Data science plays a pivotal role in monitoring patient health and informing the necessary measures to prevent the occurrence of potential diseases. Data scientists use powerful analytical predictive tools to diagnose chronic diseases at an early stage. The application of data science in the healthcare industry was first done in 2008.
Google Company and the production of health tools!
Google employees have discovered that they can map the flu outbreak very quickly by tracking spatial data in flu-related searches. Existing CDC maps of influenza document cases, FluView, are updated only once a week. Google quickly introduced a competitive tool called Google Flu Trends with frequent updates, but it didn’t work!
In 2013, Google estimated that the number of cases of the flu was about twice as high. The tool seemed to have a secret approach to finding the link between a word search and flu-related terms, which means that the Flu Trends algorithm sometimes stores too many unrelated words or phrases like “high school basketball”! Even so, owning one is still beyond the reach of the average person. Here are some powerful and accurate healthcare tools developed in the years following Google’s initial efforts, all of which use data science.
1) LYNA tool and early detection of cancerous tumors
Google in California has not stopped using data science in healthcare. In fact, the company has developed a new tool called LYNA to detect breast cancer tumors that metastasize to surrounding lymph nodes, which may be difficult for the human eye to see, especially when the tumor has just begun to grow. In one experiment, LYNA – an acronym for lymph node assistant – accurately identified 99% of the time using a machine learning algorithm. However, more tests are needed before doctors can use them in hospitals.
2) The popular Clue app during pregnancy
Research in the German city of Berlin on the popular Clue program uses data science to track the menstrual cycle and fertility of users by tracking the start date of the cycle, mood, stool type, hair condition, and many other criteria. Behind the scenes, data scientists use tools such as Python and Jupyter’s Notebook to extract this wealth of anonymous information. Users are then informed algorithmically about their problem when they are fertile, late in life, or if they are at high risk for conditions such as ectopic pregnancy, in most cases before something special happens to them.
3) Oncora software and effective cancer treatment
Researchers in Philadelphia, Pennsylvania, develop senior Oncora software using machine learning to create personalized recommendations for current cancer patients based on past patient data. Healthcare facilities using the company’s platforms include Northwell Health Services in New York. Their radiology team worked with Oncora data scientists to extract 15-year data on diagnoses, treatments, outcomes and side effects from more than 50,000 cancer cases. Based on these data, the Oncora algorithm learned to propose chemotherapy and personal radiation regimens.
Advances in pharmacological research to find a cure for cancer and Ebola
BERG Health Company
Cancer, as one of the most common and deadly diseases, has been the subject of regular scientific research. The number of cancer patients is growing. BERG Health, a Boston-based healthcare company, is transforming the cancer drug market with the widespread use of data science. Using powerful machine learning algorithms, the company extracted and analyzed biological samples from more than 1,000 patients. With more than 14 trillion data points in each instance, there was a lot of information for the AI algorithm.
As a result, the company developed the drug BPM 31510, which detects and stimulates the natural death of cells damaged by the disease. Therefore, cancer cells can be removed naturally from the human body, without widespread drug and further damage to the patient’s health. While the drug is being carefully tested, it gives us a clear understanding of the evolutionary potential that data science and machine learning technologies can provide to the pharmaceutical industry. Finding a way to advance these areas of research could lead to discoveries in the treatment of AIDS, the Ebola virus, or Zika.
Atomwise Company and data science in its industry
In another case, Atomwise, an artificial intelligence technology startup, has recently made progress in seeking Ebola treatment. The company has used virtual models and neural networks to evaluate how 7,000 available drugs interact with the virus. The experiment, carried out by an AI-based program, lasted only about a day instead of months, leading to potentially promising discoveries and proving that two drugs have been tested. Makes human cells resistant to the virus.
While research is still ongoing, these preliminary results indicate the enormous potential of such an approach in pharmaceutical research. “If we can deal with deadly viruses months or years faster, it could be tens of thousands of lives. Imagine how many people might survive the next epidemic,” said Alexander Levy, a researcher at Atomwise. “Because there is a technology like Atomwise.”
Data science specialist in the healthcare industry: general and specific skills
The main goal of health care organizations is to provide quality treatment at a reasonable cost. To maintain high standards of patient care, providers need to make the right medical decisions. The sheer volume of unstructured health care data complicates decisions.
It is essential that all call center records, physician notes, reports, prescriptions, laboratory results, and status summaries are stored quickly and accurately. The list of skills and responsibilities required for a data science specialist in the medical industry is summarized, and they need to know how to deal with a large data set on the one hand and a small number of individuals on the other. In addition, the specialist needs to acquire at least basic medical knowledge and a deep understanding of the healthcare industry.
Adequate knowledge helps a data expert determine what data is needed to execute a particular project and interpret the results of analytical and modeling work.
The bright future of data science in the healthcare industry
From predicting treatment outcomes to curing cancer and more effective patient care, data science health care has proven to be a very important part of the industry’s future. Following the above examples, there are three main factors that drive health innovation:
- Advances in technology
- The growth of digital consumerism
- Need to fight rising costs
While data science provides tools and methods to extract real value from non-structural patient information, it ultimately contributes to the efficiency, accessibility, and personalization of health care. The number of health care providers making data-based decisions is slowly but steadily increasing. In 2015, only 15 percent of hospitals used data science and forecasting analysis to prevent readmission, which a year later, 31 percent of institutions said had been doing so for more than a year. Give.