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an open service, we foster the development of third-party applications or services and provide motivational technological support in several projects crossing different domains of interest.
Information related to patient medication is crucial for health care; however, up to 80% of the information resides solely in unstructured text. Manual extraction is difficult and time-consuming, and there is not a lot of research on natural language processing extracting medical information from unstructured text from French corpora.

We aimed to develop a system to extract medication-related information from clinical text written in French.

We developed a hybrid system combining an expert rule-based system, contextual word embedding (embedding for language model) trained on clinical notes, and a deep recurrent neural network (bidirectional long short term memory-conditional random field). The task consisted of extracting drug mentions and their related information (eg, dosage, frequency, duration, route, condition). We manually annotated 320 clinical notes from a French clinical data warehouse to train and evaluate the model. We compared the performance of our approach to those of standard approaches rule-based or machine learning only and classic word embeddings. We evaluated the models using token-level recall, precision, and F-measure.

The overall F-measure was 89.9% (precision 90.8; recall 89.2) when combining expert rules and contextualized embeddings, compared to 88.1% (precision 89.5; recall 87.2) without expert rules or contextualized embeddings. The F-measures for each category were 95.3% for medication name, 64.4% for drug class mentions, 95.3% for dosage, 92.2% for frequency, 78.8% for duration, and 62.2% for condition of the intake.

Associating expert rules, deep contextualized embedding, and deep neural networks improved medication information extraction. Our results revealed a synergy when associating expert knowledge and latent knowledge.
Associating expert rules, deep contextualized embedding, and deep neural networks improved medication information extraction. Our results revealed a synergy when associating expert knowledge and latent knowledge.
Social media are important for monitoring perceptions of public health issues and for educating target audiences about health; however, limited information about the demographics of social media users makes it challenging to identify conversations among target audiences and limits how well social media can be used for public health surveillance and education outreach efforts. Certain social media platforms provide demographic information on followers of a user account, if given, but they are not always disclosed, and researchers have developed machine learning algorithms to predict social media users' demographic characteristics, mainly for Twitter. To date, there has been limited research on predicting the demographic characteristics of Reddit users.

We aimed to develop a machine learning algorithm that predicts the age segment of Reddit users, as either adolescents or adults, based on publicly available data.

This study was conducted between January and September 2020 using publicly available Reddit pe prediction algorithm with competitive accuracy using publicly available data, suggesting machine learning methods can help public health agencies identify age-related target audiences on Reddit. Our results also suggest that there are characteristics of Reddit users' posting behavior, linguistic patterns, and account features that distinguish adolescents from adults.
Researchers developing personal health tools employ a range of approaches to involve prospective users in design and development.

The aim of this paper was to develop a validated measure of the human- or user-centeredness of design and development processes for personal health tools.

We conducted a psychometric analysis of data from a previous systematic review of the design and development processes of 348 personal health tools. Using a conceptual framework of user-centered design, our team of patients, caregivers, health professionals, tool developers, and researchers analyzed how specific practices in tool design and development might be combined and used as a measure. We prioritized variables according to their importance within the conceptual framework and validated the resultant measure using principal component analysis with Varimax rotation, classical item analysis, and confirmatory factor analysis.

We retained 11 items in a 3-factor structure explaining 68% of the variance in the data. The Cr requiring people to adapt to tools.
The User-Centered Design 11-item measure (UCD-11) may be used to quantitatively document the user/human-centeredness of design and development processes of patient-centered tools. By building an evidence base about such processes, we can help ensure that tools are adapted to people who will use them, rather than requiring people to adapt to tools.
Filling a prescription on the web has become an alternative to in-person pharmacies for individuals to access their medications. However, the adoption of web-based filling has been gradual, and the use patterns remain to be unclear.

This study aims to estimate the trend and prevalence of web-based prescription-filling behavior and identify associated factors among adults in the United States.

We used data from the US National Health Interview Survey (NHIS) from 2009 to 2018. Selleck SGI-1027 Adult respondents (aged ≥18 years and over) self-reported their behavior of web-based prescription filling, which was defined as having filled a prescription using the internet in the past 12 months during the survey year. We reported trends using weighted percentages adjusted by the NHIS complex sampling design. We used descriptive statistics and multivariable logistic regression models to examine trends and identify factors associated with web-based prescription-filling behavior.

The estimated number of adults reporting web-based prescription-filling behavior significantly increased from 13,319,877 (13,319,877/225,217,942, 5.91%) in 2009 to 28,308,262 (28,308,262/246,611,125, 11.48%) in 2018 (P<.001). Those who were more likely to report filling a prescription on the web were aged between 35 and 74 years, female, White, and frequent users of the computer or internet; these adults also reported higher education, higher income, insurance coverage, and poorer health status.

Web-based prescription-filling behavior among US adults has increased significantly from 2009 to 2018. Health care providers should be aware of the upward trend in the use of web-based pharmacies and ensure the clinical safety of web-based prescriptions.
Web-based prescription-filling behavior among US adults has increased significantly from 2009 to 2018. Health care providers should be aware of the upward trend in the use of web-based pharmacies and ensure the clinical safety of web-based prescriptions.
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