Original article was published on Becoming Human: Artificial Intelligence Magazine
Not every disease is fatal, we know this well. Some patients’ bodies to take up the fight against the disease with a greater chance. What just made these patients more resilient, and how can this be facilitated?
The roots of data science, just like in the case of artificial intelligence appeared in the previous century and thus become widespread in recent years. This science allows for a structured analysis of data and varieties of tools and algorithms for construing change.
The elevated level of gained knowledge thus can shed light on facts and contexts that would remain hidden from the human intellect. We can outline patterns, future scenarios that — while not equal to a prediction — can embark on the challenges of the future with more confidence.
It lends techniques from several fields, including math, statistics, and computer science.
Use cases in healthcare
Data analysis can also be used in health care to develop more advanced and effective therapies. Thereby increasing the life expectancy of the population. Data science is used in healthcare in several areas, including more accurate diagnosis, drug development, and prevention.
To be able to do this, professionals must first gather the amount of data that is sufficient to perform the analysis. The human brain and muscles produce two terabytes of data per day. This amount of data is already enough to gain insight into the hidden processes that take place in the body.
The number one public enemy is still cancer, which is projected to cause 1.8 million illnesses in the United States, of which more than 600,000 will be lethal.
A healthcare startup in Boston is developing BPM 31510, a drug that detects and triggers the natural death of cells damaged by the disease. With these drugs, the cancer cells can be removed from the human body naturally, without extensive medication and further damage to the patient’s health.
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To be able to do this, BERG Health used machine learning algorithms to extract and analyze data from samples of 1,000 patients.
This kind of extensive technology use on healthcare development can help lots of people in the future, with less harm and less money spent by the patient.
Advances in health care have been able to rid humanity of many diseases, including deaths such as plague or smallpox. There are still diseases that we can’t cure for sure. Perhaps it is data science and machine learning that will bring the desired next age of development, in which we can also say goodbye to dreaded diseases like cancer.
Other use cases:
Data science and machine learning can be utilized not only in healthcare, but also in areas such as agriculture, telecommunications, or finance.
From the data obtained from the previously recorded market price movements, we can make models that result in more accurate predictions than a human can do. The repetitive patterns recognized in the data tell the financial professional when and how much to buy or sell from the asset being traded. Algorithms created with machine learning technology produce much more precise results and low degrees of drawdowns, as can be seen in the results of the bot developed by b-cube.ai fintech startup.
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How can data science and machine learning save lives? was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.