Abstract

Background and aims

To assess the hospitalized sick children admitted to the pediatric emergency department (ED) and to find new patterns of clinical and laboratory attributes using association rule mining (ARM).

Methods

In this observational study, 158 children with median (IQR) age 11 months and a PRISM III score of 5 (2–9) were enrolled. Hotspot data mining method was applied to assess clinical attributes, lab investigations and pre-defined outcome parameters of children and their association in sick hospitalized children aged 1 month to 12 years.

Results

We obtained 30 rules with value for outcome as discharge is given attributes as follows: duration of hospitalization > 4 days, lactate > 1.2 mmol/L, platelet = 3.67/μL, dur_ventil = 0 h, serum K = 5.2 mmol/L, SBP = 120 mmHg, pCO₂ = 41.9 mmHg, PaO₂ = 163 mmHg, age = 92 months, heart rate > 114–159 per minute, temperature > 98 °F, GCS (Glasgow Coma Scale) > 7–14, gas K = 4.14 mmol/L, gas Na = 138.1 mmol/L, BUN (Blood Urea Nitrogen) = 18.69 mg/dL, Diagnosis > 1–718, Creatinine = 1.2 mg/dL, serum Na = 148 mmol/L, shock = 2, Glucose = 144 mg/dL, Mg(i) > 0.23 meq/L, BUN > 6.54 mg/dL.

Conclusion

ARM is an effective data analysis technique to find meaningful patterns using clinical features with actual numbers in pediatric critical illness. It can prove to be important while analysing the association of clinical attributes with disease pattern, its features, and therapeutic or intervention success patterns.

中文翻译：

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目标关联规则挖掘以探索紧急情况下的新型儿科疾病模式

摘要

背景和目标

评估入院儿科急诊科 (ED) 的住院患病儿童，并使用关联规则挖掘 (ARM) 寻找临床和实验室属性的新模式。

方法

在这项观察性研究中，招募了 158 名中位 (IQR) 年龄为 11 个月且 PRISM III 评分为 5 (2–9) 的儿童。应用热点数据挖掘方法评估 1 个月至 12 岁患病住院儿童的临床属性、实验室调查和预定义结果参数及其关联。

结果

我们获得了 30 条具有结果价值的规则，因为给出的属性如下：住院时间 > 4 天，乳酸 > 1.2 mmol/L，血小板 = 3.67/μL，dur_ventil = 0 h，血清K  = 5.2 mmol/L，SBP = 120 mmHg，pCO ₂  = 41.9 mmHg，PaO ₂  = 163 mmHg，年龄 = 92 个月，心率 > 114–159 每分钟，体温 > 98 °F，GCS（格拉斯哥昏迷量表）> 7–14，气体K  = 4.14 mmol/L，气体 Na = 138.1 mmol/L，BUN（血尿素氮）= 18.69 mg/dL，诊断 > 1–718，肌酐 = 1.2 mg/dL，血清 Na = 148 mmol/L，休克 = 2，葡萄糖 = 144 mg/dL，Mg(i) > 0.23 meq/L，BUN > 6.54 mg/dL。

结论

ARM 是一种有效的数据分析技术，可使用具有儿科危重疾病实际数字的临床特征来寻找有意义的模式。在分析临床属性与疾病模式、其特征以及治疗或干预成功模式的关联时，它可能被证明是重要的。