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This paper presents a deep learning approach for automatic detection and visual analysis of Invasive Ductal Carcinoma (IDC) tissue regions. The method proposed in this work is a convolutional neural network (CNN) for visual semantic analysis of tumor regions for diagnostic support. read more Detection of IDC is a time-consuming and challenging task, mainly because a pathologist needs to examine large tissue regions to identify areas of malignancy. Deep Learning approaches are particularly suitable for dealing with this type of problem, especially when many samples are available for training, ensuring high quality of the learned features by the classifier and, consequently, its generalization capacity. A 3-hidden-layer CNN with data balancing reached both accuracy and F1-Score of 0.85 and outperforming other approaches from the literature. Thus, the proposed method in this article can serve as a support tool for the identification of invasive breast cancer.Data imbalance is a well-known challenge in the development of machine learning models. This is particularly relevant when the minority class is the class of interest, which is frequently the case in models that predict mortality, specific diagnoses or other important clinical end-points. Typical methods of dealing with this include over- or under-sampling training data, or weighting the loss function in order to boost the signal from the minority class. Data augmentation is another frequently employed method - particularly for models that use images as input data. For discrete time-series data, however, there is no consensus method of data augmentation. We propose a simple data augmentation strategy that can be applied to discrete time-series data from the EMR. This strategy is then demonstrated using a publicly available data-set, in order to provide proof of concept for the work undertaken in [1], where data is unable to be made open.The space of clinical planning requires a complex arrangement of information, often not capable of being captured in a singular dataset. As a result, data fusion techniques can be used to combine multiple data sources as a method of enriching data to mimic and compliment the nature of clinical planning. These techniques are capable of aiding healthcare providers to produce higher quality clinical plans and better progression monitoring techniques. Clinical planning and monitoring are important facets of healthcare which are essential to improving the prognosis and quality of life of patients with chronic and debilitating conditions such as COPD. To exemplify this concept, we utilize a Node-Red-based clinical planning and monitoring tool that combines data fusion techniques using the JDL Model for data fusion and a domain specific language which features a self-organizing abstract syntax tree.Blood products and their derivatives are perishable commodities that require an efficient inventory management to ensure both a low wastage rate and a high product availability rate. To optimize blood product inventory, Blood Transfusion Services (BTS) need to reduce wastage by avoiding outdates and improving availability of different blood products. We took a blood product lifecycle approach and used advanced visualization techniques to design and develop a highly interactive web-based dashboard to audit retrospective data and consequently, to identify and learn from procedural inefficiencies based on analysis of transactional data. We present pertinent scenarios to show how the blood transfusion staff can use the dashboard to investigate blood product lifecycles so as to probe transition sequence patterns that led to wastage as a means to discover causes of procedural inefficiencies in the BTS.Book music is extensively used in street organs. It consists of thick cardboard, containing perforated holes specifying the musical notes. We propose to represent clinical time-dependent data in a tabular form inspired from this principle. The sheet represents a statistical individual, each row represents a binary time-dependent variable, and each hole denotes the "true" value. Data from electronic health records or nationwide medical-administrative databases can then be represented demographics, patient flow, drugs, laboratory results, diagnoses, and procedures. This data representation is suitable for survival analysis (e.g., Cox model with repeated outcomes and changing covariates) and different types of temporal association rules. Quantitative continuous variables can be discretized, as in clinical studies. The "book music" approach could become an intermediary step in feature extraction from structured data. It would enable to better account for time in analyses, notably for historical cohort analyses based on healthcare data reuse.Over the past 5 years, there has been an increase in the development of EHR-based models for predicting suicidal behaviour. Using the McGinn (2000) framework for creating clinical prediction rules, this study discusses the broad validation of one such predictive model in a context external to its derivation. Along with reporting performance metrics, our paper high-lights five practical challenges that arise when trying to undertake such a project including (i) validation sample sizes, (ii) availability and timeliness of data, (iii) limited or incomplete documentation for predictor variables, (iv) reliance on structured data and (v) differences in the source context of algorithms. We also discuss our study in the context of the current literature.Social media has become a predominant source of information for many health care consumers. However, false and misleading information is a pervasive problem in this context. Specifically, health-related misinformation has become a significant public health challenge, impeding the effectiveness of public health awareness campaigns and resulting in suboptimal responsiveness to the communication of legitimate risk-related information. Little is known about the mechanisms driving the seeding and spreading of such information. In this paper, we specifically examine COVID-19 tweets which attempt to correct misinformation. We employ a mixed-methods approach comprising qualitative coding, deep learning classification, and computerized text analysis to understand the manifestation of speech acts and other linguistic variables. Results indicate significant differences in linguistic variables (e.g., positive emotion, tone, authenticity) of corrective tweets and their dissemination level. Our deep learning classifier has a macro average performance of 0.82. Implications for effective and persuasive misinformation correction efforts are discussed.As Twitter emerged as an important data source for pharmacovigilance, heterogeneous data veracity becomes a major concern for extracted adverse drug reactions (ADRs). Our objective is to categorize different levels of data veracity and explore linguistic features of tweets and Twitter variables as they may be used for automatic screening high-veracity tweets that contain ADR-related information. We annotated a published Twitter corpus with linguistic features from existing studies and clinical experts. Multinomial logistic regression models found that first-person pronouns, expressing negative sentiment, ADR and drug name being in the same sentence were significantly associated with higher levels of data veracity (p less then 0.05), using medical terminology and fewer indications were associated with good data veracity (p less then 0.05), less drug numbers were marginally associated with good data veracity (p=0.053). These findings suggest opportunities for developing machine learning models for automatic screening of ADR-related tweets using key linguistic features, Twitter variables, and association rules.Oral anticancer agents (OAA) are increasingly prescribed to treat cancer because they are flexible and convenient to use. However, managing complex OAA regimens and life-threatening toxicities at home can be challenging for patients and their caregivers. It is urgent to better understand the supportive care needs for OAA and develop novel approaches to facilitating self-management and communicating about OAA. Guided by the chronic care model (CCM), we conducted a grounded theory-based study to analyze OAA-related online discussions and potential mHealth interventions. We found that patients and caregivers commonly used the online community to share personal experiences and concerns, exchange emotional and informational support, identify relevant resources, and obtain benefits of peer coaching. The findings deepen the understanding of the needs for OAA self-management and mHealth interventions, contributing to the development of mHealth models to enhance supportive care and improve communication among peer patients and between patients and providers.This study was aimed at identifying how telemedicine is used for rehabilitation of patients with cancer. An electronic literature search was conducted using the PubMed database covering January 2015 to October 2020. To be included in the review, studies had to report telerehabilitation interventions for patients with cancer. Randomized controlled trials, quasi-experimental studies, as well as feasibility and usability studies were included, and reviews were excluded. Overall, 33 eligible studies were found but only 22 were considered for inclusion. After a detailed analysis, 16 studies were included. Most of the studies concluded that telehealth systems supporting physical exercise were effective to improve function, quality of life, pain, satisfaction and muscle strength. Limitations in most of the studies included non-randomized design and limited number of subjects. We conluded that more studies are needed for stronger evidence of this type of treatment and to facilitate clinical practice in this field.Atopic dermatitis is a common chronic dermatological disease in childhood that can affect people's quality of life. The aim of this study was to inquire about the difficulties, needs and interests related to the disease that people with eczema and their caregivers have; in order to develop a tool that is useful for the follow-up of the illness. Electronic surveys were sent to potential users and interviews were conducted with professionals who are specialized on the subject. The main findings allowed us to understand the challenges and situations they face on a daily basis, such as the difficulties related to the family support, the queries on the eczema flare-ups, the struggles with the adherence to treatment and the needs of optimizing their quality of life. These results helped us design a tool that allows patients and their companions to better monitor their disease while optimizing communication with their health professionals.This study presents an online psoriasis community developed with dermatologists in a PHR. We describe the interaction of users with this platform and the relationship between the use of self-report questionnaires, their results and users' subsequent contact with the healthcare system. Out of 2175 users that interacted with the platform, 477 visited the forums. 60% of those who completed questionnaires presented at least one abnormal result that prompted a recommendation for an outpatient visit. Although our data suggest a trend, we failed to find a statistically significant association between questionnaire severity and visits scheduling. To our knowledge, this is the first study that analyses the relationship between patient self-reported disease severity and the subsequent contact with the healthcare system.