Hong Zhao’s research while affiliated with Northeastern University and other places

The health care domain is a knowledge-intensive domain. The quality of clinical diagnosis relies mainly on the medical knowledge and experience held by doctors. However, the ability of a single doctor is very limited, so the quality of clinical diagnosis is currently not high. In this paper, an aided diagnosis method based on domain semantic knowledge bases is proposed. Firstly, a domain semantic knowledge base is established by extracting and refining the knowledge of the medicine subject matter domain from the Freebase RDF dumps. Then, based on the semantic knowledge base, the algorithms for calculating the weights of the symptoms in the knowledge base, the relative weights of the diseases related to the input symptom set from a patient, and the related symptom set related to the input symptom set from the patient are proposed. Finally, the clinical medical record data of several common diseases are selected to make an evaluation on the proposed method. For each medical record, the symptom information is extracted from the chief complaint as the patient’s input symptom set. Based on the input symptom set, the method of this paper is used to obtain the list of related diseases and the ranking of disease relative weights. From the disease relevance rankings, the Top 1 (first diagnosis) and Top-3 (first 3 diagnoses) are compared with the doctor’s diagnoses in the medical records. Among them, ovarian cyst has the highest Top-1 and Top-3 hit rates of 67.3% and 89.1%, respectively. Followed by acute upper respiratory tract infection, Top-1 and Top-3 hit rates are 56.6% and 85.2%, respectively. The average Top-1 and Top-3 hit rates are 47.9% and 79.7%, respectively. Compared with the relevant methods, the method of this paper is better. The evaluation results show that based on the domain semantic knowledge base and the aided diagnosis method of diseases constructed in this paper, it is possible to provide aided diagnosis services of a large number of common diseases for general practitioners (especially inexperienced doctors) at the grass-roots level as well as self-diagnosis services of diseases for patients.

In the process of constructing a domain semantic knowledge base based on ontologies, reusing existing domain knowledge bases not only facilitates sharing, integration and reuse of the domain semantic knowledge base, but also can accelerate the construction of the domain semantic knowledge base. The open and fast growing Freebase database is a good data source which can be reused to construct the domain semantic knowledge base. However, extracting domain knowledge from the Freebase RDF dumps faces many challenges. For example, the dump package is too large to read or load; the dump package contains a lot of unnecessary and redundant facts; some ill-formed triples may cause the load to fail, and so on. In response to these obstacles and the deficiencies of existing research, this paper proposes a method to extract domain knowledge quickly, accurately and completely from the Freebase RDF dumps and describes the domain knowledge using the semantic constructs in ontology standard description languages. Taking extracting the ontology schema and instance data of the medicine domain, including the facts pointing to semantically related domains, as an example, the principle and implementation process of the method are explained in detail and the algorithms of the key processes are described. Finally, the method of this paper is evaluated, including the comparison and analysis of related methods with work objectives, software tools used, processing results, processing performance, accuracy, completeness, and reusability.

It is well-known that cloud computing has many potential advantages and many enterprise applications and data are migrating to public or hybrid cloud. But regarding some business-critical applications, the organizations, especially large enterprises, still wouldn't move them to cloud. The market size the cloud computing shared is still far behind the one expected. From the consumers' perspective, cloud computing security concerns, especially data security and privacy protection issues, remain the primary inhibitor for adoption of cloud computing services. This paper provides a concise but all-round analysis on data security and privacy protection issues associated with cloud computing across all stages of data life cycle. Then this paper discusses some current solutions. Finally, this paper describes future research work about data security and privacy protection issues in cloud.