Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering

Due to the rapid growth of information available about individual patients, most physicians suffer from information overload when they review patient information in health information technology systems. In this manuscript, we present a novel hybrid dynamic and multi-collaborative filtering method to improve information retrieval from electronic health records. This method recommends relevant information from electronic health records for physicians during patient visits. It models information search dynamics using a Markov model. It also leverages the key idea of collaborative filtering, originating from Recommender Systems, to prioritize information based on various similarities among physicians, patients and information items We tested this new method using real electronic health record data from the Indiana Network for Patient Care. Our experimental results demonstrated that for 46.7% of test cases, this new method is able to correctly prioritize relevant information among top-5 recommendations that physicians are truly interested in

Utilizing RxNorm to Support Practical Computing Applications: Capturing Medication History in Live Electronic Health Records

RxNorm was utilized as the basis for direct-capture of medication history data in a live EHR system deployed in a large, multi-state outpatient behavioral healthcare provider in the United States serving over 75,000 distinct patients each year across 130 clinical locations. This tool incorporated auto-complete search functionality for medications and proper dosage identification assistance. The overarching goal was to understand if and how standardized terminologies like RxNorm can be used to support practical computing applications in live EHR systems. We describe the stages of implementation, approaches used to adapt RxNorm's data structure for the intended EHR application, and the challenges faced. We evaluate the implementation using a four-factor framework addressing flexibility, speed, data integrity, and medication coverage. RxNorm proved to be functional for the intended application, given appropriate adaptations to address high-speed input/output (I/O) requirements of a live EHR and the flexibility required for data entry in multiple potential clinical scenarios. Future research around search optimization for medication entry, user profiling, and linking RxNorm to drug classification schemes holds great potential for improving the user experience and utility of medication data in EHRs.Comment: Appendix (including SQL/DDL Code) available by author request. Keywords: RxNorm; Electronic Health Record; Medication History; Interoperability; Unified Medical Language System; Search Optimizatio

Approaches for the digital profiling of activities and their applications in design information push

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Swarm intelligence for clustering dynamic data sets for web usage mining and personalization.

Swarm Intelligence (SI) techniques were inspired by bee swarms, ant colonies, and most recently, bird flocks. Flock-based Swarm Intelligence (FSI) has several unique features, namely decentralized control, collaborative learning, high exploration ability, and inspiration from dynamic social behavior. Thus FSI offers a natural choice for modeling dynamic social data and solving problems in such domains. One particular case of dynamic social data is online/web usage data which is rich in information about user activities, interests and choices. This natural analogy between SI and social behavior is the main motivation for the topic of investigation in this dissertation, with a focus on Flock based systems which have not been well investigated for this purpose. More specifically, we investigate the use of flock-based SI to solve two related and challenging problems by developing algorithms that form critical building blocks of intelligent personalized websites, namely, (i) providing a better understanding of the online users and their activities or interests, for example using clustering techniques that can discover the groups that are hidden within the data; and (ii) reducing information overload by providing guidance to the users on websites and services, typically by using web personalization techniques, such as recommender systems. Recommender systems aim to recommend items that will be potentially liked by a user. To support a better understanding of the online user activities, we developed clustering algorithms that address two challenges of mining online usage data: the need for scalability to large data and the need to adapt cluster sing to dynamic data sets. To address the scalability challenge, we developed new clustering algorithms using a hybridization of traditional Flock-based clustering with faster K-Means based partitional clustering algorithms. We tested our algorithms on synthetic data, real VCI Machine Learning repository benchmark data, and a data set consisting of real Web user sessions. Having linear complexity with respect to the number of data records, the resulting algorithms are considerably faster than traditional Flock-based clustering (which has quadratic complexity). Moreover, our experiments demonstrate that scalability was gained without sacrificing quality. To address the challenge of adapting to dynamic data, we developed a dynamic clustering algorithm that can handle the following dynamic properties of online usage data: (1) New data records can be added at any time (example: a new user is added on the site); (2) Existing data records can be removed at any time. For example, an existing user of the site, who no longer subscribes to a service, or who is terminated because of violating policies; (3) New parts of existing records can arrive at any time or old parts of the existing data record can change. The user\u27s record can change as a result of additional activity such as purchasing new products, returning a product, rating new products, or modifying the existing rating of a product. We tested our dynamic clustering algorithm on synthetic dynamic data, and on a data set consisting of real online user ratings for movies. Our algorithm was shown to handle the dynamic nature of data without sacrificing quality compared to a traditional Flock-based clustering algorithm that is re-run from scratch with each change in the data. To support reducing online information overload, we developed a Flock-based recommender system to predict the interests of users, in particular focusing on collaborative filtering or social recommender systems. Our Flock-based recommender algorithm (FlockRecom) iteratively adjusts the position and speed of dynamic flocks of agents, such that each agent represents a user, on a visualization panel. Then it generates the top-n recommendations for a user based on the ratings of the users that are represented by its neighboring agents. Our recommendation system was tested on a real data set consisting of online user ratings for a set of jokes, and compared to traditional user-based Collaborative Filtering (CF). Our results demonstrated that our recommender system starts performing at the same level of quality as traditional CF, and then, with more iterations for exploration, surpasses CF\u27s recommendation quality, in terms of precision and recall. Another unique advantage of our recommendation system compared to traditional CF is its ability to generate more variety or diversity in the set of recommended items. Our contributions advance the state of the art in Flock-based 81 for clustering and making predictions in dynamic Web usage data, and therefore have an impact on improving the quality of online services

Social Relations and Methods in Recommender Systems: A Systematic Review

With the constant growth of information, data sparsity problems, and cold start have become a complex problem in obtaining accurate recommendations. Currently, authors consider the user's historical behavior and find contextual information about the user, such as social relationships, time information, and location. In this work, a systematic review of the literature on recommender systems that use the information on social relationships between users was carried out. As the main findings, social relations were classified into three groups: trust, friend activities, and user interactions. Likewise, the collaborative filtering approach was the most used, and with the best results, considering the methods based on memory and model. The most used metrics that we found, and the recommendation methods studied in mobile applications are presented. The information provided by this study can be valuable to increase the precision of the recommendations

Web information search and sharing :

制度:新 ; 報告番号:甲2735号 ; 学位の種類:博士(人間科学) ; 授与年月日:2009/3/15 ; 早大学位記番号:新493

Addendum to Informatics for Health 2017: Advancing both science and practice

This article presents presentation and poster abstracts that were mistakenly omitted from the original publication