Breakthrough infections, hospital admissions, and mortality after major COVID-19 vaccination profiles: A prospective cohort study.

Background: Several COVID-19 vaccination rollout strategies are implemented. Real-world data from the large-scale, government-mandated Central Vaccination Center (CVC), Thailand, could be used for comparing the breakthrough infection, across all available COVID-19 vaccination profiles.

Methods: This prospective cohort study combined the vaccine profiles from the CVC registry with three nationally validated outcome datasets to assess the breakthrough COVID-19 infection, hospitalization, and death among Thais individuals who received at least one dose of the COVID-19 vaccine.

Selecting the Most Important Features for Predicting Mild Cognitive Impairment from Thai Verbal Fluency Assessments.

Mild cognitive impairment (MCI) is an early stage of cognitive decline or memory loss, commonly found among the elderly. A phonemic verbal fluency (PVF) task is a standard cognitive test that participants are asked to produce words starting with given letters, such as "F" in English and "ก" /k/ in Thai. With state-of-the-art machine learning techniques, features extracted from the PVF data have been widely used to detect MCI.

Using Automatic Speech Recognition to Assess Thai Speech Language Fluency in the Montreal Cognitive Assessment (MoCA).

The Montreal cognitive assessment (MoCA), a widely accepted screening tool for identifying patients with mild cognitive impairment (MCI), includes a language fluency test of verbal functioning; its scores are based on the number of unique correct words produced by the test taker. However, it is possible that unique words may be counted differently for various languages. This study focuses on Thai as a language that differs from English in terms of word combinations.

Design and evaluation of nonverbal sound-based input for those with motor handicapped.

Most personal computing interfaces rely on the users' ability to use their hand and arm movements to interact with on-screen graphical widgets via mainstream devices, including keyboards and mice. Without proper assistive devices, this style of input poses difficulties for motor-handicapped users. We propose a sound-based input scheme enabling users to operate Windows' Graphical User Interface by producing hums and fricatives through regular microphones.