approach demonstrates variable results from sample to sample, performing on some better than the others. A likely cause of this is the approach recognizing specific words better than the others, which once again comes back to the limited availability of models. Overall, the approach shows somewhat positive, although not accurate enough results for the method to be applied on real data. There is a chance, however, that these results are not indicative of the real life environment due to the data samples being created specifically for testing purposes. Real patient note records may show results that are drastically different. \begin{table}
\begin{tabular}{l l l l l} \hline
**Dthr** & **Accuracy Percentage** & **Avg. Fitting Time (ms)** & **Avg. Prediction Time (ms)** & **Mean Square Error (for 50 Experiments)** \\ \hline  
\end{tabular}
\end{table} 
Table 8: TWNFI evaluation depending on the ECM radius parameter, on a dataset of 250 samples. In an environment without any prior manipulations to the data, with the formats similar to each other, the approach yields pretty good results, with best values (367.6, 2.54, and 0.46 for the first experiment, and 367.74, 2.54, 0.46 for the second one) achieved being not far away from the expected coefficients. However, the optimal numbers of similar samples (250 and 1000 in the cases of these experiments) appear to differ from case to case, with a smaller amount of known traits possibly requiring larger samples. While the similarity-based conversion method has proven to be relatively accurate when tested on data with a high number of known traits without discrepancies, both the simulated environment with split data and when used to merge two datasets alone, it has proven to be more unstable and less effective. It is also clearly visible that, in the case of the first table, amounts of samples greater than 250 distort the results due to less similar samples influencing the calculation. Upon a closer look at Table 12, where the datasets have been split and the program was forced to reconstruct the values we know, we can see how certain data samples look when extended, which reveals a few things. Depending on the parameters, the restored sample may either get distorted by a large number of outliers as similar samples with a smaller number, or will be too generalized under a larger number of similar samples, which plays into the widely known problem of overfitting. A similar trend can be observed in Table 13, which demonstrates how the number of steps N impacts the overall performance.