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      Can online support groups address psychological morbidity of cancer patients? An artificial intelligence based investigation of prostate cancer trajectories

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          Abstract

          Background

          Online Cancer Support Groups (OCSG) are becoming an increasingly vital source of information, experiences and empowerment for patients with cancer. Despite significant contributions to physical, psychological and emotional wellbeing of patients, OCSG are yet to be formally recognised and used in multidisciplinary cancer support programs. This study highlights the opportunity of using Artificial Intelligence (AI) in OCSG to address psychological morbidity, with supporting empirical evidence from prostate cancer (PCa) patients.

          Methods

          A validated framework of AI techniques and Natural Language Processing (NLP) methods, was used to investigate PCa patient activities based on conversations in ten international OCSG (18,496 patients- 277,805 conversations). The specific focus was on activities that indicate psychological morbidity; the reasons for joining OCSG, deep emotions and the variation from joining through to milestones in the cancer trajectory. Comparative analyses were conducted using t-tests, One-way ANOVA and Tukey-Kramer post-hoc analysis.

          Findings

          PCa patients joined OCSG at four key phases of psychological distress; diagnosis, treatment, side-effects, and recurrence, the majority group was ‘treatment’ (61.72%). The four groups varied in expression of the intense emotional burden of cancer. The ‘side-effects’ group expressed increased negative emotions during the first month compared to other groups (p<0.01). A comparison of pre-treatment vs post-treatment emotions showed that joining pre-treatment had significantly lower negative emotions after 12-months compared to post-treatment (p<0.05). Long-term deep emotion analysis reveals that all groups except ‘recurrence’ improved in emotional wellbeing.

          Conclusion

          This is the first empirical study of psychological morbidity and deep emotions expressed by men with a new diagnosis of cancer, using AI. PCa patients joining pre-treatment had improved emotions, and long-term participation in OCSG led to an increase in emotional wellbeing, indicating a decrease in psychological distress. It is opportune to further investigate AI in OCSG for early psychological intervention as an adjunct to conventional intervention programs.

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          Most cited references36

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          Stress, social support, and the buffering hypothesis.

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            What are the unmet supportive care needs of people with cancer? A systematic review.

            The identification and management of unmet supportive care needs is an essential component of health care for people with cancer. Information about the prevalence of unmet need can inform service planning/redesign. A systematic review of electronic databases was conducted to determine the prevalence of unmet supportive care needs at difference time points of the cancer experience. Of 94 articles or reports identified, 57 quantified the prevalence of unmet need. Prevalence of unmet need, their trends and predictors were highly variable in all domains at all time points. The most frequently reported unmet needs were those in the activities of daily living domain (1-73%), followed by psychological (12-85%), information (6-93%), psychosocial (1-89%) and physical (7-89%). Needs within the spiritual (14-51%), communication (2-57%) and sexuality (33-63%) domains were least frequently investigated. Unmet needs appear to be highest and most varied during treatment, however a greater number of individuals were likely to express unmet need post-treatment compared to any other time. Tumour-specific unmet needs were difficult to distinguish. Variations in the classification of unmet need, differences in reporting methods and the diverse samples from which patients were drawn inhibit comparisons of studies. The diversity of methods used in studies hinders analysis of patterns and predictors of unmet need among people with cancer and precludes generalisation. Well-designed, context-specific, prospective studies, using validated instruments and standard methods of analysis and reporting, are needed to benefit future interventional research to identify how best to address the unmet supportive care needs of people with cancer.
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              Robot-assisted laparoscopic prostatectomy versus open radical retropubic prostatectomy: early outcomes from a randomised controlled phase 3 study

              The absence of trial data comparing robot-assisted laparoscopic prostatectomy and open radical retropubic prostatectomy is a crucial knowledge gap in uro-oncology. We aimed to compare these two approaches in terms of functional and oncological outcomes and report the early postoperative outcomes at 12 weeks.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: Writing – original draft
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: Writing – original draft
                Role: ConceptualizationRole: MethodologyRole: Writing – original draft
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: Writing – original draft
                Role: ConceptualizationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                4 March 2020
                2020
                : 15
                : 3
                : e0229361
                Affiliations
                [1 ] Research Centre for Data Analytics and Cognition, La Trobe University, Bundoora, Victoria, Australia
                [2 ] MD Anderson Cancer Center, University of Texas, Houston, Texas
                [3 ] College of Engineering and Science, Victoria University, Heidelberg, Victoria, Australia
                [4 ] NHS Trust, North Bristol, England, United Kingdom
                [5 ] Department of Surgery, University of Melbourne and Olivia Newton-John Cancer Centre, Austin Hospital, Melbourne, Australia
                [6 ] EJ Whitten Prostate Cancer Research Centre at Epworth Healthcare, Melbourne, Australia
                [7 ] Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Australia
                The Cancer Institute of New Jersey, Robert Wood Johnson Medical School, UNITED STATES
                Author notes

                Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this article.

                Author information
                http://orcid.org/0000-0001-5112-5063
                http://orcid.org/0000-0003-3878-5969
                http://orcid.org/0000-0002-4006-0388
                http://orcid.org/0000-0001-5047-3496
                http://orcid.org/0000-0001-8553-5618
                Article
                PONE-D-19-30015
                10.1371/journal.pone.0229361
                7055800
                32130256
                ec6ce177-42a7-4ea3-9a0a-012d647e6642
                © 2020 Adikari et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 30 October 2019
                : 4 February 2020
                Page count
                Figures: 3, Tables: 4, Pages: 14
                Funding
                The author(s) received no specific funding for this work.
                Categories
                Research Article
                Biology and Life Sciences
                Psychology
                Emotions
                Social Sciences
                Psychology
                Emotions
                Medicine and Health Sciences
                Oncology
                Cancers and Neoplasms
                Genitourinary Tract Tumors
                Prostate Cancer
                Medicine and Health Sciences
                Urology
                Prostate Diseases
                Prostate Cancer
                Medicine and Health Sciences
                Oncology
                Cancer Treatment
                Medicine and Health Sciences
                Diagnostic Medicine
                Cancer Detection and Diagnosis
                Medicine and Health Sciences
                Oncology
                Cancer Detection and Diagnosis
                Medicine and Health Sciences
                Health Care
                Health Statistics
                Morbidity
                Computer and Information Sciences
                Artificial Intelligence
                Computer and Information Sciences
                Information Technology
                Natural Language Processing
                Physical Sciences
                Mathematics
                Applied Mathematics
                Algorithms
                Machine Learning Algorithms
                Research and Analysis Methods
                Simulation and Modeling
                Algorithms
                Machine Learning Algorithms
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Machine Learning Algorithms
                Custom metadata
                The datasets used in this study are publicly available from the individual online support groups (full information in Table 1 of the manuscript). The source code to extract data from these online discussions is available at https://github.com/tharindurb/PRIME.

                Uncategorized
                Uncategorized

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