A deep learning approach for identifying cancer survivors living with post-traumatic stress disorder on Twitter

Effects of Mindfulness-Based Cancer Recovery training on anxiety, depression, post-traumatic stress disorder, and cancer-related fatigue in breast neoplasm patients undergoing chemotherapy

RATIONALE

Mindfulness-Based Cancer Recovery (MBCR) program is a group course training for cancer patients that combines cancer knowledge and psychological knowledge, emphasizing focusing on the psychosomatic symptoms of cancer patients. Currently, the application value of Mindfulness-Based Cancer Recovery in improving psychosomatic health of cancer patients has been confirmed, however, its intervention effect on breast neoplasm patients has not yet been widely studied in China.

PATIENT CONCERNS AND DIAGNOSES

This study introduced the Mindfulness-Based Cancer Recovery protocol into the rehabilitation process of breast cancer patients, aiming to elucidate the effects of Mindfulness-Based Cancer Recovery on anxiety, depression, post-traumatic stress disorder, and cancer-related fatigue in breast neoplasm patients, to provide a practical basis for improving the physical and mental health of breast cancer patients.

INTERVENTION

In this study, 80 patients with chemotherapy-stage breast neoplasm attending the oncology department of a tertiary-level hospital from January 2022 to December 2022 were selected, 40 patients attending from January 2022 to June 2022 were included in the study group, and 40 patients attending from July 2022 to December 2022 were included in the control group. The control group was administered conventional care, and the study group was administered Mindfulness-Based Cancer Recovery based on conventional care in the control group for 8 weeks. After the intervention, hospital anxiety and depression scale, impact of event scale-revised, and cancer fatigue scale were used for evaluation.

OUTCOMES

After the intervention, hospital anxiety and depression scale scores decreased in both groups compared with pre-intervention, with the study group scoring lower than the control group (P < .05). After the intervention, the impact of event scale-revised scores of the 2 groups decreased from the preintervention period, with the study group scoring lower than the control group (P < .05). After the intervention, cancer fatigue scale scores decreased in the 2 groups compared with the preintervention period, with the study group scoring lower than the control group (P < .05).

LESSONS

Mindfulness-Based Cancer Recovery can effectively reduce the levels of anxiety, depression and post-traumatic stress disorder in breast neoplasm patients undergoing chemotherapy, reduce the levels of cancer-related fatigue, and promote the physical and mental health of patients.

Psychological Health and Drugs: Data-Driven Discovery of Causes, Treatments, Effects, and Abuses

Mental health issues can have significant impacts on individuals and communities and hence on social sustainability. There are several challenges facing mental health treatment; however, more important is to remove the root causes of mental illnesses because doing so can help prevent mental health problems from occurring or recurring. This requires a holistic approach to understanding mental health issues that are missing from the existing research. Mental health should be understood in the context of social and environmental factors. More research and awareness are needed, as well as interventions to address root causes. The effectiveness and risks of medications should also be studied. This paper proposes a big data and machine learning-based approach for the automatic discovery of parameters related to mental health from Twitter data. The parameters are discovered from three different perspectives: Drugs and Treatments, Causes and Effects, and Drug Abuse. We used Twitter to gather 1,048,575 tweets in Arabic about psychological health in Saudi Arabia. We built a big data machine learning software tool for this work. A total of 52 parameters were discovered for all three perspectives. We defined six macro-parameters (Diseases and Disorders, Individual Factors, Social and Economic Factors, Treatment Options, Treatment Limitations, and Drug Abuse) to aggregate related parameters. We provide a comprehensive account of mental health, causes, medicines and treatments, mental health and drug effects, and drug abuse, as seen on Twitter, discussed by the public and health professionals. Moreover, we identify their associations with different drugs. The work will open new directions for a social media-based identification of drug use and abuse for mental health, as well as other micro and macro factors related to mental health. The methodology can be extended to other diseases and provides a potential for discovering evidence for forensics toxicology from social and digital media.

Selected articles from the Fourth International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019)

In this introduction, we first summarize the Fourth International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019) held on October 26, 2019 in conjunction with the 18th International Semantic Web Conference (ISWC 2019) in Auckland, New Zealand, and then briefly introduce seven research articles included in this supplement issue, covering the topics on Knowledge Graph, Ontology-Powered Analytics, and Deep Learning.