Voice of Cancer Patients: Analysis of patient concerns regarding cognitive deficits associated with treatment for breast cancer.

2015 ◽  
Vol 33 (28_suppl) ◽  
pp. 82-82
Author(s):  
Sangeeta Aggarwal ◽  
Mingfeng Liu ◽  
Rishi Sharma ◽  
Fred Yang ◽  
Ankit Gupta ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Language Processing ◽  
Data Science ◽  
Strong Association ◽  
Supportive Therapy ◽  
Relevant Information ◽  
Cancer Therapies ◽  
Online Forums ◽  
Cancer Treatments ◽  
System A

82 Background: Patients undergoing breast cancer treatment often report Symptoms of Cognitive Deficit (SCD). Many of them share their experiences on online forums, which contain millions of freely shared messages that can be used to analyze these SCD. Unfortunately, this data is unstructured, making it difficult to analyze. In this project we organize this data using methods from Big Data Science (BDS) and analyze it by creating a Decision Support System (DSS): an interface that can be used by patients and providers to understand how SCD are associated with specific types – hormonal only (HT), chemo only (CT), or both (CT/HT) – of breast cancer therapies. Methods: We collected 3.5 million unique messages from 20 unrestricted breast cancer forums that provide clinically relevant information. We next built custom ontologies for breast cancer treatments, SCD, and supportive therapies. Then, we created a DSS using methods from BDS, including topic modeling, information retrieval, and natural language processing to extract the relevant data from these messages. We also used token windows and co-occurrence-based algorithms to associate treatment with SCD and supportive therapies. To use this system, a user provides disease-related parameters and the treatment. The DSS then gives the percentage of messages discussing SCD for a similar cohort of patients and the percentage of messages that discuss supportive therapies for each of these SCD. Results: We found 15719 messages that had strong association of SCD with treatments. 3355 messages were from HT patients, 5740 messages were from CT patients, and 9095 messages were from CT/HT patients. Among HT, 28.18% patients taking aromatase inhibitors and 19.20% taking tamoxifen associated SCD to HT. Among CT, 35.26% patient receiving taxane containing chemo associated SCD to CT. SCD worsened during HT for CT/HT patients. Suggestive therapy: 80 messages found Vitamin B12 and B6 useful, 65 suggested Acetyle-L-Carnitine, and 50 suggested playing word games. Conclusions: Using methods from BDS, our DSS reliably associates SCD with HT, CT and CT/CT, and suggests supportive therapies. More research is needed to evaluate the role of supportive therapy for SCD.

scholarly journals eGARD: Extracting associations between genomic anomalies and drug responses from text

2017 ◽  
Author(s):  
A. S. M. Ashique Mahmood ◽  
Shruti Rao ◽  
Peter McGarvey ◽  
Cathy Wu ◽  
Subha Madhavan ◽  
...  
Keyword(s):  
Patient Outcomes ◽  
Language Processing ◽  
Relevant Information ◽  
Biomedical Literature ◽  
Cancer Treatments ◽  
Therapeutic Implications ◽  
Integral Role ◽  
Speed Up ◽  
Best Fit ◽  
Genomic Anomalies

AbstractTumor molecular profiling plays an integral role in identifying genomic anomalies which may help in personalizing cancer treatments, improving patient outcomes and minimizing risks associated with different therapies. However, critical information regarding the evidence of clinical utility of such anomalies is largely buried in biomedical literature. It is becoming prohibitive for biocurators, clinical researchers and oncologists to keep up with the rapidly growing volume and breadth of information, especially those that describe therapeutic implications of biomarkers and therefore relevant for treatment selection. In an effort to improve and speed up the process of manually reviewing and extracting relevant information from literature, we have developed a natural language processing (NLP)-based text mining (TM) system called eGARD (extracting Genomic Anomalies association with Response to Drugs). This system relies on the syntactic nature of sentences coupled with various textual features to extract relations between genomic anomalies and drug response from MEDLINE abstracts. Our system achieved high precision, recall and F-measure of up to 0.95, 0.86 and 0.90, respectively, on annotated evaluation datasets created in-house and obtained externally from PharmGKB. Additionally, the system extracted information that helps determine the confidence level of extraction to support prioritization of curation. Such a system will enable clinical researchers to explore the use of published markers to stratify patients upfront for ‘best-fit’ therapies and readily generate hypotheses for new clinical trials.

Utilization of online crowdfunding for alternative cancer treatments at home and abroad.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14044-e14044
Author(s):  
Joshua David Gruhl ◽  
John Stuart Peterson ◽  
Sydney Davis ◽  
Jaxon Carey Olsen ◽  
Matthew Parsons ◽  
...  
Keyword(s):  
Cancer Treatment ◽  
English Language ◽  
Stage Iv ◽  
Light Therapy ◽  
Relevant Information ◽  
Cancer Therapies ◽  
Cancer Treatments ◽  
Primary Tumors ◽  
Stage Iv Disease ◽  
The Usa

e14044 Background: The use of alternative cancer treatments has been associated with decreased survival. However, little is known about the types of individuals who seek out these therapies, the alternative therapies pursued, and the associated costs. We utilized GoFundMe campaigns to characterize the types of patients who sought alternative cancer treatments, the types of therapies pursued, and the associated costs. Methods: We queried GoFundMe ( www.gofundme.com ) for English language campaigns using the term “alternative cancer treatment” using custom code for web scraping on 10/25/2019. We identified 1000 campaigns between 2011-2019, of which, 795 had received donations and were used for analysis. We studied each individual campaign in detail and extracted relevant information. Results: The majority of patients were female (63.5%). The most common cancer types were breast (25.3%), colorectal (10.8%), and lung (5.5%) cancer. Of patients reporting cancer stage, 79.3% had stage IV disease. Of those reporting prior cancer treatment history, 34.8% had never undergone traditional cancer treatment; 42.1%, 46.8%, and 26.6% had undergone prior surgery, chemotherapy, and radiotherapy, respectively. The most common proposed alternative treatments were vitamins and minerals (23.4%), herbs and botanicals (16.1%), special diets (13.0%), supplements (10.8%), heat or light therapy (7.8%), IV infusions (7.8%), homeopathic/naturopathic therapies (7.2%), and hyperbaric oxygen therapy (5.9%). Among all campaigns, a total of $36,394,110 was requested and a total of $8,601,759 (23.9%) was raised. The median campaign fundraising goal was $25900 (US dollars; Interquartile range [IQR] $1000 – $50000); the median amount donated was $5805 (IQR $2595 – $12580). These costs include travel as 629 (79.1%) campaigns were for patients residing in the USA, 70 (8.8%) in Europe, 62 (7.8%) in Canada, and 34 (4.3%) in other regions, from which, 54.3% of patients stated that they planned to travel internationally—most commonly to Mexico—for their alternative cancer treatments. Conclusions: Millions of dollars have been requested and raised between 2011 and 2019 for alternative cancer treatments. The majority of patients sought treatment at alternative clinics internationally, were females, had stage IV disease and primary tumors of the breast, colon/rectum or lung, who had previously undergone traditional cancer therapies, and highlight a group of patients where improved communication/education between providers and patients appears to be needed.

scholarly journals Voice Analysis of Cancer Experiences Among Patients With Breast Cancer: VOICE-BC

2021 ◽  
Vol 8 ◽  
pp. 237437352110480
Author(s):  
Ernest H Law ◽  
Maria J Auil ◽  
Patricia A Spears ◽  
Kiersten Berg ◽  
Randall Winnette
Keyword(s):  
Breast Cancer ◽  
Language Processing ◽  
Early Stage ◽  
Recurrence Risk ◽  
Disease Recurrence ◽  
Online Forums ◽  
Voice Analysis ◽  
Mixed Methods Approach ◽  
Cluster Network ◽  
Quantitative Metrics

Patient experience literature in early-stage breast cancer (eBC) is limited. This study used a mixed-methods approach to examine patient conversations from public online forums to identify and evaluate eBC-related themes. Among 60,000 eBC-related posts published September 2014–2019, text from a random subset of 15,000 posts was extracted and grouped into linguistically similar, mutually exclusive clusters using an advanced natural language processing (NLP) algorithm. Clusters were characterized using four quantitative metrics: betweenness centrality (linguistic similarity to other areas of the cluster network), sentiment (general attitude toward a topic), recency (average date of posts), and volume (total number of posts). This analysis represented 3906 unique users (67% and 33% obtained from cancer–specific and general health/nonhealth forums, respectively). Of the 27 clusters identified, most important were “discussing recurrence & progression,” “understanding diagnosis & prognosis,” and “understanding cancer, biomarkers, and treatments.” Several major themes related to recurrence risk, diagnosis, monitoring, and treatment were identified. Additional emphasis on communicating the disease recurrence risk and shared decision-making could strengthen patient-clinician partnerships.

Breast Cancer Resistance Protein: A Potential Therapeutic Target for Cancer

2020 ◽  
Vol 21 ◽  
Author(s):  
Sonali Mehendale-Munj
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Resistance Protein ◽  
Protein A ◽  
The Body ◽  
Cancer Therapies ◽  
Cancer Resistance ◽  
Lung Cancers ◽  
Resistance Protein ◽  
Atp Regulation ◽  
Treatment Procedures

: Breast Cancer Resistance Protein (BCRP) is an efflux transporter responsible for causing multidrug re-sistance(MDR). It is known to expel many potent antineoplastic drugs, owing to its efflux function. Efflux of chemothera-peutics because of BCRP develops resistance to manydrugs, leading to failure in cancer treatment. BCRP plays an important role in physiology by protecting the organism from xenobiotics and other toxins. It is a half-transporter affiliated to theATP-binding cassette (ABC) superfamily of transporters, encoded by the gene ABCG2 and functions in response to adenosine triphosphate (ATP). Regulation of BCRP expression is critically controlled at molecular levels which help in maintaining the balance of xenobiotics and nutrients inside the body. Expression of BCRP can be found in brain, liver, lung cancers and acute myeloid leukemia (AML). Moreover, it is also expressed at high levels in stem cells and many cell lines. This frequent expression of BCRP has an impact on the treatment procedures and if not scrutinized may lead to failure of many cancer therapies.

scholarly journals Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 204
Author(s):  
Charlyn Villavicencio ◽  
Julio Jerison Macrohon ◽  
X. Alphonse Inbaraj ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Data Science ◽  
Naive Bayes ◽  
The Philippines ◽  
Naïve Bayes ◽  
Social Networking Site ◽  
Bayes Model ◽  
The Government ◽  
Processing Techniques

A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government’s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Naïve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines.

scholarly journals Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation

2021 ◽  
Vol 18 (12) ◽  
pp. 6408
Author(s):  
Mario Jojoa Acosta ◽  
Gema Castillo-Sánchez ◽  
Begonya Garcia-Zapirain ◽  
Isabel de la Torre Díez ◽  
Manuel Franco-Martín
Keyword(s):  
Health Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Language Processing ◽  
Health Care Professionals ◽  
Ground Truth ◽  
Relevant Information ◽  
Free Text

The use of artificial intelligence in health care has grown quickly. In this sense, we present our work related to the application of Natural Language Processing techniques, as a tool to analyze the sentiment perception of users who answered two questions from the CSQ-8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satisfaction level of the participants involved, with a view to establishing strategies to improve future experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks, and transfer learning, so as to classify the inputs into the following three categories: negative, neutral, and positive. Due to the limited amount of data available—86 registers for the first and 68 for the second—transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02% and 90.53%, respectively, based on ground truth labeled by three experts. Finally, we proposed a complementary analysis, using computer graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages.

scholarly journals Preferences of Canadian Patients and Physicians for Treatment of HR+/HER2− Advanced Breast Cancer

2021 ◽  
Vol 28 (1) ◽  
pp. 491-508
Author(s):  
Daniel Stellato ◽  
Marroon Thabane ◽  
Caitlin Eichten ◽  
Thomas E. Delea
Keyword(s):  
Breast Cancer ◽  
Advanced Breast Cancer ◽  
Cancer Progression ◽  
Past Research ◽  
Advanced Breast ◽  
Cancer Treatments ◽  
Patient Advocacy Group ◽  
Treatment Alternatives ◽  
E Mail

(1) Background: Past research suggests that patients with advanced breast cancer prefer treatments with improved clinical outcomes and lower risk of side effects. Evidence on preferences of Canadian patients and physicians for treatments for advanced breast cancer is limited. (2) Methods: Patients’ and physicians’ preferences for treatments for HR+/HER2−, pre-/peri-menopausal advanced breast cancer were assessed by an online discrete choice experiment (DCE). Treatment alternatives were characterized by seven attributes regarding dosing, efficacy, and toxicities, with levels corresponding to those for ribociclib plus a non-steroidal aromatase inhibitor (NSAI), NSAI, and tamoxifen. For patients, impacts of advanced breast cancer on quality of life (QOL) and ability to work/perform activities of daily living also were assessed. Patients were recruited by a Canadian breast cancer patient advocacy group through email and social media. Physicians were recruited by email. (3) Results: Among 118 patients starting the survey, 23 completed ≥ 1 DCE question (19%). Among 271 physicians who were sent the e-mail invitation, 21 completed ≥ 1 DCE question (8%). For both patients and physicians, the increased probability of remaining alive and without cancer progression over 2 years was the most important attribute. A treatment with attributes consistent with ribociclib plus NSAI was chosen by patients and physicians in 70% and 88% of the time, respectively. A substantial proportion of patients reported worrying about future diagnostic tests and their cancer getting worse; (4) Conclusions: Canadian patients and physicians are generally concordant in preference for advanced breast cancer treatments, preferring ribociclib plus NSAI to other options.

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