“…As shown in the figure, it is clear that blood parameters are indefinitely associated with cancer characteristics and their careful examination possibly helps in the easy diagnosis of breast cancer. The automatic text-mining functionality of VOSviewer and Word2vec is used for the classification of decision making variable in breast cancer surgery and to retrieve co-occurrence networks of terms associated with PCA 51 , 52 . Figure 7 The Word Cloud Generator Based on Text Network Visualization.…”
Metastatic Breast Cancer (MBC) is one of the primary causes of cancer-related deaths in women. Despite several limitations, histopathological information about the malignancy is used for the classification of cancer. The objective of our study is to develop a non-invasive breast cancer classification system for the diagnosis of cancer metastases. The anaconda—Jupyter notebook is used to develop various python programming modules for text mining, data processing, and Machine Learning (ML) methods. Utilizing classification model cross-validation criteria, including accuracy, AUC, and ROC, the prediction performance of the ML models is assessed. Welch Unpaired t-test was used to ascertain the statistical significance of the datasets. Text mining framework from the Electronic Medical Records (EMR) made it easier to separate the blood profile data and identify MBC patients. Monocytes revealed a noticeable mean difference between MBC patients as compared to healthy individuals. The accuracy of ML models was dramatically improved by removing outliers from the blood profile data. A Decision Tree (DT) classifier displayed an accuracy of 83% with an AUC of 0.87. Next, we deployed DT classifiers using Flask to create a web application for robust diagnosis of MBC patients. Taken together, we conclude that ML models based on blood profile data may assist physicians in selecting intensive-care MBC patients to enhance the overall survival outcome.
“…As shown in the figure, it is clear that blood parameters are indefinitely associated with cancer characteristics and their careful examination possibly helps in the easy diagnosis of breast cancer. The automatic text-mining functionality of VOSviewer and Word2vec is used for the classification of decision making variable in breast cancer surgery and to retrieve co-occurrence networks of terms associated with PCA 51 , 52 . Figure 7 The Word Cloud Generator Based on Text Network Visualization.…”
Metastatic Breast Cancer (MBC) is one of the primary causes of cancer-related deaths in women. Despite several limitations, histopathological information about the malignancy is used for the classification of cancer. The objective of our study is to develop a non-invasive breast cancer classification system for the diagnosis of cancer metastases. The anaconda—Jupyter notebook is used to develop various python programming modules for text mining, data processing, and Machine Learning (ML) methods. Utilizing classification model cross-validation criteria, including accuracy, AUC, and ROC, the prediction performance of the ML models is assessed. Welch Unpaired t-test was used to ascertain the statistical significance of the datasets. Text mining framework from the Electronic Medical Records (EMR) made it easier to separate the blood profile data and identify MBC patients. Monocytes revealed a noticeable mean difference between MBC patients as compared to healthy individuals. The accuracy of ML models was dramatically improved by removing outliers from the blood profile data. A Decision Tree (DT) classifier displayed an accuracy of 83% with an AUC of 0.87. Next, we deployed DT classifiers using Flask to create a web application for robust diagnosis of MBC patients. Taken together, we conclude that ML models based on blood profile data may assist physicians in selecting intensive-care MBC patients to enhance the overall survival outcome.
“…Hypothesis generated by the enhanced reading can inform surveys or be part of the design of clinical trials. For instance, the ETHOS survey [18, 19] used this tool to inform the panel about the semantic relevance of a list of preoperative features and postoperative outcomes. The panel expressed its opinion on approving or rejecting the proposed drivers after reviewing the quantitative information received.…”
<b><i>Introduction:</i></b> Books and papers are the most relevant source of theoretical knowledge for medical education. New technologies of artificial intelligence can be designed to assist in selected educational tasks, such as reading a corpus made up of multiple documents and extracting relevant information in a quantitative way. <b><i>Methods:</i></b> Thirty experts were selected transparently using an online public call on the website of the sponsor organization and on its social media. Six books edited or co-edited by members of this panel containing a general knowledge of breast cancer or specific surgical knowledge have been acquired. This collection was used by a team of computer scientists to train an artificial neural network based on a technique called Word2Vec. <b><i>Results:</i></b> The corpus of six books contained about 2.2 billion words for 300d vectors. A few tests were performed. We evaluated cosine similarity between different words. <b><i>Discussion:</i></b> This work represents an initial attempt to derive formal information from textual corpus. It can be used to perform an augmented reading of the corpus of knowledge available in books and papers as part of a discipline. This can generate new hypothesis and provide an actual estimate of their association within the expert opinions. Word embedding can also be a good tool when used in accruing narrative information from clinical notes, reports, etc., and produce prediction about outcomes. More work is expected in this promising field to generate “real-world evidence.”
“…Methodologies such as GRADE and Delphi interviews attempt to reconcile potentially conflicting viewpoints and assimilate opinion and experience from a large number of clinicians and place this in context with published data. The technique of text–mining can be employed to analyse decision drivers ( 44 ). Surgeons and other healthcare workers must be honest with patients when discussing cosmetic and other outcomes of breast reconstructive surgery.…”
Section: Development Of Oncoplastic Breast Surgerymentioning
This article provides an overview of the principles and techniques of oncoplastic and reconstructive breast surgery for patients with early-stage breast cancer. Oncoplastic breast surgery (OPBS) with partial breast reconstruction is a natural evolution in the application of breast conserving surgery and permits wide surgical resection of tumours that might otherwise mandate mastectomy and whole breast reconstruction. These reconstructive techniques must be optimally selected and integrated with ablative breast surgery together with non-surgical treatments such as radiotherapy and chemotherapy that may be variably sequenced with each other. A multidisciplinary approach with shared decision-making is essential to ensure optimal clinical and patient-reported outcomes that address oncological, aesthetic, functional and psychosocial domains. Future practice of OPBS must incorporate routine audit and comprehensive evaluation of outcomes.
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