Use of Big Data and Predictive Analytics to Analyze Cancer Treatment

A definitive cure for cancer appears to be a long way off, despite significant improvements in the quality of research, life-prolonging medications, and surgeries.

How can AI and big data in oncology improve the current scenario in terms of long-term diagnostics and treatment?

With a few notable exceptions, health professionals have discovered ways to inhibit the life-threatening components of all known terminal diseases. Persistent research and sheer willpower have enabled global healthcare to eradicate diseases such as measles and, to a lesser extent, more recent ones such as Ebola. Cancer presents a unique set of challenges in comparison to nearly all other fatal diseases we have encountered in the past. To begin, cancer is not a single disease but a collection of them. Second, cancer accelerates your demise through the use of your own cells, in contrast to the majority of other fatal diseases that involve external agents. Additionally, as previously stated, several types of cancer frequently recur following treatment. Additionally, the fact that malignant cells mutate in unpredictable and wild ways does not help matters. As a result, cancer continues to claim several thousand lives each year, with the death toll reaching nearly ten million by 2020.

As previously stated, there is a wealth of research-generated data available on the various types of cancer, their typical symptoms, and available treatments and medications to keep patients alive and healthy for longer. This data can be used to fuel AI-powered predictive analysis in order to improve the success of future cancer diagnoses and treatments. As is well known, AI is already being used in a variety of areas of healthcare. Thus, how can AI benefit the field of oncology?

AI Aids in the Early Diagnosis of Cancer

Accurately diagnosing cancer in an individual at an early stage has the potential to help them avoid death from the disease at a later stage. In such a situation, healthcare systems can greatly benefit from automated tools that can monitor cellular formations closely in order to determine whether tumors, malignant or benign, can form in a person’s body.

For this purpose, AI’s data processing and analytical capabilities can be put to good use. Diagnostic operations can be performed using a combination of machine learning and computer vision. Once a person’s potentially cancerous cells are identified, healthcare professionals can conduct tests to determine whether the tumor is malignant and metastatic or not. The entire process of cancer diagnosis can be carried out in a systematic manner. To begin, a full-body examination of a suspected cancer patient must be conducted to rule out any anomalies in their vital statistics. Cancers can generally be detected if a person loses weight rapidly or experiences a loss of appetite, among other indicators.

By categorizing factors such as the radius, perimeter, compactness, and proximity to organs, AI models and neural networks can be trained to identify cancerous tumors using computer vision. Often, such factors can help in determining the type of treatment that can be used to address abnormal cellular growth. Following that, we’ll dig deeper into some of the ways that big data and predictive analytics impact oncology.

Oncology Predictive Analysis and Big Data

As the name implies, predictive analysis uses historical data to forecast future events. Thus, utilizing this capability of an AI system in the future may be beneficial for cancer research.

Predictive analysis can be an effective tool for assisting healthcare professionals in diagnosing and treating cancer patients. It enables such experts to not only detect tumors and classify them according to the level of danger they pose to patients, but also to adhere to global healthcare privacy guidelines.

As previously stated, artificial intelligence was introduced into the field of healthcare to eliminate human error at every stage of disease diagnosis and treatment. As a result, by incorporating cancer-related treatment strategies into an AI system, doctors can reduce the number of errors associated with the use of inappropriate treatments for specific types of cancer.

In oncology, predictive analytics is beneficial for identifying patients at high risk of developing cancer. Patients in this category frequently experience cancer relapses despite receiving high-intensity treatments such as chemotherapy or other types of surgery. Even the most experienced health professionals may have difficulty identifying such patients at an early stage, but machine learning can identify specific patterns in them and forecast the possible reappearance of cancer cells. By identifying such patients early, costly treatments can be avoided, and health professionals can instead focus on malignancy prevention measures (medicines and lifestyle recommendations).

Second, AI-based predictive analysis may enable health experts to conduct in-depth studies of tumor characteristics in specific patients. These studies enable these workers to identify individuals whose bodies are capable of undergoing chemotherapy without experiencing significant post-treatment damage. Certain patients’ bodies may respond more positively to specific treatments than others.

Finally, predictive analysis is a valuable tool in the field of pathology and biopsy. One of the primary areas of concern in cancer treatment is the possibility of using excessive or ineffective treatments on patients who may not require them. As a result, such patients have a greater chance of dying as a result of excessive treatment than patients with cancer. As a result, predictive analysis systems, such as Google’s AI tool, improve cancer diagnosis accuracy, allowing doctors to focus on measures other than powerful treatments.

Apart from these, predictive analytics has the potential to significantly improve other aspects of cancer diagnosis. For instance, machine learning models could be used to create sequencing panels in the future, allowing large healthcare organizations to avoid screening an entire population for cancer blindly. Due to the numerous benefits predictive analytics provides in the field of oncology, health experts in the field are increasingly utilizing it.

Cancer, as we all know, is a complicated beast. Additionally, almost every day, new discoveries about the concept are made. Treating cancer patients requires healthcare workers to possess and evaluate massive amounts of reference data, which is physically impossible. Addressing such a complex problem has taken modern healthcare several years and several deaths, with mostly mixed to mildly encouraging outcomes.

This is where big data in oncology truly shines. Health researchers can leverage big data analysis tools that continuously absorb new information. When combined with AI and predictive analysis, oncologists can derive valuable insights from data in order to make the best treatment decisions. At the Massachusetts General Hospital, one such tool is currently being used to diagnose the type of breast cancer in female patients, allowing oncologists to focus on the type of treatment required for each patient. To forecast the element of patient risk, big data and predictive analysis work in tandem. AI-powered systems can be used to incorporate a wealth of patient data, including family history, hereditary cancer occurrences, prior biopsies, and issues with hormonal production, in order to determine the severity of cancer present in a patient’s body.

Due to the disease’s highly diverse (and ever-changing) nature, it becomes critical for oncologists to utilize all available data-that is, virtually all information about cancer discovered throughout history-when deploying a treatment strategy from patient to patient. Due to the fact that humans are involved in the data collection and analysis processes, traditional cancer detection and treatment tools and models may be inconsistent. By incorporating AI and big data into oncology, this aspect of the process is eliminated. Additionally, the technologies enable more precise, cost-effective, and time-efficient cancer treatments. To ensure these things, massive amounts of historical data and machine learning are analyzed.

Additionally, big data in oncology is beneficial for analyzing the protein levels and types found in benign and malignant cancer cells. While that piece of information may seem insignificant, it is important to remember that cancer treatment is also influenced by such factors. Consider how AI and big data in oncology are similar to two sides of the same coin. Both elements are interdependent, which means that one cannot thrive without the other. The combination of big data and artificial intelligence provides a significant boost to healthcare in general, and particularly to our fight against cancer. As previously stated, big data provides the information upon which AI and predictive analysis can operate. With these two technologies, it’s not difficult to imagine us eventually conquering the long-standing health hazard known as cancer.

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