Clinicians conduct routine diagnosis by scrutinizing signs and symptoms of patients in treating epidemics. This skill evolves through trial-and-error and improves with time. The success of the therapeutic regimen relies largely on the accuracy of interpretation of such sign-symptoms, based on which the clinician ranks the potent causes of the epidemic and analyzes their interdependence to devise sustainable containment strategies. This study proposed an alternative medical front, a VIRtual DOCtor (VIRDOC), that can self-consistently rank key contributors of an epidemic and also correctly identify the infection stage, using the language of statistical modelling and Machine Learning. VIRDOC analyzes medical data and then translates these into a vector comprising Multiple Linear Regression (MLR) coefficients to probabilistically predict scores that compare with clinical experience-based assessment. The VIRDOC algorithm, risk managed through ANOVA, has been tested on dengue epidemic data (N=100 with 11 weighted sign-symptoms). Results highly encouraging with ca 75% accurate fatality prediction, compared to 71.4% from traditional diagnosis. The algorithm can be generically extended to analyze other epidemic forms.

翻译：临床医生通过仔细检查病人在治疗流行病方面的症状和症状进行常规诊断。这种技能通过试验和感应过程演化,并随着时间的推移不断改进。治疗疗法的成功主要取决于对这种症状的精确解释,临床医生据此排列了该流行病的有力原因,并分析了其相互依存性,以制定可持续的遏制战略。这项研究建议了另一种医疗战线,即VIRTAL DOCtor(VIRDOC),它可以自我一致地排在某一流行病的主要发病者的位置,并且也可以正确地确定感染阶段,使用统计模型和机器学习的语言。VIRDOC分析医疗数据,然后将这些数据转化为由多线性回归(MLRR)系数构成的矢量,以概率预测与临床经验评估相比较的得分数。VIRDC算法,通过ANOVA管理的风险,已经根据登革热流行病数据进行了测试(N=100,11个加权信号-症状),结果非常令人鼓舞,75%的准确致命性预测是传统诊断的71.4%。算法可以扩展为其他的通用分析形式。