Symptom clusters in women with breast cancer: an analysis of data from social media and a research study

Breast cancer symptom profile longitudinal changes: data mining study

OBJECTIVES

Identifying stable co-occurring symptoms in breast cancer (BC) patients during chemotherapy can improve symptom management and the treatment process. This study examines symptom cluster stability in Iranian BC patients receiving chemotherapy and evaluates stability across three-time points within each cluster.

METHODS

This study collected data from three-time points: initial chemotherapy commencement, 2½ months postdiagnosis, and 5 months postdiagnosis. The research used exploratory factor analysis (EFA) in combination with hierarchical cluster analysis (HCA) and K means clustering to address research questions.

RESULTS

In the initial clustering step, EFA identified five clusters with high consistency across three-time points. The first cluster showed depression, anxiety and irritability, while the second cluster was characterised by sexual interest and pain. The third cluster was associated with diarrhoea and vomiting. In the second step, we obtained the HCA item output and two clusters of K means clustering that recorded depression and anxiety symptoms over time. Vomiting, dry mouth, sexual interest, worrying and numbness were observed during the first and second points, but the frequency has decreased since then.

CONCLUSIONS

Cancer's psychological and physiological symptoms, including depression, anxiety, digestive and hormonal issues, remain stable throughout the disease. Palliative care centres can improve patients' quality of life and treatment process by addressing persistent symptoms.

Effects of nonpharmacological interventions on symptom clusters in breast cancer survivors: A systematic review of randomized controlled trials

Objective

To summarize nonpharmacological interventions and assess their effects on symptom clusters and quality of life (QoL) in breast cancer (BC) survivors.

Methods

Seven English and three Chinese electronic databases and three clinical trial registries were searched from January 2001 to August 2023. A narrative approach was applied to summarize the data. The primary outcome was symptom clusters measured by any patient-reported questionnaires, and the secondary outcomes were QoL and intervention-related adverse events.

Results

Six published articles, one thesis, and one ongoing trial involving 625 BC survivors were included. The fatigue-sleep disturbance-depression symptom cluster was the most frequently reported symptom cluster among BC survivors. The nonpharmacological interventions were potentially positive on symptom clusters and QoL among the BC survivors. However, some of the included studies exhibited methodological concerns (e.g., inadequate blinding and allocation concealment). The intervention protocols in only two studies were developed following a solid evidence-based approach. Adverse events related to the targeted interventions were reported in six included studies, with none performing a causality analysis.

Conclusions

The nonpharmacological interventions could be promising strategies for alleviating symptom clusters in BC survivors. Future studies should adopt rigorously designed, randomized controlled trials to generate robust evidence.

Systematic review registration

INPLASY202380028.

Impact of post-treatment symptoms on supportive care needs among breast cancer survivors in South Korea

Objective

The aim of this study is to investigate the factors influencing the supportive care needs of survivors of breast cancer who have completed major treatment.

Methods

A total of 121 survivors of breast cancer from an online community in South Korea participated in this study. The study variables were supportive care needs, physical symptoms, anxiety, and depression. Independent t-tests, one-way Analysis of Variance (ANOVA), Pearson's correlation, and hierarchical regression analyses were performed.

Results

The highest rankings of supportive care needs of survivors of breast cancer were medical system and information needs, patient care and support needs, psychological needs, sexual needs, and physical and daily life needs. Hierarchical regression analysis revealed that the participants' supportive care needs were explained by physical symptoms (P ​< ​0.001) and anxiety (P ​< ​0.001), accounting for 52.1% of the variance.

Conclusions

Supportive care needs of survivors of breast cancer have a high level of medical system and information needs, and posttreatment conditions are related to high physical symptoms and anxiety. In the future, it will be necessary to identify supportive care needs and apply interventions to reduce their physical symptoms and anxiety.

Cluster Analysis on Gastrointestinal Symptoms during Menopausal Transition

The purpose of this secondary analysis was to determine the clusters of midlife women by gastrointestinal (GI) symptoms and to explore differences in the clusters by race/ethnicity. This analysis used the data from two internet-based studies among 1,054 midlife women. The analysis was conducted with the data on background characteristics, health and menopausal status, and GI symptoms (collected using the GI Symptom Index for Midlife Women). The data were analyzed using factor analyses, hierarchical cluster analyses, chi-square tests, multinomial logistic regression analyses, and analyses of covariance. Three clusters were adopted: Cluster 1 (with low total numbers and severity scores of symptoms; 46.0%), Cluster 2 (with moderate total numbers and severity scores of symptoms; 44.0%), and Cluster 3 (with high total numbers and severity scores of symptoms; 10.0%). Only in Cluster 2, there were significant racial/ethnic differences in individual GI symptoms. These results provide directions for future GI symptom management among midlife women.

The influence of video-based social modelling on the nocebo effect

OBJECTIVE

Seeing someone else experience side effects (i.e., social modelling) can increase negative expectations and subsequent nocebo effects. In face-to-face contexts, this effect appears stronger in female participants. Less is known about the influence of gender on negative expectations and nocebo effects generated via video-based social modelling.

METHODS

One hundred and seven undergraduate participants recruited from a participant pool at an Australian university took part in a study ostensibly investigating the influence of beta-blocker medications (actually a sham treatment) on physiological and psychological aspects of anxiety. Participants were randomly assigned to either a no-treatment control group, a standard treatment group, or a video modelling group, in which participants viewed video-recorded confederates (one male, one female) report experiencing four side effects (two each) after taking the study treatment. Symptoms were assessed 15-min following pill ingestion, and at follow-up 24 h later.

RESULTS

Video modelling of side effects, compared to standard treatment, interacted with gender and was associated with increased reporting of modelled symptoms in female compared to male participants, p = .01, η_p²=0.06. Video modelling also increased negative expectations in female compared to male participants, p = .03, η_p²=0.07, and expectations mediated the influence of modelling on modelled symptoms in female participants.

CONCLUSIONS

Social modelling of side effects via video increased negative expectations, and nocebo symptoms, to a greater extent in female participants. These findings suggest that males and females are differentially impacted by video-based side effect modelling. Results have implications for social modelling of side effects via social media and patient-support websites.

Social media mining to support drug repurposing: Exploring long-COVID self-medication reported by Reddit users (Preprint)

Background

Since the beginning of the COVID-19 pandemic, over 480 million people have been infected and more than 6 million people have died from COVID-19 worldwide. In some patients with acute COVID-19, symptoms manifest over a longer period, which is also called “long-COVID.” Unmet medical needs related to long-COVID are high, since there are no treatments approved. Patients experiment with various medications and supplements hoping to alleviate their suffering. They often share their experiences on social media.

Objective

The aim of this study was to explore the feasibility of social media mining methods to extract important compounds from the perspective of patients. The goal is to provide an overview of different medication strategies and important agents mentioned in Reddit users’ self-reports to support hypothesis generation for drug repurposing, by incorporating patients’ experiences.

Methods

We used named-entity recognition to extract substances representing medications or supplements used to treat long-COVID from almost 70,000 posts on the “/r/covidlonghaulers” subreddit. We analyzed substances by frequency, co-occurrences, and network analysis to identify important substances and substance clusters.

Results

The named-entity recognition algorithm achieved an F1 score of 0.67. A total of 28,447 substance entities and 5789 word co-occurrence pairs were extracted. “Histamine antagonists,” “famotidine,” “magnesium,” “vitamins,” and “steroids” were the most frequently mentioned substances. Network analysis revealed three clusters of substances, indicating certain medication patterns.

Conclusions

This feasibility study indicates that network analysis can be used to characterize the medication strategies discussed in social media. Comparison with existing literature shows that this approach identifies substances that are promising candidates for drug repurposing, such as antihistamines, steroids, or antidepressants. In the context of a pandemic, the proposed method could be used to support drug repurposing hypothesis development by prioritizing substances that are important to users.

Distress among Korean Cancer Survivors: A Latent Profile Analysis

This study aimed to classify cancer survivors’ latent profile analysis (LPA) according to the problem list and identify the differences in distress between subgroups. Furthermore, this study identified differences between subgroups based on their demographic and clinical characteristics. A self-reported cross-sectional survey was administered to 446 adult cancer survivors in Korea. A distress thermometer and problem list were used, and four domains of the problem list were used to perform LPA and create subgroups. Quade’s non-parametric analysis of covariance was used to determine the difference in distress between the profiles. The three identified subgroups of the problem list were: “low problem group” (36.7%), “high problem group” (49.1%), and “family only low problem group” (14.2%). The analysis showed that there was a difference in the distress level according to the sub-profile of the problem list (F = 43.69, p < 0.001). In interventions for distress, integrative interventions that are not limited to one area are necessary, and cultural characteristics as well as the problem list relevant to cancer survivors should be considered.

The Mediating Effect of Perceived Injustice and Pain Catastrophizing in the Relationship of Pain on Fatigue and Sleep in Breast Cancer Survivors: A Cross-Sectional Study

OBJECTIVE

Multidimensional aspects of pain have raised awareness about cognitive appraisals, such as perceived injustice (PI) and pain catastrophizing (PC). It has been demonstrated that they play an important role in patients' pain experience. However, the mediating effect of these appraisals has not been investigated in breast cancer survivors (BCS), nor have they been related to fatigue and sleep.

METHODS

Cross-sectional data from 128 BCS were analysed by structural path analysis with the aim to examine the mediating effect of PI and PC in the relationship of pain on fatigue and sleep.

RESULTS

The indirect mediating effects of PI on fatigue (CSI*PI = 0.21; P < 0.01 and VAS*PI = 1.19; P < 0.01) and sleep (CSI*PI = 0.31; P < 0.01 and VAS*PI = 1.74; P < 0.01) were found significant for both pain measures (Central Sensitization Inventory (CSI) and Visual Analogue Scale (VAS)). PC, on the other hand, only mediated the relationship between pain measured by VAS and fatigue (VAS*PC = 0.80; P = 0.03). Positive associations were found, indicating that higher pain levels are positively correlated with PI and PC, which go hand in hand with higher levels of fatigue and sleep problems.

CONCLUSION

PI is an important mediator in the relationship of pain on fatigue and sleep, while PC is a mediator on fatigue after cancer treatment. These findings highlight that both appraisals are understudied and open new perspectives regarding treatment strategies in BCS.

Identification of Breast Cancer Survivors With High Symptom Burden

BACKGROUND

While women diagnosed with breast cancer have increased survival when compared with other cancers, survivorship may include residual symptom burden from treatment and continuing endocrine therapies.

OBJECTIVE

The objective of this study was to identify subgroups of breast cancer survivors experiencing similar symptom severity.

METHODS

Participants were 498 women with breast cancer, not on active treatment. Symptom severity was self-reported using the MD Anderson Symptom Inventory. Target symptoms were included in a latent profile analysis. Factors related to subgroup membership and differences in quality of life (QOL) and functioning were explored using logistic regression.

RESULTS

Mean age was 60.11 (SD, 11.32) years, 86.1% were white, and 79.1% were receiving endocrine therapy. Target symptoms included fatigue (reported at ≥5 by 22.8% of women), sleep disturbance (24.8%), and trouble remembering (17.2%). Two subgroups were identified: low symptom severity (77.0% of women) and high (23.0%). Older women (odds ratio [OR], 0.971; 95% confidence interval [CI], 0.952-0.989) and employed women (OR, 0.621; 95% CI, 0404-0.956) were less likely to be in the high subgroup; women with poorer performance status (OR, 1.653; 95% CI, 1.188-2.299) were more likely to be in the high subgroup. Women in the high subgroup reported lower QOL (P = .000) and greater interference with functioning (P = .000).

CONCLUSIONS

Two subgroups of women with distinct symptom severity were identified.

IMPLICATIONS FOR PRACTICE

Identification of women at risk for high symptoms during survivorship may allow clinicians to intensify their approach to symptom management, thereby mitigating poor outcomes and impairments in QOL.

Development of a Lexicon for Pain

Pain has been an area of growing interest in the past decade and is known to be associated with mental health issues. Due to the ambiguous nature of how pain is described in text, it presents a unique natural language processing (NLP) challenge. Understanding how pain is described in text and utilizing this knowledge to improve NLP tasks would be of substantial clinical importance. Not much work has previously been done in this space. For this reason, and in order to develop an English lexicon for use in NLP applications, an exploration of pain concepts within free text was conducted. The exploratory text sources included two hospital databases, a social media platform (Twitter), and an online community (Reddit). This exploration helped select appropriate sources and inform the construction of a pain lexicon. The terms within the final lexicon were derived from three sources—literature, ontologies, and word embedding models. This lexicon was validated by two clinicians as well as compared to an existing 26-term pain sub-ontology and MeSH (Medical Subject Headings) terms. The final validated lexicon consists of 382 terms and will be used in downstream NLP tasks by helping select appropriate pain-related documents from electronic health record (EHR) databases, as well as pre-annotating these words to help in development of an NLP application for classification of mentions of pain within the documents. The lexicon and the code used to generate the embedding models have been made publicly available.

Information Patients With Melanoma Spontaneously Report About Health-Related Quality of Life on Web-Based Forums: Case Study

Background

There is a general agreement on the importance of health-related quality of life (HRQoL). This type of information is becoming increasingly important for the value assessment of health technology assessment agencies in evaluating the benefits of new health technologies, including medicines. However, HRQoL data are often limited, and additional sources that provide this type of information may be helpful.

Objective

We aim to identify the HRQoL topics important to patients with melanoma based on web-based discussions on public social media forums.

Methods

We identified 3 public web-based forums from the United States and the United Kingdom, namely the Melanoma Patient Information Page, the Melanoma International Forum, and MacMillan. Their posts were randomly selected and coded using qualitative methods until saturation was reached.

Results

Of the posts assessed, 36.7% (150/409) of posts on Melanoma International Forum, 45.1% (198/439) on MacMillan, and 35.4% (128/362) on Melanoma Patient Information Page focused on HRQoL. The 2 themes most frequently mentioned were mental health and (un)certainty. The themes were constructed based on underlying and more detailed codes. Codes related to fear, worry and anxiety, uncertainty, and unfavorable effects were the most-often discussed ones.

Conclusions

Web-based forums are a valuable source for identifying relevant HRQoL aspects in patients with a given disease. These aspects could be cross-referenced with existing tools and they might improve the content validity of patient-reported outcome measures, including HRQoL questionnaires. In addition, web-based forums may provide health technology assessment agencies with a more holistic understanding of the external aspects affecting patient HRQoL. These aspects might support the value assessment of new health technologies and could therefore help inform topic prioritization as well as the scoping phase before any value assessment.

A Computational Framework to Analyze the Associations Between Symptoms and Cancer Patient Attributes Post Chemotherapy Using EHR Data

Patients with cancer, such as breast and colorectal cancer, often experience different symptoms post-chemotherapy. The symptoms could be fatigue, gastrointestinal (nausea, vomiting, lack of appetite), psychoneurological symptoms (depressive symptoms, anxiety), or other types. Previous research focused on understanding the symptoms using survey data. In this research, we propose to utilize the data within the Electronic Health Record (EHR). A computational framework is developed to use a natural language processing (NLP) pipeline to extract the clinician-documented symptoms from clinical notes. Then, a patient clustering method is based on the symptom severity levels to group the patient in clusters. The association rule mining is used to analyze the associations between symptoms and patient attributes (smoking history, number of comorbidities, diabetes status, age at diagnosis) in the patient clusters. The results show that the various symptom types and severity levels have different associations between breast and colorectal cancers and different timeframes post-chemotherapy. The results also show that patients with breast or colorectal cancers, who smoke and have severe fatigue, likely have severe gastrointestinal symptoms six months after the chemotherapy. Our framework can be generalized to analyze symptoms or symptom clusters of other chronic diseases where symptom management is critical.

Analyzing the symptoms in colorectal and breast cancer patients with or without type 2 diabetes using EHR data

This research extracted patient-reported symptoms from free-text EHR notes of colorectal and breast cancer patients and studied the correlation of the symptoms with comorbid type 2 diabetes, race, and smoking status. An NLP framework was developed first to use UMLS MetaMap to extract all symptom terms from the 366,398 EHR clinical notes of 1694 colorectal cancer (CRC) patients and 3458 breast cancer (BC) patients. Semantic analysis and clustering algorithms were then developed to categorize all the relevant symptoms into eight symptom clusters defined by seed terms. After all the relevant symptoms were extracted from the EHR clinical notes, the frequency of the symptoms reported from colorectal cancer (CRC) and breast cancer (BC) patients over three time-periods post-chemotherapy was calculated. Logistic regression (LR) was performed with each symptom cluster as the response variable while controlling for diabetes, race, and smoking status. The results show that the CRC and BC patients with Type 2 Diabetes (T2D) were more likely to report symptoms than CRC and BC without T2D over three time-periods in the cancer trajectory. We also found that current smokers were more likely to report anxiety (CRC, BC), neuropathic symptoms (CRC, BC), anxiety (BC), and depression (BC) than non-smokers.

Determination of Patient Sentiment and Emotion in Ophthalmology: Infoveillance Tutorial on Web-Based Health Forum Discussions

Background

Clinical data in social media are an underused source of information with great potential to allow for a deeper understanding of patient values, attitudes, and preferences.

Objective

This tutorial aims to describe a novel, robust, and modular method for the sentiment analysis and emotion detection of free text from web-based forums and the factors to consider during its application.

Methods

We mined the discussion and user information of all posts containing search terms related to a medical subspecialty (oculoplastics) from MedHelp, the largest web-based platform for patient health forums. We used data cleaning and processing tools to define the relevant subset of results and prepare them for sentiment analysis. We executed sentiment and emotion analyses by using IBM Watson Natural Language Understanding to generate sentiment and emotion scores for the posts and their associated keywords. The keywords were aggregated using natural language processing tools.

Results

Overall, 39 oculoplastic-related search terms resulted in 46,381 eligible posts within 14,329 threads. Posts were written by 18,319 users (117 doctors; 18,202 patients) and included 201,611 associated keywords. Keywords that occurred ≥500 times in the corpus were used to identify the most prominent topics, including specific symptoms, medication, and complications. The sentiment and emotion scores of these keywords and eligible posts were analyzed to provide concrete examples of the potential of this methodology to allow for a better understanding of patients’ attitudes. The overall sentiment score reflects a positive, neutral, or negative sentiment, whereas the emotion scores (anger, disgust, fear, joy, and sadness) represent the likelihood of the presence of the emotion. In keyword grouping analyses, medical signs, symptoms, and diseases had the lowest overall sentiment scores (−0.598). Complications were highly associated with sadness (0.485). Forum posts mentioning body parts were related to sadness (0.416) and fear (0.321). Administration was the category with the highest anger score (0.146). The top 6 forum subgroups had an overall negative sentiment score; the most negative one was the Neurology forum, with a score of −0.438. The Undiagnosed Symptoms forum had the highest sadness score (0.448). The least likely fearful posts were those from the Eye Care forum, with a score of 0.260. The overall sentiment score was much more negative before the doctor replied. The anger, disgust, fear, and sadness emotion scores decreased in likelihood, whereas joy was slightly more likely to be expressed after doctors replied.

Conclusions

This report allows physicians and researchers to efficiently mine and perform sentiment analysis on social media to better understand patients’ perspectives and promote patient-centric care. Important factors to be considered during its application include evaluating the scope of the search; selecting search terms and understanding their linguistic usages; and establishing selection, filtering, and processing criteria for posts and keywords tailored to the desired results.

Symptom clusters experienced by breast cancer patients at various treatment stages: A systematic review

Breast cancer patients often experience symptoms that adversely affect their quality of life. It is understood that many of these symptoms tend to cluster together: while they might have different manifestations and occur during different phases of the disease trajectory, the symptoms often have a common aetiology that is a potential target for intervention. Understanding the symptom clusters associated with breast cancer might usefully inform the development of effective care plans for affected patients. The aim of this paper is to provide an updated systematic review of the known symptom clusters among breast cancer patients during and/or after cancer treatment. A search was conducted using five databases for studies reporting symptom clusters among breast cancer patients. The search yielded 32 studies for inclusion. The findings suggest that fatigue-sleep disturbance and psychological symptom cluster (including anxiety, depression, nervousness, irritability, sadness, worry) are the most commonly-reported symptom clusters among breast cancer patients. Further, the composition of symptom clusters tends to change across various stages of cancer treatment. While this review identified some commonalities, the different methodologies used to identify symptom clusters resulted in inconsistencies in symptom cluster identification. It would be useful if future studies could separately examine the symptom clusters that occur in breast cancer patients undergoing a particular treatment type, and use standardised instruments across studies to assess symptoms. The review concludes that further studies could usefully determine the biological pathways associated with various symptom clusters, which would inform the development of effective and efficient symptom management strategies.

Symptom Clusters in Breast Cancer Survivors: A Latent Class Profile Analysis

OBJECTIVES

To identify symptom clusters in breast cancer survivors and to determine sociodemographic and clinical characteristics influencing symptom cluster membership.

SAMPLE AND SETTING

The authors performed a cross-sectional secondary analysis of data obtained from a community-based cancer registry-linked survey with 1,500 breast cancer survivors 6-13 months following a breast cancer diagnosis.

METHODS AND VARIABLES

Symptom clusters were identified using latent class profile analysis of four patient-reported symptoms (pain, fatigue, sleep disturbance, and depression) with custom PROMIS® short forms.

RESULTS

Four distinct classes were identified.

IMPLICATIONS FOR NURSING

Common symptom clusters may lead to better prevention and treatment strategies that target a group of symptoms. Results also suggest that certain factors place patients at high risk for symptom burden, which can guide tailored interventions.

Understanding breast cancer survivors' information-seeking behaviours and overall experiences: a comparison of themes derived from social media posts and focus groups

OBJECTIVE

Using two different analysis techniques, this study explored differences and similarities in information-seeking discourse and overall breast cancer experiences between posters to a Reddit board and breast cancer survivor focus groups.

DESIGN

This study incorporates two qualitative methods for determining themes in breast cancer survivors' information-seeking behaviours and overall cancer experiences. First, posts from a breast cancer-specific Reddit community were extracted and analysed using the meaning extraction method (MEM) to determine core themes. Then, investigators performed a thematic analysis of two focus groups of breast cancer survivors (N = 18). Finally, themes derived from each analysis method were compared.

MAIN OUTCOME MEASURES

Outcome measures include themes extracted from Reddit posts and themes generated from breast cancer survivor focus groups.

RESULTS

Findings between qualitative methodologies represent similar yet nuanced themes in survivors' discourse. The MEM resulted in seven themes: diagnosis, treatment process, social support, existentialism, risk, information-seeking and surgery. Focus groups revealed the same initial four MEM themes plus the following: disclosure, coping and fears.

CONCLUSIONS

The MEM is a cost-effective research mechanism for informing common themes of experiences of cancer patients and survivors and may offer initial data to guide psychosocial oncology research design and recruitment.

Beyond the Randomized Clinical Trial: Innovative Data Science to Close the Pediatric Evidence Gap

Despite the application of advanced statistical and pharmacometric approaches to pediatric trial data, a large pediatric evidence gap still remains. Here, we discuss how to collect more data from children by using real-world data from electronic health records, mobile applications, wearables, and social media. The large datasets collected with these approaches enable and may demand the use of artificial intelligence and machine learning to allow the data to be analyzed for decision making. Applications of this approach are presented, which include the prediction of future clinical complications, medical image analysis, identification of new pediatric end points and biomarkers, the prediction of treatment nonresponders, and the prediction of placebo-responders for trial enrichment. Finally, we discuss how to bring machine learning from science to pediatric clinical practice. We conclude that advantage should be taken of the current opportunities offered by innovations in data science and machine learning to close the pediatric evidence gap.

Impact of chemotherapy on symptoms and symptom clusters in postmenopausal women with breast cancer prior to aromatase inhibitor therapy

AIMS AND OBJECTIVES

To examine and compare the differences in symptoms and symptom clusters between postmenopausal women with early-stage breast cancer who did and did not receive chemotherapy prior to aromatase inhibitor (AI) therapy.

BACKGROUND

Women with breast cancer often experience multiple concurrent symptoms during AI therapy. The burden of symptoms prior to AI is associated with nonadherence to cancer treatment. To date, few studies have comprehensively explored the symptoms and symptom clusters occurring prior to AI therapy.

DESIGN

Secondary analysis of a prospective repeated-measures study.

METHODS

The sample comprised postmenopausal women (N = 339) with breast cancer who would receive AI therapy with or without chemotherapy. We collected information on 48 symptoms after surgery or chemotherapy but before AI therapy using different symptom assessment tools. Mann-Whitney U tests were used to compare the differences in the severity of symptoms between groups. Exploratory factor analysis (EFA) was conducted to determine symptom clusters. This study followed STROBE guidelines.

RESULTS

The most severe symptoms among women with breast cancer prior to AI therapy were breast sensitivity, unhappy with the appearance of my body, general aches and pain, joint pain and muscle stiffness. Women who received chemotherapy prior to AI therapy experienced significantly higher severity of 22 symptoms than women who did not receive chemotherapy. Through EFA seven distinct symptom clusters were revealed in both groups: cognitive, musculoskeletal, psychological, vasomotor, weight, sexual and urinary, with additional gastrointestinal symptom cluster been identified in women who received chemotherapy.

CONCLUSIONS

This study indicates the presence of symptoms among women with breast cancer prior to AI therapy, with higher severity of symptoms and greater number of symptom clusters for women who received chemotherapy.

RELEVANCE TO CLINICAL PRACTICE

Nurses should assess and be aware of symptoms and symptom clusters existed prior to AI therapy and manage them in advance.

Mining of Textual Health Information from Reddit: Analysis of Chronic Diseases With Extracted Entities and Their Relations

BACKGROUND

Social media platforms constitute a rich data source for natural language processing tasks such as named entity recognition, relation extraction, and sentiment analysis. In particular, social media platforms about health provide a different insight into patient's experiences with diseases and treatment than those found in the scientific literature.

OBJECTIVE

This paper aimed to report a study of entities related to chronic diseases and their relation in user-generated text posts. The major focus of our research is the study of biomedical entities found in health social media platforms and their relations and the way people suffering from chronic diseases express themselves.

METHODS

We collected a corpus of 17,624 text posts from disease-specific subreddits of the social news and discussion website Reddit. For entity and relation extraction from this corpus, we employed the PKDE4J tool developed by Song et al (2015). PKDE4J is a text mining system that integrates dictionary-based entity extraction and rule-based relation extraction in a highly flexible and extensible framework.

RESULTS

Using PKDE4J, we extracted 2 types of entities and relations: biomedical entities and relations and subject-predicate-object entity relations. In total, 82,138 entities and 30,341 relation pairs were extracted from the Reddit dataset. The most highly mentioned entities were those related to oncological disease (2884 occurrences of cancer) and asthma (2180 occurrences). The relation pair anatomy-disease was the most frequent (5550 occurrences), the highest frequent entities in this pair being cancer and lymph. The manual validation of the extracted entities showed a very good performance of the system at the entity extraction task (3682/5151, 71.48% extracted entities were correctly labeled).

CONCLUSIONS

This study showed that people are eager to share their personal experience with chronic diseases on social media platforms despite possible privacy and security issues. The results reported in this paper are promising and demonstrate the need for more in-depth studies on the way patients with chronic diseases express themselves on social media platforms.

A systematic literature review of machine learning in online personal health data

OBJECTIVE

User-generated content (UGC) in online environments provides opportunities to learn an individual's health status outside of clinical settings. However, the nature of UGC brings challenges in both data collecting and processing. The purpose of this study is to systematically review the effectiveness of applying machine learning (ML) methodologies to UGC for personal health investigations.

MATERIALS AND METHODS

We searched PubMed, Web of Science, IEEE Library, ACM library, AAAI library, and the ACL anthology. We focused on research articles that were published in English and in peer-reviewed journals or conference proceedings between 2010 and 2018. Publications that applied ML to UGC with a focus on personal health were identified for further systematic review.

RESULTS

We identified 103 eligible studies which we summarized with respect to 5 research categories, 3 data collection strategies, 3 gold standard dataset creation methods, and 4 types of features applied in ML models. Popular off-the-shelf ML models were logistic regression (n = 22), support vector machines (n = 18), naive Bayes (n = 17), ensemble learning (n = 12), and deep learning (n = 11). The most investigated problems were mental health (n = 39) and cancer (n = 15). Common health-related aspects extracted from UGC were treatment experience, sentiments and emotions, coping strategies, and social support.

CONCLUSIONS

The systematic review indicated that ML can be effectively applied to UGC in facilitating the description and inference of personal health. Future research needs to focus on mitigating bias introduced when building study cohorts, creating features from free text, improving clinical creditability of UGC, and model interpretability.

A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data

OBJECTIVE

In this systematic review, we aim to synthesize the literature on the use of natural language processing (NLP) and text mining as they apply to symptom extraction and processing in electronic patient-authored text (ePAT).

MATERIALS AND METHODS

A comprehensive literature search of 1964 articles from PubMed and EMBASE was narrowed to 21 eligible articles. Data related to purpose, text source, number of users and/or posts, evaluation metrics, and quality indicators were recorded.

RESULTS

Pain (n = 18) and fatigue and sleep disturbance (n = 18) were the most frequently evaluated symptom clinical content categories. Studies accessed ePAT from sources such as Twitter and online community forums or patient portals focused on diseases, including diabetes, cancer, and depression. Fifteen studies used NLP as a primary methodology. Studies reported evaluation metrics including the precision, recall, and F-measure for symptom-specific research questions.

DISCUSSION

NLP and text mining have been used to extract and analyze patient-authored symptom data in a wide variety of online communities. Though there are computational challenges with accessing ePAT, the depth of information provided directly from patients offers new horizons for precision medicine, characterization of sub-clinical symptoms, and the creation of personal health libraries as outlined by the National Library of Medicine.

CONCLUSION

Future research should consider the needs of patients expressed through ePAT and its relevance to symptom science. Understanding the role that ePAT plays in health communication and real-time assessment of symptoms, through the use of NLP and text mining, is critical to a patient-centered health system.

Impact of Self-Acupressure on Co-Occurring Symptoms in Cancer Survivors

Background

Cancer survivors with fatigue often experience depressive symptoms, anxiety, and pain. Previously, we reported that self-acupressure improved fatigue; however, its impact on other co-occurring symptoms and their involvement in treatment action has not been explored.

Methods

Changes in depressive symptoms, anxiety, and pain were examined prior to and following two formulas of self-acupressure and usual care using linear mixed models in 288 women from a previously reported clinical trial. Participants were categorized by random assignment into one of three groups: 1) relaxing acupressure, 2) stimulating acupressure, or 3) usual care. Moderators investigated were body mass index, age, depressive symptoms, anxiety, sleep and pain, and mediators were change in these symptoms.

Results

Following treatment, depressive symptoms improved statistically significantly for the relaxing acupressure group (41.5%) compared with stimulating acupressure (25%) and usual care (7.7%). Both acupressure groups were associated with greater improvements in anxiety than usual care, but only relaxing acupressure was associated with greater reductions in pain severity, and only stimulating acupressure was associated with greater reductions in pain interference. There were no statistically significant moderators of sleep quality, anxiety, or depressive symptoms. Fatigue statistically significantly moderated pain, and age statistically significantly modified fatigue. Changes in depressive symptoms and sleep quality statistically significantly mediated the relationship between relaxing acupressure and usual care on fatigue; however, the effect was small.

Conclusions

Acupressure was associated with greater improvements than usual care in anxiety, pain, and symptoms of depression in breast cancer survivors with troublesome fatigue. These findings warrant further evaluation in suitably controlled randomized trials.

Use of Social Media in the Assessment of Relative Effectiveness: Explorative Review With Examples From Oncology

Background

An element of health technology assessment constitutes assessing the clinical effectiveness of drugs, generally called relative effectiveness assessment. Little real-world evidence is available directly after market access, therefore randomized controlled trials are used to obtain information for relative effectiveness assessment. However, there is growing interest in using real-world data for relative effectiveness assessment. Social media may provide a source of real-world data.

Objective

We assessed the extent to which social media-generated health data has provided insights for relative effectiveness assessment.

Methods

An explorative literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to identify examples in oncology where health data were collected using social media. Scientific and grey literature published between January 2010 and June 2016 was identified by four reviewers, who independently screened studies for eligibility and extracted data. A descriptive qualitative analysis was performed.

Results

Of 1032 articles identified, eight were included: four articles identified adverse events in response to cancer treatment, three articles disseminated quality of life surveys, and one study assessed the occurrence of disease-specific symptoms. Several strengths of social media-generated health data were highlighted in the articles, such as efficient collection of patient experiences and recruiting patients with rare diseases. Conversely, limitations included validation of authenticity and presence of information and selection bias.

Conclusions

Social media may provide a potential source of real-world data for relative effectiveness assessment, particularly on aspects such as adverse events, symptom occurrence, quality of life, and adherence behavior. This potential has not yet been fully realized and the degree of usefulness for relative effectiveness assessment should be further explored.

Differences in symptom clusters before and twelve months after breast cancer surgery

PURPOSE

Given the inter-relatedness among symptoms, research efforts are focused on an evaluation of symptom clusters. The purposes of this study were to evaluate for differences in the number and types of menopausal-related symptom clusters assessed prior to and at 12-months after surgery using ratings of occurrence and severity and to evaluate for changes in these symptom clusters over time.

METHODS

Prior to and at 12 months after surgery, 392 women with breast cancer completed the Menopausal Symptoms Scale. Exploratory factor analyses were used to identify the symptom clusters.

RESULTS

Of the 392 women evaluated, the mean number of symptoms (out of 46) was 13.2 (±8.5) at enrollment and 10.9 (±8.2) at 12 months after surgery. Using occurrence and severity, three symptom clusters were identified prior to surgery. Five symptom clusters were identified at 12 months following surgery. Two symptom clusters (i.e., pain/discomfort and hormonal) were relatively stable across both dimensions and time points. Two symptom clusters were relatively stable across both dimensions either prior to surgery (i.e., sleep/psychological/cognitive) or at 12 months after surgery (i.e., sleep). The other four clusters (i.e., irritability, psychological/cognitive, cognitive, psychological) were identified at one time point using a single dimension.

CONCLUSIONS

While some menopausal-related symptom clusters were consistent across time and dimensions, the majority of symptoms clustered together differently depending on whether they were evaluated prior to or at 12 months after breast cancer surgery. An increased understanding of how symptom clusters change over time may assist clinicians to focus their symptom assessments and management strategies.

Mining social media data: How are research sponsors and researchers addressing the ethical challenges?

Background:

Data representing people’s behaviour, attitudes, feelings and relationships are increasingly being harvested from social media platforms and re-used for research purposes. This can be ethically problematic, even where such data exist in the public domain. We set out to explore how the academic community is addressing these challenges by analysing a national corpus of research ethics guidelines and published studies in one interdisciplinary research area.

Methods:

Ethics guidelines published by Research Councils UK (RCUK), its seven-member councils and guidelines cited within these were reviewed. Guidelines referring to social media were classified according to published typologies of social media research uses and ethical considerations for social media mining. Using health research as an exemplar, PubMed was searched to identify studies using social media data, which were assessed according to their coverage of ethical considerations and guidelines.

Results:

Of the 13 guidelines published or recommended by RCUK, only those from the Economic and Social Research Council, the British Psychological Society, the International Association of Internet Researchers and the National Institute for Health Research explicitly mentioned the use of social media. Regarding data re-use, all four mentioned privacy issues but varied with respect to other ethical considerations. The PubMed search revealed 156 health-related studies involving social media data, only 50 of which mentioned ethical concepts, in most cases simply stating that they had obtained ethical approval or that no consent was required. Of the nine studies originating from UK institutions, only two referred to RCUK ethics guidelines or guidelines cited within these.

Conclusions:

Our findings point to a deficit in ethical guidance for research involving data extracted from social media. Given the growth of studies using these new forms of data, there is a pressing need to raise awareness of their ethical challenges and provide actionable recommendations for ethical research practice.