## 3 Results and Discussion
### Workflow from Data Acquisition to Location Tracking
#### 3.1.2 Correlation and Regression of Trauma Scores and their Predictors
Only eight patients belonging to a particular clinical class were considered for analysis; with additional samples, the correlation scores may improve. Also, with additional information related to blood chemistry using portable blood chemistry analysis kits, it would be possible to predict mortality and probability of survival using Simplified Acute Physiology Score (SAPS II) scores, which is the more precise score, as it also considers blood chemistry and urine samples. In addition, further research may consider the activity monitoring of the human subject using the accelerometer \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **R** & **R Square** & **Adjusted R Square** & **Std. Error of the Estimate** \\ \hline
1 & 0.968 \({}^{\mathrm{a}}\) & 0.937 & 0.937 & 0.033065132300000 \\ \hline \hline \end{tabular} \({}^{\mathrm{a}}\) Predictors: (Constant), SpO2, RespiratoryRate, SysBP, HeartRate, Age.  
\end{table} 
Table 4: Regression for Ps (Blunt) prediction. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scores and Correlation Measure** & **PsBlunt** & **NEWS** & **RTS** \\ \hline \multirow{3}{*}{PsBlunt} & Pearson Correlation & 1 & 0.7950 \({}^{**}\) & 0.0630 \({}^{**}\) \\  & Sig. (2-tailed) & & 0.000 & 0.000 \\  & Sig. (2-tailed) & 0.000 & 0.000 \\ NEWS & Sig. (2-tailed) & 0.000 & 0.000 \\  & N & 368721 & 368721 & 368721 \\ \hline \multirow{3}{*}{RTS} & Pearson Correlation & 0.063 \({}^{**}\) & \(-\)0.252 \({}^{**}\) & 1 \\  & Sig. (2-tailed) & 0.000 & 0.000 & \\  & N & 368721 & 368721 & 368721 \\ \hline \hline \end{tabular} \({}^{**}\)Correlation is significant at the 0.01 level (2-tailed).  
\end{table} 
Table 3: Correlation between Ps, NEWS, and RTS scores. module, which could give more insights into the effect of activity on the ECG and PPG readings. The use of machines learning models can enhance the prediction accuracy, as has been found in quite a few data analysis experiments that have used the MIMIC II Numerics database, as it contains the physiological parameters for NEWS and RTS calculations. The MIMIC database has been used in several machine learning data analysis tasks, and it is quite a widely used database. The Numerics records contain the time series of vital signs that are sampled once per second, or once per minute, containing measurements of the systolic and diastolic blood pressures, or the heart rate. The Numerics records are stored in a format that are similar to the waveform records, but since the sampling rates are far lower, the Numerics records are much smaller. The Numerics data also contain annotations that are related to the patient alerts and monitoring device-related alerts with other non-periodic data; e.g., electrodes being misplaced or devices being disconnected, etc. Information is available for some of the ICU monitor alerts, in some cases with additional observations that are collected from other equipment sources. The format of the additional observations is similar to the annotation logs of the MIMIC II Clinical Database, and each annotation links with a specific time interval in a waveform or Numerics record, and it may optionally also link to a specific waveform signal or time series. The copies of the alarm annotation files containing information about these alerts are linked to both the waveform and the Numerics records. Since MIMIC II database is combination of the waveform and clinical database, a QueryBuilder can be used to link the waveform, the Numerics (vital signs), and the clinical information and using a Structured Query Language (SQL) the database can be queried and resulting data can be presented. MIMIC-II has also provided data for annual PhysioNet/Computing in Cardiology Challenges, including the 2012 Challenge, \"Predicting mortality of ICU Patients\" [44]. Since the relationship between the risk of mortality in the ICU and the physiological variables depends on the sample space of the dataset, the prediction can be improved by using automated neural networks or data-mining approaches, to predict hospital mortality in ICU patients.