An automatic key medical information generating model. Issue 4 (December 2016)

• Record Type: Journal Article
• Authors: Yang, Shih-Ting
• Is Part Of: Health policy and technology. Volume 5:Issue 4(2016)
• Page Start: 389
• Page End: 413
• Publication Date: 2016-12
• Subjects: Data mining -- Knowledge management -- Clustering -- Medical information -- Medical knowledge; Medical policy -- Periodicals; Medical technology -- Periodicals; Medical policy; Medical technology; Health Policy -- Periodicals; Biomedical Technology -- Periodicals; Technology Assessment, Biomedical -- Periodicals; Periodicals; 362.105
• DOI: 10.1016/j.hlpt.2016.07.006
• Languages: English
• ISSNs: 2211-8837

Abstract

Abstract: Present-day society shows keen interest in the field of medical treatment, and the diagnostic mode is now developing toward doctor–patient shared decision-making. Therefore, a patient׳s source of medical information is quite important, with that source needing to be reliable, accurate, and easily accessible. Ensuring that informational sources meet these requirements becomes a challenge with the development of the informational network, which causes the amount of material available online to steadily increase and the general public to become more aware of health- and medical-treatment-related information. Therefore, focusing on the medical information seeker, this paper will discuss two user identities: patients and healthcare professionals. For patients, online medical articles are a major source of medical information; patients with concerns about diseases often search for their symptoms on the Internet and look for related medical information. However, online medical articles are usually long, so patients sometimes self-diagnose their disease or determine the severity of their condition based on only part of an article or on limited, incomplete, or even inaccurate information in several articles related to the symptoms searched out. Consequently, patients may misdiagnose their condition or underestimate the severity or seriousness of the condition and delay treatment. In addition, present medical technology advances rapidly, so physicians and other healthcare professionals must obtain the latest medical information from the Internet. However, searching for and reading professional in-depth medical articles to find required, critical information online is time-consuming, creating a time-management challenge. To address these aforementioned problems, this paper develops an Automatic Key Medical Information Generating model, uses medical articles as the basis of analysis, and develops and designs a medical article key-information-generating methodology applicable to medical article retrieval and reading. The word segmentation is implemented for the articles according to the Chinese Knowledge and Information Processing (CKIP) of Academia Sinica, and the medical articles are then distributed to various clusters by the clustering technology of this model, so that the medical information seeker can conduct a rapid search for the required medical article information. When the medical information seeker finds the target medical article, the article׳s key statements are screened out by the keywords rule base created in this paper, and the key statement scores are calculated. The medical article key information is sequenced according to the key statements so as to generate the medical article key information table. In addition, a web-based key-medical-information-generating system will be built based on the proposed model, and the effectiveness and feasibility of the model and technology will be evaluated using a real-world case. In summary, this paper presents a model to analyze the keywords and key statements of medical articles to generate a medical article key information table. This model can help the medical information seeker look for the required health information rapidly and accurately on the Internet, shortening the time for screening medical information and increasing the probability of obtaining the required information. Highlights: This study is to generate medical article key info. for doctors and patients. A solution to enhance doctor-patient shared decision-making is provided. A key medical info. generating system is developed based on proposed model. A real-world case is presented to examine applicability of proposed model/system. Testing and verifying the performance of the model and system is stable and good.