網路不僅僅被應用在基因、蛋白質及代謝等各個領域上，已有許多研究關於疾病之間的共病性，卻鮮少著重在急病之間的方向性，意指疾病之間的前後發生順序。透過醫院的資料，研究疾病之間的關聯性，利用統計方法McNemar's test去估算兩兩疾病之間的關聯性，找出有顯著意義的疾病(χ^2>3.84, p<0.05) ，接著藉由odds ratio(OR) 計算疾病之間關聯性的強度，OR>1的疾病具有正相關性。建構疾病網路，分析疾病的共病性和盛行率在各年齡層的分佈、性別上的差異，以及疾病的所屬科別，經由疾病共病性的關係，以及在統計分析上具有顯著意義的資料，進而探討疾病之間的方向性，使用skewness test估算疾病之間的方向性，疾病間可能存在的因果關係。結果顯示，疾病間的共病性對於人類個體的影響更甚於單一疾病，不論是共病性或是方向性，皆因年齡和性別而有所不同。 Networks are performed to different genomic, proteomic, metabolic datasets to demonstrating the origins of specific diseases. A lot of studies are about disease comorbidity, but seldom researches on relationships about directionality of diseases. Through hospital datasets, we research association between disease pairs. To estimate correlations within two diseases by using statistic method that is McNemar's test. Getting significant data from McNemar's test are χ^2>3.84 (p<0.05). Then, we explore the strength of disease pairs by odds ratio (OR) that significant data are OR>1, meaning positive correlation. To construct disease networks and we analyze prevalence and comorbidity of disease difference in age, gender and department. Furthermore, according to comorbidity, we even try to investigae directionality of disease pairs. Through skewness test, we measure precedence between two diseases. Potentially, disorders exist causal relationships. However, disease comorbidity to individual affects more than single disease. Our study can show the result of comorbidity that differs in age and gender.
