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

Purpose: 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. Methods: This study used a convergent mixed-methods design. From July 2020 to November 2021, AYAs with cancer (N = 77; 15-39 years of age) participated in a quantitative cross-sectional study, using a tool developed by our study team. In May 2021, a qualitative focus group was conducted with 10 AYAs with cancer. Integrated analyses were conducted concurrently by reporting the quantitative and qualitative findings together. Results: Quantitative findings revealed that the highest care need domains were communication and information, whereas the highest care priority item was the management of pain and symptoms occurring during the treatment. Qualitative findings revealed 12 themes across 5 domains. Comparing and merging of the quantitative and qualitative data resulted in eight confirmed themes and four expanded findings, including knowing people who overcame similar illnesses, fear of death, dedicated space, and a program for AYAs with cancer. Conclusion: When developing and implementing programs and health care services, especially in countries with no established program or cancer specialty unit for AYAs with cancer, it is important to consider the special care needs and priorities of AYAs with cancer. This mixed methods study provided empirical evidence to help understand and prioritize the needs of AYAs with cancer undergoing active treatment in South Korea.

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. Manual transcription, while highly accurate, limits the potential scalability and cost savings associated with language-based screening. OBJECTIVE: To better understand the use of automatic transcription for classification of neurodegenerative disease, namely, Alzheimer disease (AD), mild cognitive impairment (MCI), or subjective memory complaints (SMC) versus healthy controls, we compared automatically generated transcripts against transcripts that went through manual correction. METHODS: We recruited individuals from a memory clinic ("patients") with a diagnosis of mild-to-moderate AD, (n=44, 30%), MCI (n=20, 13%), SMC (n=8, 5%), as well as healthy controls (n=77, 52%) living in the community. Participants were asked to describe a standardized picture, read a paragraph, and recall a pleasant life experience. We compared transcripts generated using Google speech-to-text software to manually verified transcripts by examining transcription confidence scores, transcription error rates, and machine learning classification accuracy. For the classification tasks, logistic regression, Gaussian naive Bayes, and random forests were used. RESULTS: The transcription software showed higher confidence scores (P<.001) and lower error rates (P>.05) for speech from healthy controls compared with patients. Classification models using human-verified transcripts significantly (P<.001) outperformed automatically generated transcript models for both spontaneous speech tasks. This comparison showed no difference in the reading task. Manually adding pauses to transcripts had no impact on classification performance. However, manually correcting both spontaneous speech tasks led to significantly higher performances in the machine learning models. CONCLUSIONS: We found that automatically transcribed speech data could be used to distinguish patients with a diagnosis of AD, MCI, or SMC from controls. We recommend a human verification step to improve the performance of automatic transcripts, especially for spontaneous tasks. Moreover, human verification can focus on correcting errors and adding punctuation to transcripts. However, manual addition of pauses is not needed, which can simplify the human verification step to more efficiently process large volumes of speech data.

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. METHODS: We applied a weakly supervised aspect-based sentiment analysis (ABSA) technique, which involves the human-in-the-loop system, on COVID-19 vaccination-related tweets in Canada. Automatically generated aspect and opinion terms were manually corrected by public health experts to ensure the accuracy of the terms and make them more domain-specific. Then, based on these manually corrected terms, the system inferred sentiments toward the aspects. We observed sentiments toward key aspects related to COVID-19 vaccination, and investigated how sentiments toward "vaccination" changed over time. In addition, we analyzed the most retweeted or liked tweets by observing most frequent nouns and sentiments toward key aspects. RESULTS: After applying the ABSA system, we obtained 170 aspect terms (eg, "immunity" and "pfizer") and 6775 opinion terms (eg, "trustworthy" for the positive sentiment and "jeopardize" for the negative sentiment). While manually verifying or editing these terms, our public health experts selected 20 key aspects related to COVID-19 vaccination for analysis. The sentiment analysis results for the 20 key aspects revealed negative sentiments related to "vaccine distribution," "side effects," "allergy," "reactions," and "anti-vaxxer," and positive sentiments related to "vaccine campaign," "vaccine candidates," and "immune response." These results indicate that the Twitter users express concerns about the safety of vaccines but still consider vaccines as the option to end the pandemic. In addition, compared to the sentiment of the remaining tweets, the most retweeted or liked tweets showed more positive sentiment overall toward key aspects (P<.001), especially vaccines (P<.001) and vaccination (P=.009). Further investigation of the most retweeted or liked tweets revealed two opposing trends in Twitter users who showed negative sentiments toward vaccines: the "anti-vaxxer" population that used negative sentiments as a means to discourage vaccination and the "Covid Zero" population that used negative sentiments to encourage vaccinations while critiquing the public health response. CONCLUSIONS: Our study examined public sentiments toward COVID-19 vaccination on tweets over an extended period in Canada. Our findings could inform public health agencies to design and implement interventions to promote vaccination.

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. OBJECTIVE: The aim of this study was to use initial OHC postings to develop an automated model for the classification of OvCa patient and caregiver needs. METHODS: We collected data from the OvCa OHC and analyzed the initial postings of patients and caregivers (n = 853). Two annotators coded each posting with 12 types of needs. Then, we applied the machine learning approach with bag-of-words features to build a model to classify needs. F1 score, an indicator of model accuracy, was used to evaluate the model. RESULTS: The most reported needs were information, social, psychological/emotional, and physical. Thirty-nine percent of postings described information and social needs in the same posting. Our model reported a high level of accuracy for classifying those top needs. Psychological terms were important for classifying psychological/emotional and social needs. Medical terms were important for physical and information needs. CONCLUSIONS: We demonstrate the potential of using OHCs to supplement traditional needs assessment. Further research would incorporate additional information (eg, trajectory, stage) for more sophisticated models. IMPLICATIONS FOR PRACTICE: This study shows the potential of automated classification to leverage OHCs for needs assessment. Our approach can be applied to different types of cancer and enhanced by using domain-specific information.

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. The cohort includes data from two novel tasks not previously assessed in classification models for AD (pupil fixation and description of a pleasant past experience), as well as two established tasks (picture description and paragraph reading). Our dataset includes language and eye movement data from 79 memory clinic patients with diagnoses of mild-moderate AD, mild cognitive impairment (MCI), or subjective memory complaints (SMC), and 83 older adult controls. The analysis of the individual novel tasks showed similar classification accuracy when compared to established tasks, demonstrating their discriminative ability for memory clinic patients. Fusing the multimodal data across tasks yielded the highest overall AUC of 0.83 ± 0.01, indicating that the data from novel tasks are complementary to established tasks.

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. METHODS: We analyzed COVID-19-related tweets using topic modeling and aspect-based sentiment analysis (ABSA), and interpreted the results with public health experts. To generate insights on the effectiveness of specific public health interventions for COVID-19, we compared timelines of topics discussed with the timing of implementation of interventions, synergistically including information on people's sentiment about COVID-19-related aspects in our analysis. In addition, to further investigate anti-Asian racism, we compared timelines of sentiments for Asians and Canadians. RESULTS: Topic modeling identified 20 topics, and public health experts provided interpretations of the topics based on top-ranked words and representative tweets for each topic. The interpretation and timeline analysis showed that the discovered topics and their trend are highly related to public health promotions and interventions such as physical distancing, border restrictions, handwashing, staying home, and face coverings. After training the data using ABSA with human-in-the-loop, we obtained 545 aspect terms (eg, "vaccines," "economy," and "masks") and 60 opinion terms such as "infectious" (negative) and "professional" (positive), which were used for inference of sentiments of 20 key aspects selected by public health experts. The results showed negative sentiments related to the overall outbreak, misinformation and Asians, and positive sentiments related to physical distancing. CONCLUSIONS: Analyses using natural language processing techniques with domain expert involvement can produce useful information for public health. This study is the first to analyze COVID-19-related tweets in Canada in comparison with tweets in the United States by using topic modeling and human-in-the-loop domain-specific ABSA. This kind of information could help public health agencies to understand public concerns as well as what public health messages are resonating in our populations who use Twitter, which can be helpful for public health agencies when designing a policy for new interventions.

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. METHOD: The timing of a CLABSI onset was identified through the onset of CLABSI and the duration of catheterization. Cox proportional hazards regression analysis was used to estimate the impact of variables on the timing of CLABSI onset. The duration of catheterization was estimated using the Kaplan-Meier method. FINDING: Multivariable analysis by Cox proportional model analysis showed that there are six independent variables affecting the timing of a CLABSI onset: length of stay in hospital, catheter insertion location, use of antibiotics on day of catheter insertion, catheter function, number of blood transfusions per 100 days, and number of blood tests per 100 days. CONCLUSIONS: The findings of this study provide a foundation for the development of EBP-based CVC guidelines to effectively reduce CLABSIs and maintain a long-term CVC without a CLABSI.

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. According to the evaluation presented on two dissimilar corpora, the CNET forum and NPS Chat corpus, the effectiveness of each modeling assumption is found to vary depending on characteristics of the data. De-emphasizing content-related words yields improvement on the CNET corpus, while utilizing speaker tendencies is advantageous on the NPS corpus. The components of our model complement one another to achieve robust performance on both corpora and outperform state-of-the-art baseline models.

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. A service based on intelligent gadgets can be built intuitively and easily with a context representation language, called the intelligent gadget markup language (IGML) based on the event-condition-action (ECA) rule. The inherent nature of extensibility, not only environmental information but also physiological information can be specified as a context in IGML and can be dealt with an intelligent gadget with ease. It enables intelligent gadgets to be adopted to many different kinds of personalized healthcare services.

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.