Evaluating Web-Based Automatic Transcription for Alzheimer Speech Data: Transcript Comparison and Machine Learning Analysis.

Background: Speech data for medical research can be collected noninvasively and in large volumes. Speech analysis has shown promise in diagnosing neurodegenerative disease. To effectively leverage speech data, transcription is important, as there is valuable information contained in lexical content.

Care Needs of Adolescents and Young Adults with Cancer Undergoing Active Treatment in South Korea: A Mixed Methods Study.

Adolescents and young adults (AYAs) with cancer have special care needs that are different from those of children and older adults with cancer. This study assessed the perspective and experience of AYAs with cancer in South Korea to identify their care needs. This study used a convergent mixed-methods design.

Tracking Public Attitudes Toward COVID-19 Vaccination on Tweets in Canada: Using Aspect-Based Sentiment Analysis.

Background: The development and approval of COVID-19 vaccines have generated optimism for the end of the COVID-19 pandemic and a return to normalcy. However, vaccine hesitancy, often fueled by misinformation, poses a major barrier to achieving herd immunity.

Objective: We aim to investigate Twitter users' attitudes toward COVID-19 vaccination in Canada after vaccine rollout.

Classification of Alzheimer's Disease Leveraging Multi-task Machine Learning Analysis of Speech and Eye-Movement Data.

Alzheimer's disease (AD) is a progressive neurodegenerative condition that results in impaired performance in multiple cognitive domains. Preclinical changes in eye movements and language can occur with the disease, and progress alongside worsening cognition. In this article, we present the results from a machine learning analysis of a novel multimodal dataset for AD classification.

Identifying Language Features Associated With Needs of Ovarian Cancer Patients and Caregivers Using Social Media.

Background: Online health communities (OHCs) can be a source for clinicians to learn the needs of cancer patients and caregivers. Ovarian cancer (OvCa) patients and caregivers deal with a wide range of unmet needs, many of which are expressed in OHCs. An automated need classification model could help clinicians more easily understand and prioritize information available in the OHCs.

Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis.

Background: Social media is a rich source where we can learn about people's reactions to social issues. As COVID-19 has impacted people's lives, it is essential to capture how people react to public health interventions and understand their concerns.

Objective: We aim to investigate people's reactions and concerns about COVID-19 in North America, especially in Canada.

Factors Affecting the Timing of a Central Line Associated Bloodstream Infection Onset in Children with Cancer.

Purpose: The purpose of this study is to identify controllable treatment-environment-related factors affecting the timing of a central line-associated bloodstream infection (CLABSI) onset in children with cancer with central venous catheters (CVC).

Design: This study is a secondary data analysis with the data extracted from electronic medical records in a tertiary hospital in South Korea. This study was conducted by reviewing electronic medical records of 470 pediatric cancer patients younger than the age of 18 years from 2010 to 2016.

Modeling Dialogue Acts with Content Word Filtering and Speaker Preferences.

We present an unsupervised model of dialogue act sequences in conversation. By modeling topical themes as transitioning more slowly than dialogue acts in conversation, our model de-emphasizes content-related words in order to focus on conversational function words that signal dialogue acts. We also incorporate speaker tendencies to use some acts more than others as an additional predictor of dialogue act prevalence beyond temporal dependencies.

Personalized healthcare through intelligent gadgets.

An intelligent gadget is a wearable platform which is reconfigurable, scalable, and component-based and which can be equipped, carried as a personal accessory, or in a certain case, implanted internally into a body. Various kinds of personal information can be gathered with intelligent gadgets, and that information is used to provide specially personalized services to people in the ubiquitous computing environment. In this paper, we show a personalized healthcare service through intelligent gadgets.

Semantic tagging for medical knowledge tracking.

We propose a semantic tagger that provides high level concept information for phrases in clinical documents, which enriches medical information tracking system that support decision making or quality assurance of medical treatment. In this paper, we have tried to deal with patient records written by doctors rather than well-formed documents such as Medline abstracts. In addition, annotating clinical text on phrases semantically rather than syntactically has been attempted, which are at higher level granularity than words that have been the target for most tagging work.