How Efficiently can Ml Predict the Progress of Cancer?

How Efficiently can Ml Predict the Progress of Cancer?

By Healthcare Tech Outlook | Friday, August 16, 2019

ML models are steadily overtaking pathologists in their capability of accurately predicting the progress of cancer.

FREMONT, CA: Machine learning (ML) is transforming the way humans live, steadily permeating everyday processes. Its applications range across a variety of sectors, including transport, manufacturing, healthcare, and so on. Researchers are exploring the potential of ML in the treatment of cancer.

The advent of modern technologies has enabled physicians to gather and store vast troves of cancer-related data. Despite the availability of the data, the accurate prediction of disease outcomes has always been challenging for physicians. The emergence of ML has equipped medical researchers with a robust tool to enhance their capabilities.

Physicians can identify the patterns in the complex datasets and determine the relation between them using ML techniques. The insights will enable them to predict the outcomes of different cancer types effectively. The rising significance of personalized medicine and the utilization of ML techniques are enhancing cancer prediction and prognosis.

One of the ML trends involves the consolidation of clinical and genomic data, utilizing it for training the predictive models. The application of the ML techniques will potentially augment the accuracy of predicting cancer susceptibility, recurrence, and survival.

Over the last few years, the accuracy of cancer prediction has witnessed a significant rise, almost up to 20 percent, with the integration of ML techniques. Many studies explore approaches related to the profiling of circulating miRNAs, which offers promising outcomes for cancer detection and identification.

However, several of these methods are hindered by low sensitivity, especially during the screening at the early stages. Consequently, they fail to differentiate between benign and malignant tumors efficiently. Although gene signatures exhibit significant potential in the enhancement of cancer prognosis, there has been scarce progress in this sector.

The ML technology can be employed to learn from the data samples to estimate the unknown dependencies in a system from a given dataset and utilizing the estimations to predict new system outputs. It has augmented biomedical research, enabling acceptable generalization by identifying a given set of biological samples in n-dimensional space using various techniques and algorithms.

Machines can not only work consistently, but they also possess the capability of conducting thousands of biopsies in a matter of seconds. Although ML models are in the testing and experimentation phase for cancer prognoses, the datasets are getting larger and better, facilitating the development of accurate models. ML models still have ways to go, but it will not be long before it dominates the pathology sector.

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