@article{park2020interactive,

title = {An interactive retrieval system for clinical trial studies with context-dependent protocol elements},

author = {Junseok Park and Seongkuk Park and Kwangmin Kim and Woochang Hwang and Sunyong Yoo and Gwan-su Yi and Doheon Lee},

url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0238290},

doi = {10.1371/journal.pone.0238290},

year  = {2020},

date = {2020-09-18},

urldate = {2020-09-18},

journal = {PloS one},

volume = {15},

number = {9},

pages = {e0238290},

publisher = {Public Library of Science San Francisco, CA USA},

abstract = {A well-defined protocol for a clinical trial guarantees a successful outcome report. When designing the protocol, most researchers refer to electronic databases and extract protocol elements using a keyword search. However, state-of-the-art database systems only offer text-based searches for user-entered keywords. In this study, we present a database system with a context-dependent and protocol-element-selection function for successfully designing a clinical trial protocol. To do this, we first introduce a database for a protocol retrieval system constructed from individual protocol data extracted from 184,634 clinical trials and 13,210 frame structures of clinical trial protocols. The database contains a variety of semantic information that allows the filtering of protocols during the search operation. Based on the database, we developed a web application called the clinical trial protocol database system (CLIPS; available at https://corus.kaist.edu/clips). This system enables an interactive search by utilizing protocol elements. To enable an interactive search for combinations of protocol elements, CLIPS provides optional next element selection according to the previous element in the form of a connected tree. The validation results show that our method achieves better performance than that of existing databases in predicting phenotypic features.},

keywords = {Clinical trial, Medical informatics},

pubstate = {published},

tppubtype = {article}

}

@article{park2020reliable,

title = {Reliable data collection in participatory trials to assess digital healthcare applications},

author = {Junseok Park and Seongkuk Park and Gwangmin Kim and Kwangmin Kim and Jaegyun Jung and Sunyong Yoo and Gwan-Su Yi and Doheon Lee},

url = {https://ieeexplore.ieee.org/abstract/document/9054970},

doi = {10.1109/ACCESS.2020.2985122},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {IEEE Access},

volume = {8},

pages = {79472–79490},

publisher = {IEEE},

abstract = {The number of digital healthcare mobile applications in the market is exponentially increasing owing to the development of mobile networks and widespread usage of smartphones. However, only few of these applications have been adequately validated. Like many mobile applications, in general, the use of healthcare applications is considered safe; thus, developers and end users can easily exchange them in the marketplace. However, existing platforms are unsuitable for collecting reliable data for evaluating the effectiveness of the applications. Moreover, these platforms reflect only the perspectives of developers and experts, and not of end users. For instance, typical clinical trial data collection methods are not appropriate for participant-driven assessment of healthcare applications because of their complexity and high cost. Thus, we identified the need for a participant-driven data collection platform for end users that is interpretable, systematic, and sustainable, as a first step to validate the effectiveness of the applications. To collect reliable data in the participatory trial format, we defined distinct stages for data preparation, storage, and sharing. The interpretable data preparation consists of a protocol database system and semantic feature retrieval method that allow a person without professional knowledge to create a protocol. The systematic data storage stage includes calculation of the collected data reliability weight. For sustainable data collection, we integrated a weight method and a future reward distribution function. We validated the methods through statistical tests involving 718 human participants. The results of a validation experiment demonstrate that the compared methods differ significantly and prove that the choice of an appropriate method is essential for reliable data collection, to facilitate effectiveness validation of digital healthcare applications. Furthermore, we created a Web-based system for our pilot platform to collect reliable data in an integrated pipeline. We compared the platform features using existing clinical and pragmatic trial data collection platforms.},

keywords = {Clinical trial, Medical informatics},

pubstate = {published},

tppubtype = {article}

}