Objective: To compare the platysma flap with submental flap in terms of tumor and flap characteristics, operative properties and the functional outcomes. Methods: A total of 65 patients presented with tumors of head and neck and underwent curative tumor resection with different neck dissections at the Department of Oromaxillofacial-Head and Neck Surgery, School of Stomatology of China Medical University; from March 2005 to December 2012 were included in the study. After radical tumor excision and neck dissection the resultant complex defects were reconstructed with either platysma flap or the submental flap. The extent of surgical resection, the type of neck dissection and choice of flap reconstruction was at the discretion of the surgical team. The functional outcomes, operative time and characteristics of both platysma and submental flaps were compared and the statistical tests of significance were applied accordingly. Results: The mean age was 60 years. The complex facial defects of 30 patients were reconstructed with platysma flap and of 35 patients with submental flap. Mean operation time of submental flap including flap harvesting (5.58±1.96hrs) was shorter than platysma flap (6.2±1.4hrs). The majority of the flaps (88-93%) were taken successfully in both groups. Submental flap was associated with significantly higher patients’ satisfaction regarding acceptable functional outcomes (p-value 0.027). The mean reduction in mouth opening was significantly smaller in platysma group (0.37 ±0.18cms) than the submental group (0.47±0.16). Conclusion: This study demonstrates that both platysma and submental flap techniques can be used for the reconstruction of complex facial defects with the acceptable functional outcome. The platysma flap can be harvested to medium size defects up to 70cm2 with good mouth opening. The submental flap is simpler, faster with a wider range of application and more acceptable functional outcomes.
Combining and analysing sensitive data from multiple sources offers considerable potential for knowledge discovery. However, there are a number of issues that pose problems for such analyses, including technical barriers, privacy restrictions, security concerns, and trust issues. Privacy-preserving distributed data mining techniques (PPDDM) aim to overcome these challenges by extracting knowledge from partitioned data while minimizing the release of sensitive information. This paper reports the results and findings of a systematic review of PPDDM techniques from 231 scientific articles published in the past 20 years. We summarize the state of the art, compare the problems they address, and identify the outstanding challenges in the field. This review identifies the consequence of the lack of standard criteria to evaluate new PPDDM methods and proposes comprehensive evaluation criteria with 10 key factors. We discuss the ambiguous definitions of privacy and confusion between privacy and security in the field, and provide suggestions of how to make a clear and applicable privacy description for new PPDDM techniques. The findings from our review enhance the understanding of the challenges of applying theoretical PPDDM methods to real-life use cases, and the importance of involving legal-ethical and social experts in implementing PPDDM methods. This comprehensive review will serve as a helpful guide to past research and future opportunities in the area of PPDDM.
A growing interest in synthetic data has stimulated the development and advancement of a large variety of deep generative models for a wide range of applications. However, as this research has progressed, its streams have become more specialized and disconnected from one another. This is why models for synthesizing text data for natural language processing cannot readily be compared to models for synthesizing health records anymore. To mitigate this isolation, we propose a data-driven evaluation framework for generative models for synthetic sequential data, an important and challenging sub-category of synthetic data, based on five high-level criteria: representativeness, novelty, realism, diversity and coherence of a synthetic data-set relative to the original data-set regardless of the models' internal structures. The criteria reflect requirements different domains impose on synthetic data and allow model users to assess the quality of synthetic data across models. In a critical review of generative models for sequential data, we examine and compare the importance of each performance criterion in numerous domains. We find that realism and coherence are more important for synthetic data natural language, speech and audio processing tasks. At the same time, novelty and representativeness are more important for healthcare and mobility data. We also find that measurement of representativeness is often accomplished using statistical metrics, realism by using human judgement, and novelty using privacy tests.
Developing personal data sharing tools and standards in conformity with data protection regulations is essential to empower citizens to control and share their health data with authorized parties for any purpose they approve. This can be, among others, for primary use in healthcare, or secondary use for research to improve human health and well-being. Ensuring that citizens are able to make fine-grained decisions about how their personal health data can be used and shared will significantly encourage citizens to participate in more health-related research. In this paper, we propose a ciTIzen-centric DatA pLatform (TIDAL) to give individuals ownership of their own data, and connect them with researchers to donate the use of their personal data for research while being in control of the entire data life cycle, including data access, storage and analysis. We recognize that most existing technologies focus on one particular aspect such as personal data storage, or suffer from executing data analysis over a large number of participants, or face challenges of low data quality and insufficient data interoperability. To address these challenges, the TIDAL platform integrates a set of components for requesting subsets of RDF (Resource Description Framework) data stored in personal data vaults based on SOcial LInked Data (Solid) technology and analyzing them in a privacy-preserving manner. We demonstrate the feasibility and efficiency of the TIDAL platform by conducting a set of simulation experiments using three different pod providers (Inrupt, Solidcommunity, Self-hosted Server). On each pod provider, we evaluated the performance of TIDAL by querying and analyzing personal health data with varying scales of participants and configurations. The reasonable total time consumption and a linear correlation between the number of pods and variables on all pod providers show the feasibility and potential to implement and use the TIDAL platform in practice. TIDAL facilitates individuals to access their personal data in a fine-grained manner and to make their own decision on their data. Researchers are able to reach out to individuals and send them digital consent directly for using personal data for health-related research. TIDAL can play an important role to connect citizens, researchers, and data organizations to increase the trust placed by citizens in the processing of personal data.
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