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Python爬虫技术在信息流行病学中的应用   总被引:1,自引:1,他引:0       下载免费PDF全文
Python网络爬虫技术是一种通过模拟用户的网络浏览行为以实现从网络中自动、大量提取信息的技术,是信息流行病学研究收集并整合多源异构信息数据的关键基础。Python网络爬虫可分为简单爬虫与大型爬虫,集数据采集与数据库构建于一体,语法简洁、灵活性高、学习成本低、维护成本低。它适用于信息流行病学的各种应用场景,通过对互联网中健康相关信息的分析,实现多种公共卫生监测、健康干预实施及效果评价、智慧寻医方略优化等目标。近年,我国政府开始鼓励对含互联网信息在内的多源大数据的整合利用,在此背景下,Python爬虫技术的应用场景势必会越来越多,相应的人才培养、技术革新建议纳入到公共卫生教育和科研体系之中。  相似文献   
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《Vaccine》2020,38(34):5430-5435
BackgroundHealth-seeking behaviors change during pandemics and may increase with regard to illnesses with symptoms similar to the pandemic. The global reaction to COVID-19 may drive interest in vaccines for other diseases.ObjectivesOur study investigated the correlation between global online interest in COVID-19 and interest in CDC-recommended routine vaccines.Design, settings, measurementsThis infodemiology study used Google Trends data to quantify worldwide interest in COVID-19 and CDC-recommended vaccines using the unit search volume index (SVI), which estimates volume of online search activity relative to highest volume of searches within a specified period. SVIs from December 30, 2019 to March 30, 2020 were collected for “coronavirus (Virus)” and compared with SVIs of search terms related to CDC-recommended adult vaccines. To account for seasonal variation, we compared SVIs from December 30, 2019 to March 30, 2020 with SVIs from the same months in 2015 to 2019. We performed country-level analyses in ten COVID-19 hotspots and ten countries with low disease burden.ResultsThere were significant positive correlations between SVIs for “coronavirus (Virus)” and search terms for pneumococcal (R = 0.89, p < 0.0001) and influenza vaccines (R = 0.93, p < 0.0001) in 2020, which were greater than SVIs for the same terms in 2015–2019 (p = 0.005, p < 0.0001, respectively). Eight in ten COVID-19 hotspots demonstrated significant positive correlations between SVIs for coronavirus and search terms for pneumococcal and influenza vaccines.LimitationsSVIs estimate relative changes in online interest and do not represent the interest of people with no Internet access.ConclusionA peak in worldwide interest in pneumococcal and influenza vaccines coincided with the COVID-19 pandemic in February and March 2020. Trends are likely not seasonal in origin and may be driven by COVID-19 hotspots. Global events may change public perception about the importance of vaccines. Our findings may herald higher demand for pneumonia and influenza vaccines in the upcoming season.  相似文献   
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Background

Twitter provides various types of location data, including exact Global Positioning System (GPS) coordinates, which could be used for infoveillance and infodemiology (ie, the study and monitoring of online health information), health communication, and interventions. Despite its potential, Twitter location information is not well understood or well documented, limiting its public health utility.

Objective

The objective of this study was to document and describe the various types of location information available in Twitter. The different types of location data that can be ascertained from Twitter users are described. This information is key to informing future research on the availability, usability, and limitations of such location data.

Methods

Location data was gathered directly from Twitter using its application programming interface (API). The maximum tweets allowed by Twitter were gathered (1% of the total tweets) over 2 separate weeks in October and November 2011. The final dataset consisted of 23.8 million tweets from 9.5 million unique users. Frequencies for each of the location options were calculated to determine the prevalence of the various location data options by region of the world, time zone, and state within the United States. Data from the US Census Bureau were also compiled to determine population proportions in each state, and Pearson correlation coefficients were used to compare each state’s population with the number of Twitter users who enable the GPS location option.

Results

The GPS location data could be ascertained for 2.02% of tweets and 2.70% of unique users. Using a simple text-matching approach, 17.13% of user profiles in the 4 continental US time zones were able to be used to determine the user’s city and state. Agreement between GPS data and data from the text-matching approach was high (87.69%). Furthermore, there was a significant correlation between the number of Twitter users per state and the 2010 US Census state populations (r ≥ 0.97, P < .001).

Conclusions

Health researchers exploring ways to use Twitter data for disease surveillance should be aware that the majority of tweets are not currently associated with an identifiable geographic location. Location can be identified for approximately 4 times the number of tweets using a straightforward text-matching process compared to using the GPS location information available in Twitter. Given the strong correlation between both data gathering methods, future research may consider using more qualitative approaches with higher yields, such as text mining, to acquire information about Twitter users’ geographical location.  相似文献   
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Several different terms have been used to describe “psychogenic nonepileptic seizures” (PNES) in the literature. In this study, we evaluated the most common English terms used to describe PNES on Google and in PubMed using multiple search terms ( https://www.google.com and http://www.ncbi.nlm.nih.gov/pubmed ). The information prevalence of the five terms most frequently used to refer to PNES in PubMed were: psychogenic non(‐)epileptic seizure(s), followed by pseudo(‐)seizure(s), non(‐)epileptic seizure(s), psychogenic seizure(s), and non(‐)epileptic event(s). The five most frequently adopted terms to describe PNES in Google were: psychogenic non(‐)epileptic seizure(s), followed by non(‐)epileptic event(s), psychogenic attack(s), non(‐)epileptic attack(s), and psychogenic non(‐)epileptic attack(s). The broad spectrum of synonyms used to refer to PNES in the medical literature reflects a lack of internationally accepted, uniform terminology for PNES. In addition to “seizure(s),” lay people use the word “attack(s)” to describe PNES. Although considered obsolete, some terms, e.g., pseudoseizure(s), are still used in the recent medical literature. Adopting a uniform terminology to describe PNES could facilitate communication between epileptologists, physicians without specific expertise in epilepsy, and patients.  相似文献   
5.
BackgroundWith headache experienced by up to 75% of adults worldwide in the last year, primary headache disorders constitute a major public health problem, yet they remain under-diagnosed and under-treated.Headache prevalence and burden is changing as society evolves, with headache now occurring earlier in life. Contributing factors, mostly associated with changing life style, such as stress, bad posture, physical inactivity, sleep disturbance, poor diet and excess use of digital technology may be associated with the phenomenon that could be labelled as ‘21st century headache’. This is especially notable in workplace and learning environments where headache impacts mental clarity and therefore cognitive performance. The headache-related impact on productivity and absenteeism negatively influences an individual’s behaviour and quality of life, and is also associated with a high economic cost. Since the majority of sufferers opt to self-treat rather than seek medical advice, substantial knowledge on headache prevalence, causation and burden is unknown globally. Mapping the entire population of headache sufferers can close this knowledge gap, leading to better headache management. The broad use of digital technology to gather real world data on headache triggers, burden and management strategies, in self-treated population will allow these sufferers to access appropriate support and medication, and therefore improve quality of life.ConclusionThese data can yield important insights into a substantial global healthcare issue and form the basis for improved patient awareness, professional education, clinical study design and drug development.  相似文献   
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