Development and validation of HBV surveillance models using big data and machine learning

Figures & data

Figure 1. Examples of structuralization of health record in text: (A) shows the text segmentation of the text records of a real case, while (B) shows the physical complaints extracted from this record data.

Table 1. List of the frequencies of the present symptoms (N = 158,988).

Symptoms	Frequency	%
Abdominal pain	7146	4.49%
Head pain	6060	3.81%
Throat pain	8007	5.04%
Chest pain	4045	2.54%
Waist pain	3567	2.24%
Neck pain	1804	1.13%
Joint pain	3121	1.96%
Chest distress	4526	2.85%
Flatulence	2665	1.68%
Nausea	6282	3.95%
Burping	2197	1.38%
Diarrhea	1875	1.18%
Loss of appetite	1444	0.91%
Abdominal mass	195	0.12%
Ulcer	92	0.06%
Urine yellow	132	0.08%
Gas dysfunction	124	0.08%
Nose running	6463	4.07%
Cough	15128	9.52%
Fever	6719	4.23%
Nasal congestion	5615	3.53%
Dizziness	6885	4.33%
Palpitation	3407	2.14%
Fatigue	5945	3.74%
Weight change	3156	1.99%
Sleep disorders	12275	7.72%
Rash	3664	2.30%
Itchiness	7929	4.99%
Urine problem	1373	0.86%
Urine color	220	0.14%

Table 2. The sensitivity and specificity of the model at different cutoff values.

Sensitivity	Specificity	Cutoff
90.20%	47.51%	0.0120
85.30%	58.99%	0.0170
80.12%	70.28%	0.0247
70.03%	79.25%	0.0347
66.28%	81.81%	0.0383
60.23%	84.92%	0.0445
50.14%	89.98%	0.0586

Note: The values of sensitivities and specificities were selected by sensitivity level of 90%, 85%, 80%, 70%, 60% and 50%, and cut-off of HBV prevalence in the cohort data.

Figure 2. Correlations between each pair of physical complaints in the study sample (N = 158,988). (Cells with dot indicate statistically significant correlation. Blue and red colour represent positive and negative correlation, respectively. The correlations were measured using Spearman’s rank correlation co-efficient, and the statistical significance was determined using Z-test.)

Figure 3. Patterns of physical complaints: (A) is the pattern of CHB patients; and (B) is the pattern of non-CHB people (symptoms in the same cluster usually present at the same time, and vice versa).

Figure 4. ROC curves of different HBV detection models on test sample (n = 9,131).

Figure 5. Importance of the predictors in XGB model based on physical complaints and clinical parameters (X-axis is the SHAP value of different predictors. Predictors with higher SHAP value can provide more predictive power and are more important).

Figure 6. Nonlinear effects of the top ten important predictors in the XGB model based on symptoms and clinical parameters.

Figure 7. ROC curves of different HBV detection models based on symptoms and common clinical parameters on test sample (n = 9,131).

Figure 8. Importance ranking of the predictors in XGB model based on symptoms and common clinical parameters.

Figure 9. Confusion matrix of the risk XGB model based on physical complaints and clinical parameters at cutoff of HBV prevalence in cohort. (TP = true positive; FP = false positive; FN = false negative; TN = true negative).

Data availability statement

Data of this study are available from the corresponding author upon reasonable request.