Association Between Comorbidity Clusters and Mortality in Patients With Cancer: Predictive Modeling Using Machine Learning Approaches of Data From the United States and Hong Kong.

Background: Patients with cancer and cancer survivors often experience multiple chronic health conditions, which can impact symptom burden and treatment outcomes. Despite the high prevalence of multimorbidity, research on cancer prognosis has predominantly focused on cancers in isolation. There is growing interest in machine learning techniques for cancer studies.

Psychosocial challenges among Asian adolescents and young adults with cancer: a scoping review.

Background: Most of the landmark cohorts and reviews that assessed the psychosocial outcomes among adolescent and young adult (AYA) cancer survivors have focused on Western populations. This scoping review summarizes the existing evidence on psychosocial challenges experienced by AYAs with cancer in Asia, specifically work- and school-related outcomes, financial distress, social relationships, and concerns with infertility.

Methods: A literature search was conducted on Embase and Medline for studies that (1) were published in English between 2000 and 2023, (2) recruited AYAs diagnosed with cancer between the age of 15 and 39 years, (3) were conducted in Asia, and (4) assessed outcomes related to (i) work or/and school performance, (ii) financial distress, (iii) romantic relationship or/and relationship with family and peers, and (iii) concerns with childbirth and infertility.

A Hybrid Deep Learning-Based Feature Selection Approach for Supporting Early Detection of Long-Term Behavioral Outcomes in Survivors of Cancer: Cross-Sectional Study.

Background: The number of survivors of cancer is growing, and they often experience negative long-term behavioral outcomes due to cancer treatments. There is a need for better computational methods to handle and predict these outcomes so that physicians and health care providers can implement preventive treatments.

Objective: This study aimed to create a new feature selection algorithm to improve the performance of machine learning classifiers to predict negative long-term behavioral outcomes in survivors of cancer.

Perceptions in 3.6 Million Web-Based Posts of Online Communities on the Use of Cancer Immunotherapy: Data Mining Using BERTopic.

Background: Immunotherapy has become a game changer in cancer treatment. The internet has been used by patients as a platform to share personal experiences and seek medical guidance. Despite the increased utilization of immunotherapy in clinical practice, few studies have investigated the perceptions about its use by analyzing social media data.

Racial Health Disparities and Black Heterogeneity in COVID-19: A Case Study of Miami Gardens.

The COVID-19 pandemic is a critical public health concern that has disproportionately affected the Black community in the United States. The purpose of this study was to examine the risk and protective factors faced by residents in the City of Miami Gardens during the COVID-19 pandemic, with emphases placed on racial health disparities and Black heterogeneity. Using convenience and snowball sampling, quantitative and qualitative data for this study were collected via an anonymous online questionnaire using QuestionPro.

The Use of Traditional, Complementary, and Integrative Medicine in Cancer: Data-Mining Study of 1 Million Web-Based Posts From Health Forums and Social Media Platforms.

Background: Patients with cancer are increasingly using forums and social media platforms to access health information and share their experiences, particularly in the use of traditional, complementary, and integrative medicine (TCIM). Despite the popularity of TCIM among patients with cancer, few related studies have used data from these web-based sources to explore the use of TCIM among patients with cancer.

Objective: This study leveraged multiple forums and social media platforms to explore patients' use, interest, and perception of TCIM for cancer care.