New algorithm more accurately predicts life expectancy after heart failure
A new algorithm developed by UCLA researchers more accurately predicts which people will survive heart failure, and for how long, whether or not they recieve a heart transplant, This algorithm would allow doctors to make more personalized assessments of people who are waiting for awaiting heart trasnplants, which in turn could enable health care prviders to make better use of limited life saving resources and potentially reduce health care costs.
Algorithm termed as tree of predictors uses machine learning meaning that computers effectively learn from additional new data over time. The System analyzes 53 data points, such as age, gender, body mass index, blood type, and blood chemistry, in order to assess the complex differences among people waiting for heart transplant. Hence, draws conclusion on the compatiblity between heart transplant recipents and donors.
Research into the new algorithm has been published in the journal PLOS One, with the paper titled "Personalized survival predictions via Trees of Predictors: An application to cardiac transplantation."
Data science is also influencing other, similar medical matters, according to Dr. Jennifer L. Hall, who is Chief of the American Heart Association’s Institute for Precision Cardiovascular Medicine. Writing in Research and Development magazine, Hall notes: "Data driven precision medicine could empower the world’s providers to treat and potentially prevent virtually any disease, taking into account a person’s health information, environment and lifestyle."
This reflects improvements in technology allowing medical researchers to cross-talk, mine big data and tackle unsolved problems relating to heart health. Perhaps of greatest importance is cloud computing, which provides "a powerful environment of analytical tools and state-of-the-art software to researchers who are analyzing large datasets for scientific insights."
Following this method, we are able to identify a significant number of patients who are good transplant candidates but were not identified as such by traditional approaches
Dr. Martin Cadeiras, a cardiologist at the David Geffen School of Medicine at UCLA
. “This methodology better resembles the human thinking process by allowing multiple alternative solutions for the same problem but taking into consideration the variability of each individual.For example, when compared against prediction models that most doctors currently use to project which transplant recipients would live for at least three years after a transplant, a commonly used benchmark, the UCLA algorithm outperformed the models by 14 percent — correctly predicting that 2,442 more heart transplant recipients of the 17,441 who received transplants and lived at least that long after the surgery.
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