Language-agnostic deep learning framework for automatic monitoring of population-level mental health from social networks.

In many countries, mental health issues are among the most serious public health concerns. National mental health statistics are frequently collected from reported patient cases or government-sponsored surveys, which have restricted coverage, frequency, and timeliness. Many domains of study, including public healthcare and biomedical informatics, have recently adopted social media data as a feasible real-time alternative to traditional methods of gathering representative information at the population level in a variety of contexts. However, because of the limits of fundamental natural language processing tools and labeled corpora in countries with limited natural language resources, such as Thailand, implementing social media systems to monitor mental health signals could be challenging. This paper presents LAPoMM, a novel framework for monitoring real-time mental health indicators from social media data without using labeled datasets in low-resource languages. Specifically, we use cross-lingual methods to train language-agnostic models and validate our framework by examining cross-correlations between the aggregate predicted mental signals and real-world administrative data from Thailand's Department of Mental Health, which includes monthly depression patients and reported cases of suicidal attempts. A combination of a language-agnostic representation and a deep learning classification model outperforms all other cross-lingual techniques for recognizing various mental signals in tweets, such as emotions, sentiments, and suicidal tendencies. The correlation analyses discover a strong positive relationship between actual depression cases and the predicted negative sentiment signals as well as suicide attempts and negative signals (e.g., fear, sadness, and disgust) and suicidal tendency. These findings establish the effectiveness of our proposed framework and its potential applications in monitoring population-level mental health using large-scale social media data. Furthermore, because the language-agnostic model utilized in the methodology is capable of supporting a wide range of languages, the proposed LAPoMM framework can be easily generalized for analogous applications in other countries with limited language resources.

Precision of Low-Cost Augmented Reality in Prefabricated Cutting Guide for Fibular Free Flap Surgery.

BACKGROUND: Augmented reality (AR) is an imaging technology encompassing an interactive experience of a real-world environment enhanced by computer-generated perceptual information. It has been introduced to current medical practice to help the preoperative planning in many surgical fields. METHODS: The authors applied AR to the computed tomography angiography of 8 patient's legs. Computed tomography angiography images were processed into Digital Imaging and communications in Medicine files to make a prefabricated cutting guide and customized titanium plate. Also, three-dimensional reconstruction of the arterial supply of the leg was performed to identify the perforators. RESULTS: Followed by preoperative marking of operative details on patient's skins in antero-posterior view, lateral view, and combination of both views. Inaccuracy of measurement was confirmed by duplex ultrasound which average error of the combination of antero-posterior and lateral viewed of both legs was lowest (0.7 ± 0.2 cm). Followed by lateral view (1.0 ±â€Š0.3 cm) and antero-posterior view (1.2 ±â€Š0.4 cm), respectively. CONCLUSIONS: Augmented reality can improve patient's safety by directly locate the perforator and easily to design the skin paddle. Followed by satisfaction and confidence in patients and their relatives. Augmented reality also promoted understanding of operative steps for related assistants, residents, or fellows. Augmented reality can perform with existing equipment, mobile phone application, and can save the cost for preoperative planning. Distortion in the depth view can be more accurate by combining of AR in antero-posterior and lateral view.