Mining Typical Treatment Duration Patterns for Rational Drug Use from Electronic Medical Records

Rational drug use requires that patients receive medications for an adequate period of time.The adequate duration time of medications not only improve the therapeutic effect of medicines,but also reduce the side effects and adverse reactions of medicines.This paper proposes a data-driven method to mine typical treatment duration patterns for rational drug use from electronic medical records (EMRs).Firstly,a quintuple is defined to describe drug use duration statistics (DUDS) for each drug and treatment record is further represented with DUDS vector (DUDSV).Next a similarity measure method is adopted to compute the similarity between treatment records.Meanwhile,a clustering algorithm is used to cluster all patient treatment records to extract typical treatment duration patterns including typical drug sets,effective drug use day sets,and the DUDSs of each typical drug.Then the extracted typical treatment duration patterns are evaluated and annotated based on patients' demographic information,disease severity scores,treatment outcome and diagnostic information.Finally,a real-world EMR dataset is performed to indicate that the approach we proposed can effectively mine typical treatment duration patterns from EMRs and recommend the appropriate treatment regimens for patients based on their admission information.

A Simulation Research Towards Better Leverage of Sales Ranking

As a kind of the most significantly popular information in markets,the sales ranking has great impacts on consumer choice.However,there are few discussions on how sales ranking should be provided to consumers in the literature.This paper aims to answer the following two questions:1)To what extent does the sales ranking influence consumer choices;2)When the sales ranking should be provided to consumers.To do so,this paper first constructs a sales ranking model and then provides detailed simulation experiments to demonstrate the model.The experimental results show that for markets where consumer preferences are dramatically different,such as music and movie markets,sales rankings do not have significant influences on consumer choices and should not be provided to consumers until a large number of early independent consumer choices have been accumulated.But for markets in which consumer preferences are similar,such as markets for official supplies,sales rankings have more influences on consumer choices and should be provided to consumers earlier.Furthermore,an evolution strategy is proposed to ascertain the most suitable sales rankings(characterised by suitable influence strength and suitable release time)for some specified online markets.The comparison results show that the optimized sales rankings not only can help consumers discover higher-quality products but also can improve overall sales.