Biomedical Data Mining for Information Retrieval. Группа авторов

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СКАЧАТЬ Na Serum Sodium mEq/L 22. NIDiasABP Non-invasive diastolic arterial blood pressure mmHg 23. NIMAP Non-invasive mean arterial blood pressure mmHg 24. NISysABP Non-invasive systolic arterial blood pressure mmHg 25. PaCO2 Partial pressure of arterial carbon dioxide mmHg 26. PaO2 Partial pressure of arterial oxygen mmHg 27. pH Arterial pH [0–14] 28. Platelets Platelets cells/nL 29. RespRate Respiration Rate bpm 30. SaO2 O2 saturation in hemoglobin % 31. SysABP Invasive systolic arterial blood pressure mmHg 32. Temp Temperature °C 33. TropI Troponin-I µg/L 34. TropT Troponin-T µg/L 35. Urine Urine Output mL 36. WBC White Blood Cells Count cells/nL

      1.3.4 Mortality Prediction

After data pre-processing, normalization, feature extraction and feature reduction, different models are employed to predict the patient’s mortality in an in-hospital stage and calculate the accuracy. The models predict either patient will survive or die. This is determined by using classification technique as mortality prediction is a binary classification problem. This process is done step by step as shown in Figure 1.1.

Table 1.2 Time series variables with physical units [30].

S. no.	Variables	Physical units
1.	Temperature	Celsius
2.	Heart Rate	bpm
3.	Urine Output	mL
4.	pH	[0–14]
5.	Respiration Rate	bpm
6.	GCS (Glassgow Coma Index)	[3–15]
7.	FiO2 (Fractional Inspired Oxygen)	[0–1]
8.	PaCo2 (Partial Pressure Carbon dioxide)	mmHg
9.	MAP (Invasive Mean arterial blood pressure)	mmHg
10.	SysABP (Invasive Systolic arterial blood pressure)	mmHg
11.	DiasABP (Invasive Diastolic arterial blood pressure)	mmHg
12.	NIMAP (Non-invasive mean arterial blood pressure)	mmHg
13.	NIDiasABP (Non-invasive diastolic arterial blood pressure)	mmHg
14.	Mechanical ventilation respiration	[yes/no]
15.	NISysABP (Non-invasive systolic arterial blood pressure)	mmHg

      1.3.5 Model Description and Development

      Different models are developed in this chapter to estimate the performance of mortality prediction and comparison between them is also made. The models such as FLANN, Discriminant analysis, Decision Tree, KNN, Naive Bayesian and Support Vector Machine are applied to develop different classifiers. Out of 4,000 records of dataset A 3,000 records are taken as training set and remaining 1,000 records are used for validation or test of the models.

      First of all Factor Analysis (FA) is applied to the selected variables to reduce the features. Factor analysis is one of the feature reduction techniques which is used to reduce the high dimension features to low dimension [31]. The 58 features of the dataset are reduced to 49 using FA. Several steps to of factor analysis are

Figure 1.1 Step by step process for mortality prediction.

      1 First normalize the data СКАЧАТЬ