Basic data analysis
Mechanisms of ovarian cancer pain 5.This certificate is Part 2 of the Data Analyst Certificates Bundle - a comprehensive five-part program that provides training and practical guidance on the topic of data analytics. Analysis of pharmacological and non-pharmacological interventions for ovarian cancer pain 4. Development of diagnosis, prognosis, and pain markers 3. Classification of pain in ovarian cancer 2. We welcome contributions, including original research articles and reviews, from authors that explore but are not limited to the following: 1. In addition, screening for tumor diagnostic and prognostic markers to avoid advanced pain in ovarian cancer is equally important. This theme aims to enhance the understanding of ovarian cancer pain through big data analysis.
#Basic data analysis series#
The goal of this research theme is to bring together a series of articles focusing on the use and analysis of big data in basic and clinical ovarian cancer pain research, including large samples of clinical data as well as genomic data. In the context of the big data era, the use of machine learning and other tools to classify ovarian cancer pain can contribute to a further understanding of pain control. Multiple assessments are needed, because the type of pain and its level tend to shift with disease progression.
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One of the aspects of pain control at the end-of-life is understanding the origin of the pain experienced by the patient (somatic visceral or neuropathic), the level of the pain, and its location. The spectrum of symptoms in advanced ovarian cancer results from various complications and the involvement of different systems, due to the natural spread of the disease. Most ovarian cancer patients are diagnosed in an advanced stage and after the initial treatment experience disease recurrence, which eventually becomes palliative. Ovarian cancer is the eighth most common cause of cancer-related deaths in women. Study of individualized treatment and pain control in ovarian cancer
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