Effective medical resources searching using an ontology‐driven medical information retrieval system

Yi, Myongho

doi:10.1108/02640471211221368

Cited by 9 publications

(3 citation statements)

References 14 publications

(13 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They used the data of H1N1 2009 outbreak in China University and examined the social network, student behavior, population distribution and contact patterns of the virus. In 2012, Yi [25] proposed an ontology driven approach in searching medical databases for case studies in H1N1 outbreaks. In 2011, Bajardi et al [26] studied the impact of travel restriction during H1N1 2009 epidemic.…”

Section: Ict and Mathematical Models In H1n1 Epidemicmentioning

confidence: 99%

Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing

Sandhu

Gill

Sood

2016

Journal of Computational Science

View full text Add to dashboard Cite

a b s t r a c t H1N1 is an infectious virus which, when spread affects a large volume of the population. It is an airborne disease that spreads easily and has a high death rate. Development of healthcare support systems using cloud computing is emerging as an effective solution with the benefits of better quality of service, reduced costs and flexibility. In this paper, an effective cloud computing architecture is proposed which predicts H1N1 infected patients and provides preventions to control infection rate. It consists of four processing components along with secure cloud storage medical database. The random decision tree is used to initially assess the infection in any patient depending on his/her symptoms. Social Network Analysis (SNA) is used to present the state of the outbreak. The proposed architecture is tested on synthetic data generated for two million users. The system provided 94% accuracy for the classification and around 81% of the resource utilization on Amazon EC2 cloud. The key point of the paper is the use of SNA graphs to calculate role of an infected user in spreading the outbreak known as Outbreak Role Index (ORI). It will help government agencies and healthcare departments to present, analyze and prevent outbreak effectively.

show abstract

Section: Ict and Mathematical Models In H1n1 Epidemicmentioning

confidence: 99%

Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing

Sandhu

Gill

Sood

2016

Journal of Computational Science

View full text Add to dashboard Cite

show abstract

“…Nowadays, ontologies are extensively adapted for a variety of applications, especially information retrieval objectives. In pioneer aspects, such as in medical domains, the development of new ontologies and continuous improvement of available ones is diligently pursued by research communities (Yi, 2012). However, the progressive expansion of knowledge boundaries and the accelerated growth of new science necessitates effective and easy-to-implement ontology updating schemes.…”

Section: Introductionmentioning

confidence: 99%

Semi-automated ontology development scheme via text mining of scientific records

Tamjid,

Nooshinfard,

Hosseini Beheshti

et al. 2024

View full text Add to dashboard Cite

Purpose The purpose of this study is to develop a domain independent, cost-effective, time-saving and semi-automated ontology generation framework that could extract taxonomic concepts from unstructured text corpus. In the human disease domain, ontologies are found to be extremely useful for managing the diversity of technical expressions in favour of information retrieval objectives. The boundaries of these domains are expanding so fast that it is essential to continuously develop new ontologies or upgrade available ones. Design/methodology/approach This paper proposes a semi-automated approach that extracts entities/relations via text mining of scientific publications. Text mining-based ontology (TmbOnt)-named code is generated to assist a user in capturing, processing and establishing ontology elements. This code takes a pile of unstructured text files as input and projects them into high-valued entities or relations as output. As a semi-automated approach, a user supervises the process, filters meaningful predecessor/successor phrases and finalizes the demanded ontology-taxonomy. To verify the practical capabilities of the scheme, a case study was performed to drive glaucoma ontology-taxonomy. For this purpose, text files containing 10,000 records were collected from PubMed. Findings The proposed approach processed over 3.8 million tokenized terms of those records and yielded the resultant glaucoma ontology-taxonomy. Compared with two famous disease ontologies, TmbOnt-driven taxonomy demonstrated a 60%–100% coverage ratio against famous medical thesauruses and ontology taxonomies, such as Human Disease Ontology, Medical Subject Headings and National Cancer Institute Thesaurus, with an average of 70% additional terms recommended for ontology development. Originality/value According to the literature, the proposed scheme demonstrated novel capability in expanding the ontology-taxonomy structure with a semi-automated text mining approach, aiming for future fully-automated approaches.

show abstract

“…Much work has been done to combine ontology with knowledge base to construct sharable, reusable and reasoning knowledge base, such as Unified Medical Language System (UMLS [9]) and Ontology-based Medical Information Retrieval System (OMIR [10]). However, ontologies constructed for query and classifying of medical terms, as is the case of most existing medical ontologies, are of little use in text mining.…”

Section: Introductionmentioning

confidence: 99%

An ontology-based approach for text mining of stroke electronic medical records

Yang

Cai

Luo

et al. 2013

2013 IEEE International Conference on Bioinformatics and Biomedicine

View full text Add to dashboard Cite

In this paper, we propose a novel ontology-based approach for text mining of EMR information retrieval. The advantage of this approach is that it is capable of handling numerous variations in nature text which essentially refer to the same identity, as well as inferring implicit information from the plain text, which are both important in data mining of medical records. We applied the approach to text mining of EMR documents for stroke patients in a Chinese medical hospital. A benchmark study on an independent test set shows that the proposed pipeline can accurately extract the vast majority of useful information from the EMR documents, including the implicit ones through ontology inference. We also carry out a primary statistical analysis on a sample EMR set to illustrate the utilization of the approach on medical studies.

show abstract

Effective medical resources searching using an ontology‐driven medical information retrieval system

Cited by 9 publications

References 14 publications

Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing

Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing

Semi-automated ontology development scheme via text mining of scientific records

An ontology-based approach for text mining of stroke electronic medical records

Contact Info

Product

Resources

About