scholarly journals Annotating and Detecting Topics in Social Media Forum and Modelling the Annotation to Derive Directions-A Case Study

2020 ◽  
Author(s):  
Athira B ◽  
Josette Jones ◽  
Sumam Mary Idicula ◽  
Anand Kulanthaivel ◽  
Enming Zhang
Keyword(s):  
Breast Cancer ◽  
Social Media ◽  
Deep Learning ◽  
Cancer Patients ◽  
Learning Algorithm ◽  
Treatment Options ◽  
Healthcare Sector ◽  
Final Decision ◽  
Breast Cancer Patients ◽  
Deep Learning Algorithm

Abstract The widespread influence of social media impacts every aspect of life, including the healthcare sector. Although medics and health professionals are the final decision makers, the advice and recommendations obtained from fellow patients are significant. In this context, the present paper explores the topics of discussion posted by breast cancer patients and survivors on online forums. The study examines an online forum, Breastcancer.org, maps the discussion entries to several topics, and proposes a machine learning model based on a classification algorithm to characterize the topics. To explore the topics of breast cancer patients and survivors, approximately 1000 posts are selected and manually labeled with annotations. In contrast, millions of posts are available to build the labels. A semi-supervised learning technique is used to build the labels for the unlabeled data; hence, the large data are classified using a deep learning algorithm. The deep learning algorithm BiLSTM with BERT word embedding technique provided a better f1-score of 79.5%. This method is able to classify the following topics: medication reviews, clinician knowledge, various treatment options, seeking and providing support, diagnostic procedures, financial issues and implications for everyday life. What matters the most for the patients is coping with everyday living as well as seeking and providing emotional and informational support. The approach and findings show the potential of studying social media to provide insight into patients' experiences with cancer like critical health problems.

scholarly journals Annotating and Detecting Topics in Social Media Forum and Modelling the Annotation to Derive Directions-A Case Study

2021 ◽  
Author(s):  
Athira B ◽  
Josette Jones ◽  
Sumam Mary Idicula ◽  
Anand Kulanthaivel ◽  
Enming Zhang
Keyword(s):  
Breast Cancer ◽  
Social Media ◽  
Deep Learning ◽  
Cancer Patients ◽  
Learning Algorithm ◽  
Treatment Options ◽  
Healthcare Sector ◽  
Final Decision ◽  
Breast Cancer Patients ◽  
Deep Learning Algorithm

Abstract The widespread influence of social media impacts every aspect of life, including the healthcare sector. Although medics and health professionals are the final decision makers, the advice and recommendations obtained from fellow patients are significant. In this context, the present paper explores the topics of discussion posted by breast cancer patients and survivors on online forums. The study examines an online forum, Breastcancer.org, maps the discussion entries to several topics, and proposes a machine learning model based on a classification algorithm to characterize the topics. To explore the topics of breast cancer patients and survivors, approximately 1000 posts are selected and manually labeled with annotations. In contrast, millions of posts are available to build the labels. A semi-supervised learning technique is used to build the labels for the unlabeled data; hence, the large data are classified using a deep learning algorithm. The deep learning algorithm BiLSTM with BERT word embedding technique provided a better f1-score of 79.5%. This method is able to classify the following topics: medication reviews, clinician knowledge, various treatment options, seeking and providing support, diagnostic procedures, financial issues and implications for everyday life. What matters the most for the patients is coping with everyday living as well as seeking and providing emotional and informational support. The approach and findings show the potential of studying social media to provide insight into patients' experiences with cancer like critical health problems.

scholarly journals Annotating and detecting topics in social media forum and modelling the annotation to derive directions-a case study

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
B. Athira ◽  
Josette Jones ◽  
Sumam Mary Idicula ◽  
Anand Kulanthaivel ◽  
Enming Zhang
Keyword(s):  
Breast Cancer ◽  
Social Media ◽  
Deep Learning ◽  
Cancer Patients ◽  
Learning Algorithm ◽  
Treatment Options ◽  
Healthcare Sector ◽  
Final Decision ◽  
Breast Cancer Patients ◽  
Deep Learning Algorithm

AbstractThe widespread influence of social media impacts every aspect of life, including the healthcare sector. Although medics and health professionals are the final decision makers, the advice and recommendations obtained from fellow patients are significant. In this context, the present paper explores the topics of discussion posted by breast cancer patients and survivors on online forums. The study examines an online forum, Breastcancer.org, maps the discussion entries to several topics, and proposes a machine learning model based on a classification algorithm to characterize the topics. To explore the topics of breast cancer patients and survivors, approximately 1000 posts are selected and manually labeled with annotations. In contrast, millions of posts are available to build the labels. A semi-supervised learning technique is used to build the labels for the unlabeled data; hence, the large data are classified using a deep learning algorithm. The deep learning algorithm BiLSTM with BERT word embedding technique provided a better f1-score of 79.5%. This method is able to classify the following topics: medication reviews, clinician knowledge, various treatment options, seeking and providing support, diagnostic procedures, financial issues and implications for everyday life. What matters the most for the patients is coping with everyday living as well as seeking and providing emotional and informational support. The approach and findings show the potential of studying social media to provide insight into patients' experiences with cancer like critical health problems.

Annotating and Detecting Topics from Social Media Forum and Modelling the Annotation to Derive Directions-A Case Study (Preprint)

2020 ◽  
Author(s):  
Athira B ◽  
Josette Jones ◽  
Sumam Mary Idicula ◽  
Anand Kulanthaivel ◽  
Sunandan Chakraborty ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Social Media ◽  
Semantic Annotation ◽  
Current Trend ◽  
Learning Model ◽  
Healthcare Sector ◽  
Final Decision ◽  
Breast Cancer Patients ◽  
Machine Learning Model

BACKGROUND Widespread influence on social media has its ramifications on all walks of life over the last few decades. Interestingly enough, the healthcare sector is a significant beneficiary of the reports and pronouncements that appear on social media. Although medics and other health professionals are the final decision-makers, advice or recommendations from kindred patients has consequential role. In full appreciation of the current trend, the present paper explores the topics pertaining to the patients, diagnosed with breast cancer as well as the survivors, who are discussing on online fora. OBJECTIVE The study examines the online forum of Breast Cancer.org (BCO), automatically maps discussion entries to formal topics, and proposes a machine learning model to characterize the topics in the health-related discussion, so as to elicit meaningful deliberations. Therefore, the study of communication messages draws conclusions about what matters to the patients. METHODS Manual annotation was made in the posts of a few randomly selected forums. To explore the topics of breast cancer patients and survivors, 736 posts are selected for semantic annotation. The entire process was automated using machine learning model falling into category of supervised learning algorithms. The effectiveness of those algorithms used for above process has been compared. RESULTS The method could classify following 8-high level topics, such as writing medication reviews, explaining the adverse effects of medication, clinician knowledge, various treatment options, seeking and supporting various matters, diagnostic procedures, financial issues and implications in everyday life. The model viz. Ensembled Neural Network (ENN) achieved a promising predicted score of 83.4 % F1-score among four different models. CONCLUSIONS The research was able to segregate and name the posts all into a set of 8 classes and supported by the efficient scheme for encoding text to vectors, the current machine learning models are shown to give impressive performance in modelling the annotation process.

A Scoping Review of Communication on Social Media - Breast Cancer

2021 ◽  
Vol 10 (1) ◽  
pp. 28-32
Author(s):  
Nikhil Shetty ◽  
Ye Yang ◽  
Onur Asan
Keyword(s):  
Breast Cancer ◽  
Social Media ◽  
Cancer Patients ◽  
Scoping Review ◽  
Healthcare Sector ◽  
Future Research ◽  
Breast Cancer Patients ◽  
Raising Awareness ◽  
Health Related ◽  
Use Of Social Media

Communication on social media enables people to express their views freely and makes them a part of the larger community. Social media is a form of communication that has been adopted in the healthcare sector gradually. Similarly, breast cancer patients also use social media to gain information and support from their providers and fellow cancer survivors. This study presents a scoping review of qualitative and quantitative studies to show different communication themes. The scoping review identified 38 eligible articles. The review identified three themes from the selected articles: raising awareness, social support, and reliability. These themes show the general trends and concerns among breast cancer patients and the use of social media. Future research needs to address these themes to enhance the online patient experience and use social media for health-related activities.

scholarly journals Lesion-Based Bone Metastasis Detection in Chest Bone Scintigraphy Images of Prostate Cancer Patients Using Pre-Train, Negative Mining, and Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 518
Author(s):  
Da-Chuan Cheng ◽  
Te-Chun Hsieh ◽  
Kuo-Yang Yen ◽  
Chia-Hung Kao
Keyword(s):  
Breast Cancer ◽  
Prostate Cancer ◽  
Deep Learning ◽  
Bone Metastasis ◽  
Cancer Patients ◽  
Bone Scintigraphy ◽  
Breast Cancer Patients ◽  
Location Detection ◽  
The Mean ◽  
Mean Sensitivity

This study aimed to explore efficient ways to diagnose bone metastasis early using bone scintigraphy images through negative mining, pre-training, the convolutional neural network, and deep learning. We studied 205 prostate cancer patients and 371 breast cancer patients and used bone scintigraphy data from breast cancer patients to pre-train a YOLO v4 with a false-positive reduction strategy. With the pre-trained model, transferred learning was applied to prostate cancer patients to build a model to detect and identify metastasis locations using bone scintigraphy. Ten-fold cross validation was conducted. The mean sensitivity and precision rates for bone metastasis location detection and classification (lesion-based) in the chests of prostate patients were 0.72 ± 0.04 and 0.90 ± 0.04, respectively. The mean sensitivity and specificity rates for bone metastasis classification (patient-based) in the chests of prostate patients were 0.94 ± 0.09 and 0.92 ± 0.09, respectively. The developed system has the potential to provide pre-diagnostic reports to aid in physicians’ final decisions.

Adopting a knowledge translation approach in healthcare co-production. A case study

2020 ◽  
Vol 58 (9) ◽  
pp. 1841-1862 ◽  
Author(s):  
Francesca Dal Mas ◽  
Helena Biancuzzi ◽  
Maurizio Massaro ◽  
Luca Miceli
Keyword(s):  
Breast Cancer ◽  
Knowledge Translation ◽  
Cancer Patients ◽  
Healthcare Sector ◽  
Emotional Engagement ◽  
Breast Cancer Patients ◽  
Case Study Methodology ◽  
Content Type ◽  
Production Program

PurposeThe paper aims to contribute to the debate concerning the use of knowledge translation for implementing co-production processes in the healthcare sector. The study investigates a case study, in which design was used to trigger knowledge translation and foster co-production.Design/methodology/approachThe paper employs a case study methodology by analysing the experience of “Oncology in Motion”, a co-production program devoted to the recovery of breast cancer patients carried on by the IRCCS C.R.O. of Aviano, Italy.FindingsResults show how design could help to translate knowledge from various stakeholders with different skills (e.g. scientists, physicians, nurses) and emotional engagement (e.g. patients and patients' associations) during all the phases of a co-production project to support breast cancer patients in a recovery path. Stewardship theory is used to show that oncology represents a specific research context.Practical implicationsThe paper highlights the vast practical contribution that design can have in empowering knowledge translation at different levels and in a variety of co-production phases, among different stakeholders, facilitating their engagement and the achievement of the desired outcomes.Originality/valueThe paper contributes to the literature on knowledge translation in co-production projects in the healthcare sector showing how design can be effectively implemented.

Communication Between Physicians and Older Women With Localized Breast Cancer: Implications for Treatment and Patient Satisfaction

2002 ◽  
Vol 20 (4) ◽  
pp. 1008-1016 ◽  
Author(s):  
Wenchi Liang ◽  
Caroline B. Burnett ◽  
Julia H. Rowland ◽  
Neal J. Meropol ◽  
Lynne Eggert ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Cancer Care ◽  
Treatment Options ◽  
Treatment Choice ◽  
Satisfaction With Care ◽  
Physician Communication ◽  
Breast Cancer Patients ◽  
Actual Treatment ◽  
The Impact

PURPOSE: To identify factors associated with patient-physician communication and to examine the impact of communication on patients’ perception of having a treatment choice, actual treatment received, and satisfaction with care among older breast cancer patients. MATERIALS AND METHODS: Data were collected from 613 pairs of surgeons and their older (≥ 67 years) patients diagnosed with localized breast cancer. Measures of patients’ self-reported communication included physician- and patient-initiated communication and the number of treatment options discussed. Logistic regression analyses were conducted to examine the relationships between communication and outcomes. RESULTS: Patients who reported that their surgeons mentioned more treatment options were 2.21 times (95% confidence interval [CI], 1.62 to 3.01) more likely to report being given a treatment choice, and 1.33 times (95% CI, 1.02 to 1.73) more likely to get breast-conserving surgery with radiation than other types of treatment. Surgeons who were trained in surgical oncology, or who treated a high volume of breast cancer patients (≥ 75% of practice), were more likely to initiate communication with patients (odds ratio [OR] = 1.62; 95% CI, 1.02 to 2.56; and OR = 1.68; 95% CI, 1.01 to 2.76, respectively). A high degree of physician-initiated communication, in turn, was associated with patients’ perception of having a treatment choice (OR = 2.46; 95% CI, 1.29 to 4.70), and satisfaction with breast cancer care (OR = 2.13; 95% CI, 1.17 to 3.85) in the 3 to 6 months after surgery. CONCLUSION: Greater patient-physician communication was associated with a sense of choice, actual treatment, and satisfaction with care. Technical information and caring components of communication impacted outcomes differently. Thus, the quality of cancer care for older breast cancer patients may be improved through interventions that improve communication within the physician-patient dyad.

scholarly journals Systemic Treatment Options for HER2-Positive Breast Cancer Patients with Brain Metastases beyond Trastuzumab: A Literature Review

Breast Care ◽  
2017 ◽  
Vol 12 (3) ◽  
pp. 168-171 ◽  
Author(s):  
Elena Laakmann ◽  
Volkmar Müller ◽  
Marcus Schmidt ◽  
Isabell Witzel
Keyword(s):  
Breast Cancer ◽  
Brain Metastases ◽  
Cancer Patients ◽  
Systemic Treatment ◽  
Treatment Options ◽  
Cytotoxic Agents ◽  
Breast Cancer Patients ◽  
Her2 Positive ◽  
Her2 Positive Breast Cancer ◽  
Positive Breast Cancer

Background: The incidence of brain metastases (BM) in breast cancer patients has increased. Many retrospective analyses have shown that first-line treatment with trastuzumab prolongs survival in patients with HER2-positive BM. In contrast, the evidence for other therapies targeting HER2 for patients with BM is rare. Methods: The aim of this review is to update the reader about current systemic treatment options in patients with HER2-positive metastatic breast cancer with BM who had already received trastuzumab. A literature search was performed in the PubMed database in June 2016. 30 relevant reports concerning the efficacy of trastuzumab emtansine (T-DM1), lapatinib and its combination with other cytotoxic agents, pertuzumab and novel HER2-targeting substances were identified. Results: There is limited but promising evidence for the use of T-DM1 and pertuzumab in the treatment of BM. Up to now, most reported studies used lapatinib as treatment of HER2-positive breast cancer with BM, a treatment with only a modest effect and a high toxicity profile. The combination of lapatinib with cytotoxic agents seems to result in better response rates. Conclusion: Further prospective investigations are needed to investigate the efficacy of the established and novel HER2-targeting agents on BM in HER2-positive breast cancer patients.

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