Drs. Salleb-Aouissi, Passonneau, Waltz, McCord, McGurk and Elhadad awarded a Research Initiatives in Science and Engineering (RISE) funding from the Columbia University Executive Vice President for Research
CCLS researchers, Ansaf Salleb-Aouissi, Rebecca Passonneau, David Waltz, their collaborators from Columbia University Medical School, Mary McCord, Harriet McGurk and CU Biomedical Informatics colleague Noémie Elhadad were awarded funding by Columbia University's Office of the Executive Vice President for Research, for their research on “Understanding Infantile Colic via Machine Learning”.
CCLS: Dr. Ansaf Salleb-Aouissi, Dr. Rebecca Passonneau, Dr. David Waltz, Axinia Radeva, Ashish Tomar, Hatim Diab, Boyi Xie, Faiza Khan Khattak.
CUMC: Dr. Mary McCord, and Dr. Harriet MacGurk.
CU Biomedical informatics: Dr. Noémie Elhadad.
Goal of the project
Infantile colic is defined as persistent inconsolable crying in healthy babies between 2 weeks and 4 months of age, where the baby seems to be in great discomfort and difficult to soothe. It is not a disease, but a serious prevalent condition with serious medical and social consequences, yet it remains a mystery for medical research. It is associated with Shaken Baby Syndrome, infant brain damage that results when a caregiver violently shakes a baby [Barr, 2002, 2006, 2009], [Fujiwara et al., 2009]. Estimates of the number of affected infants aged 0-4 months who cry 3 or more hours a day, 3 or more days a week for no clear cause (Wessel’s criteria [Wessel et al., 1967]), range from 5% to 40%. Recent studies suggest that excessive crying in infancy has a small but significant adverse effect on cognitive development and mental health problems later in life [Wolke et al., 2009] and can lead to mother postpartum depression [Vik et al., 2009]. Finally, colic is costly for healthcare systems, due to various ineffective medications, doctor’s office and emergency room visits, and even hospitalization. Treatment varies substantially from physician to physician. The medications doctors prescribe to treat colic or identify its causes often have side effects but don’t provide a cure. The medical literature on colic is a mix of hypotheses to explain this mysterious condition. These include lack of bacteria in the intestines, reflux, lactose intolerance, maternal smoking, and parental depression, to cite a few.
We hypothesize that colic has discoverable root causes. We propose to conduct the first large scale study to tackle this problem through Machine Learning (ML) on a very large, high-dimensional database. We assume the underlying causes are complex, possibly a combination of variables, or distinct syndromes. Machine Learning is a powerful technology for constructing complex models in very high dimensional spaces that has proven useful in a wide range of arenas. Successful use of Machine Learning requires an understanding of the application domain, and where the data is not already in a form amenable to machine learning, it also depends on assembling comprehensive and trustworthy data. A RISE award would allow our project to establish a common ground among experts in machine learning, pediatric medicine, biomedical informatics, and natural language processing.
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