Physicists' to use their unique tool to improve neonatal health
- Date:
- January 3, 2017
- Source:
- University of Wisconsin-Milwaukee
- Summary:
- In neonatal health, knowing the exact time of conception saves lives. Two data scientists have a mathematical solution to rectify rough estimates.
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Knowing exactly when an expectant mother conceived helps physicians prepare for life-threatening complications that come with early- and late-term childbirth.
But the parents' estimates can be off by weeks.
Physicists at the University of Wisconsin-Milwaukee are using an algorithm to estimate the time of conception with much greater accuracy. In some circumstances, the algorithm can reduce timing uncertainty by a factor of 300. If that holds true in this application, it could shrink the estimate to a range of days or hours. The project is funded by the Bill & Melinda Gates Foundation.
This mathematical approach was devised by Abbas Ourmazd and Russell Fung and published in 2016 in the journal Nature.
The computer algorithm works by extracting a weak "arrow of time" from noisy data with highly inaccurate time stamps. Think of it as restoring the initial sequence of a deck of cards after it has been heavily shuffled.
The algorithm was originally devised to make movies of ultrafast chemical bond-breaking in molecules, said Ourmazd, but could have many applications.
The data for application to neonatal health will come from the foundation's collection of global statistics on the health of mothers and babies during, and for many years after, pregnancy.
"It is a privilege to use the algorithm we originally developed for fundamental science to help improve people's health everywhere, particularly in developing nations," said Ourmazd, a UWM distinguished professor of physics.
Story Source:
Materials provided by University of Wisconsin-Milwaukee. Note: Content may be edited for style and length.
Journal Reference:
- R. Fung, A. Ourmazd, A. M. Hanna, O. Vendrell, S. Ramakrishna, T. Seideman, R. Santra, A. Ourmazd. Dynamics from noisy data with extreme timing uncertainty. Nature, 2016; 532 (7600): 471 DOI: 10.1038/nature17627
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