Hong Sun

Hong Sun, Jos De Roo, Marc Twagirumukiza et al.

The Simple Knowledge Organization System (SKOS) is popular for expressing controlled vocabularies, such as taxonomies, classifications, etc., for their use in Semantic Web applications. Using SKOS, concepts can be linked to other concepts and organized into hierarchies inside a single terminology system. Meanwhile, expressing mappings between concepts in different terminology systems is also possible. This paper discusses potential quality issues in using SKOS to express these terminology mappings. Problematic patterns are defined and corresponding rules are developed to automatically detect situations where the mappings either result in 'SKOS Vocabulary Hijacking' to the source vocabularies or cause conflicts. An example of using the rules to validate sample mappings between two clinical terminologies is given. The validation rules, expressed in N3 format, are available as open source.

Hong Sun, Vincenzo De Florio

The population of the elderly people has kept increasing rapidly over the world in the past decades. Solutions that are able to effectively support the elderly people to live independently at their home are thus urgently needed. Ambient assisted living (AAL) aims to provide products and services with ambient intelligence to build a safe environment around people in need. With the high prevalence of multiple chronic diseases, the elderly people often need different levels of care management to prolong independent living at home. An effective AAL system should provide the required clinical support as an extension to the services provided in hospitals. Following the rapid growth of available data, together with the wide application of machine learning technologies, we are now able to build intelligent ambient assisted systems to fulfil such a request. This paper discusses different levels of intelligence in AAL. We also introduce our solution for building an intelligent AAL system with the discussed technologies. Taking semantic web technology as its backbone, such an AAL system is able to aggregate information from different sources, solve the semantic gap between different data sources, and perform adaptive and personalized carepath management based on the ambient environment.

Hong Sun, Kristof Depraetere, Laurent Meesseman et al.

Objective: Machine learning algorithms are now widely used in predicting acute events for clinical applications. While most of such prediction applications are developed to predict the risk of a particular acute event at one hospital, few efforts have been made in extending the developed solutions to other events or to different hospitals. We provide a scalable solution to extend the process of clinical risk prediction model development of multiple diseases and their deployment in different Electronic Health Records (EHR) systems. Materials and Methods: We defined a generic process for clinical risk prediction model development. A calibration tool has been created to automate the model generation process. We applied the model calibration process at four hospitals, and generated risk prediction models for delirium, sepsis and acute kidney injury (AKI) respectively at each of these hospitals. Results: The delirium risk prediction models achieved area under the receiver-operating characteristic curve (AUROC) ranging from 0.82 to 0.95 over different stages of a hospital stay on the test datasets of the four hospitals. The sepsis models achieved AUROC ranging from 0.88 to 0.95, and the AKI models achieved AUROC ranging from 0.85 to 0.92. Discussion: The scalability discussed in this paper is based on building common data representations (syntactic interoperability) between EHRs stored in different hospitals. Semantic interoperability, a more challenging requirement that different EHRs share the same meaning of data, e.g. a same lab coding system, is not mandated with our approach. Conclusions: Our study describes a method to develop and deploy clinical risk prediction models in a scalable way. We demonstrate its feasibility by developing risk prediction models for three diseases across four hospitals.

Yiren Chen, Yaming Yang, Hong Sun et al.

Large-scale pre-trained models have attracted extensive attention in the research community and shown promising results on various tasks of natural language processing. However, these pre-trained models are memory and computation intensive, hindering their deployment into industrial online systems like Ad Relevance. Meanwhile, how to design an effective yet efficient model architecture is another challenging problem in online Ad Relevance. Recently, AutoML shed new lights on architecture design, but how to integrate it with pre-trained language models remains unsettled. In this paper, we propose AutoADR (Automatic model design for AD Relevance) -- a novel end-to-end framework to address this challenge, and share our experience to ship these cutting-edge techniques into online Ad Relevance system at Microsoft Bing. Specifically, AutoADR leverages a one-shot neural architecture search algorithm to find a tailored network architecture for Ad Relevance. The search process is simultaneously guided by knowledge distillation from a large pre-trained teacher model (e.g. BERT), while taking the online serving constraints (e.g. memory and latency) into consideration. We add the model designed by AutoADR as a sub-model into the production Ad Relevance model. This additional sub-model improves the Precision-Recall AUC (PR AUC) on top of the original Ad Relevance model by 2.65X of the normalized shipping bar. More importantly, adding this automatically designed sub-model leads to a statistically significant 4.6% Bad-Ad ratio reduction in online A/B testing. This model has been shipped into Microsoft Bing Ad Relevance Production model.

Hong Sun, Kristof Depraetere, Jos De Roo et al.

There is a growing need to semantically process and integrate clinical data from different sources for clinical research. This paper presents an approach to integrate EHRs from heterogeneous resources and generate integrated data in different data formats or semantics to support various clinical research applications. The proposed approach builds semantic data virtualization layers on top of data sources, which generate data in the requested semantics or formats on demand. This approach avoids upfront dumping to and synchronizing of the data with various representations. Data from different EHR systems are first mapped to RDF data with source semantics, and then converted to representations with harmonized domain semantics where domain ontologies and terminologies are used to improve reusability. It is also possible to further convert data to application semantics and store the converted results in clinical research databases, e.g. i2b2, OMOP, to support different clinical research settings. Semantic conversions between different representations are explicitly expressed using N3 rules and executed by an N3 Reasoner (EYE), which can also generate proofs of the conversion processes. The solution presented in this paper has been applied to real-world applications that process large scale EHR data.

Ning Gui, Vincenzo De Florio, Hong Sun et al.

Nowadays, more and more applications require OSGi to have some form of real-time support, which is currently very limited. The resulting closed-system solutions lack of a standard management scheme which forbids standard, system-wide policies for real-time system's deployment, adaptation, and reconfiguration. In order to tackle this problem, this paper proposes a declarative real-time component model. In this model, the distinguishing real-time contract of each component is declaratively described, and a general component real-time management interface is designed. They are used to maintain an accurate view of existing real-time components' promised contracts. A real-time component runtime service is designed to control the whole lifecycle of the components. By using global information and general control interface, it can adjust the system continue to operate without impairing the deployed components' real-time contracts in the face of run-time changes. This system allows itself to be easily extended with other constraint resolving policies to fit different context. The prototype has been tested into a simulated control system. The result shows this framework can provide good real time performance while still provides real-time component dynamicity support as well. To the best of our knowledge, this is the first comprehensive solution providing explicit real-time support from design to execution in OSGi framework.

Hong Sun, Vincenzo De Florio, Ning Gui et al.

The population of elderly people keeps increasing rapidly, which becomes a predominant aspect of our societies. As such, solutions both efficacious and cost-effective need to be sought. Ambient Assisted Living (AAL) is a new approach which promises to address the needs from elderly people. In this paper, we claim that human participation is a key ingredient towards effective AAL systems, which not only saves social resources, but also has positive relapses on the psychological health of the elderly people. Challenges in increasing the human participation in ambient assisted living are discussed in this paper and solutions to meet those challenges are also proposed. We use our proposed mutual assistance community, which is built with service oriented approach, as an example to demonstrate how to integrate human tasks in AAL systems. Our preliminary simulation results are presented, which support the effectiveness of human participation.