Long-term effectiveness of an E-based survivorship care plan for breast cancer survivors: A quasi-experimental study.

OBJECTIVE: The purpose of this study was to evaluate the effect of a web-based survivorship care plan (SCP) computerized application (APP): (SCP-A) on women's unmet needs, fear of recurrence, symptom distress, anxiety, depression, and quality of life (QoL). METHODS: Women diagnosed with breast cancer, who had completed their primary treatment but less than 5 years without a sign of recurrence (N = 165) were randomized to a SCP-A or a control group. Self-reported questionnaires were completed by the both groups at baseline (T0), 5 weeks (T1), 3 months (T2), 6 months (T3), and 12 months (T4). RESULTS: Controlling for relevant covariates, mixed effect model analyses revealed a significant decrease in women in the SCP-A group compared to the control group for total unmet needs since T3 (p < .004) and fear of recurrence since T4 (p = .02). Women in the SCP-A group also reported significant improvements in QoL at T4 (p < .001) relative to those in the control group. CONCLUSION: Providing SCP using an information website application for women with breast cancer can decrease unmet needs, fear of recurrence, and improve quality of life during short-term and long-term use. PRACTICE IMPLICATIONS: Web-based information that provides survivorship care plans for breast cancer survivors are beneficial.

Analyzing depression tendency of web posts using an event-driven depression tendency warning model.

OBJECTIVE: The Internet has become a platform to express individual moods/feelings of daily life, where authors share their thoughts in web blogs, micro-blogs, forums, bulletin board systems or other media. In this work, we investigate text-mining technology to analyze and predict the depression tendency of web posts. METHODS: In this paper, we defined depression factors, which include negative events, negative emotions, symptoms, and negative thoughts from web posts. We proposed an enhanced event extraction (E3) method to automatically extract negative event terms. In addition, we also proposed an event-driven depression tendency warning (EDDTW) model to predict the depression tendency of web bloggers or post authors by analyzing their posted articles. RESULTS: We compare the performance among the proposed EDDTW model, negative emotion evaluation (NEE) model, and the diagnostic and statistical manual of mental disorders-based depression tendency evaluation method. The EDDTW model obtains the best recall rate and F-measure at 0.668 and 0.624, respectively, while the diagnostic and statistical manual of mental disorders-based method achieves the best precision rate of 0.666. The main reason is that our enhanced event extraction method can increase recall rate by enlarging the negative event lexicon at the expense of precision. Our EDDTW model can also be used to track the change or trend of depression tendency for each post author. The depression tendency trend can help doctors to diagnose and even track depression of web post authors more efficiently. CONCLUSIONS: This paper presents an E3 method to automatically extract negative event terms in web posts. We also proposed a new EDDTW model to predict the depression tendency of web posts and possibly help bloggers or post authors to early detect major depressive disorder.

Overcoming terminology barrier using Web resources for cross-language medical information retrieval.

A number of authoritative medical websites, such as PubMed and MedlinePlus, provide consumers with the most up-to-date health information. However, non-English speakers often encounter not only language barriers (from other languages to English) but also terminology barriers (from laypersons inverted exclamation mark| terms to professional medical terms) when retrieving information from these websites. Our previous work address language barriers by developing a multilingual medical thesaurus, Chinese-English MeSH, while this study presents an approach to overcome terminology barriers based on Web resources. Two techniques were utilized in our approach: monolingual concept mapping using approximate string matching and crosslingual concept mapping using Web resources. The evaluation shows that our approach can significantly improve the performance on MeSH concept mapping and cross-language medical information retrieval.

Semi-automatic construction of the Chinese-English MeSH using Web-based term translation method.

Due to language barrier, non-English users are unable to retrieve the most updated medical information from the U.S. authoritative medical websites, such as PubMed and MedlinePlus. A few cross-language medical information retrieval (CLMIR) systems have been utilizing MeSH (Medical Subject Heading) with multilingual thesaurus to bridge the gap. Unfortunately, MeSH has yet not been translated into traditional Chinese currently. We proposed a semi-automatic approach to constructing Chinese-English MeSH based on Web-based term translation. The system provides knowledge engineers with candidate terms mining from anchor texts and search-result pages. The result is encouraging. Currently, more than 19,000 Chinese-English MeSH entries have been complied. This thesaurus will be used in Chinese-English CLMIR in the future.