Carla E. Brodley
Northeastern University
Publications 142
#1Emery D. Berger (UMass: University of Massachusetts Amherst)H-Index: 29
#2Stephen M. Blackburn (ANU: Australian National University)H-Index: 25
Last.Lexing Xie (ANU: Australian National University)H-Index: 29
view all 9 authors...
2 CitationsSource
#1Emery D. Berger (UMass: University of Massachusetts Amherst)H-Index: 29
#2Stephen M. Blackburn (ANU: Australian National University)H-Index: 25
Last.Lexing Xie (ANU: Australian National University)H-Index: 29
view all 8 authors...
Rankings are a fact of life. Whether or not one likes them, they exist and are influential. Within academia, and in computer science in particular, rankings not only capture our attention but also widely influence people who have a limited understanding of computing science research, including prospective students, university administrators, and policy-makers. In short, rankings matter. This position paper advocates for the adoption of "GOTO rankings": rankings that use Good data, are Open, Tran...
Dec 1, 2017 in Big Data (International Conference on Big Data)
#1Carla E. Brodley (NU: Northeastern University)H-Index: 49
Machine learning research in academia is often conducted in vitro, divorced from motivating practical applications. As a result researchers often lose the ability to ask the question: how can my human expert's knowledge be used to best improve the machine learning outcome? In this talk, we present three motivating applications that all benefit from human-guided machine learning: systematic reviews for evidence-based medicine, generating maps of global land cover of the Earth from remotely sensed...
#1Yijun ZhaoH-Index: 2
#2Brian C. HealyH-Index: 40
Last.Sreeram V. RamagopalanH-Index: 44
view all 9 authors...
28 CitationsSource
Aug 13, 2016 in KDD (Knowledge Discovery and Data Mining)
#1Yijun Zhao (Tufts University)H-Index: 2
#2Bilal Ahmed (Tufts University)H-Index: 3
Last.Orrin Devinsky (NYU: New York University)H-Index: 82
view all 8 authors...
Visual detection of lesional areas on a cortical surface is critical in rendering a successful surgical operation for Treatment Resistant Epilepsy (TRE) patients. Unfortunately, 45% of Focal Cortical Dysplasia (FCD, the most common kind of TRE) patients have no visual abnormalities in their brains' 3D-MRI images. We collaborate with doctors from NYU Langone's Comprehensive Epilepsy Center and apply machine learning methodologies to identify the resective zones for these {MRI-negative} FCD patien...
2 CitationsSource
#1Bilal Ahmed (Tufts University)H-Index: 3
#2Thomas Thesen (Comprehensive Epilepsy Center)H-Index: 26
Last.Carla E. Brodley (NU: Northeastern University)H-Index: 49
view all 6 authors...
Focal cortical dysplasia (FCD) is the most common cause of pediatric epilepsy and the third most common cause in adults with treatment-resistant epilepsy. Surgical resection of the lesion is the most effective treatment to stop seizures. Technical advances in MRI have revolutionized the diagnosis of FCD, leading to high success rates for resective surgery. However, 45% of histologically confirmed FCD patients have normal MRIs (MRI-negative). Without a visible lesion, the success rate of surgery ...
3 Citations
#1Bilal AhmedH-Index: 3
#2Thomas ThesenH-Index: 3
Last.Carla E. BrodleyH-Index: 49
view all 7 authors...
2 Citations
#1Carla E. Brodley (Purdue University)H-Index: 49
#2Terran LaneH-Index: 23
Last.Timothy M. StoughH-Index: 2
view all 3 authors...
One of the most important parts of a scientist' work is he discovery of patterns in data. Yet the databases of modern science are frequently so im mense that they preclude direct human analysis. Inevitably, as their methods for gathering data have become auto mated, scientists have begun to search for ways to automate its analysis as well. Over the past five years, investi gators in a new field called knowledge discovery and data mining have had notable successes in training comput ers to do wha...
#1Yijun Zhao (Tufts University)H-Index: 2
#2Tanuja Chitnis (Brigham and Women's Hospital)H-Index: 48
Last.Carla E. Brodley (NU: Northeastern University)H-Index: 49
view all 5 authors...
Predicting disease course is critical in chronic progressive diseases such as multiple sclerosis (MS) for determining treatment. Forming an accurate predictive model based on clinical data is particularly challenging when data is gathered from multiple clinics/physicians as the labels vary with physicians' subjective judgment about clinical tests and further we have no a priori knowledge of the various types of physician subjectivity. At the same time, we often have some (limited) domain knowled...
#1Jingjing Liu (Tufts University)H-Index: 3
#2Carla E. Brodley (NU: Northeastern University)H-Index: 49
Last.Tanuja Chitnis (Brigham and Women's Hospital)H-Index: 48
view all 4 authors...
ObjectivesConfounding factors in unsupervised data can lead to undesirable clustering results. For example in medical datasets, age is often a confounding factor in tests designed to judge the severity of a patient's disease through measures of mobility, eyesight and hearing. In such cases, removing age from each instance will not remove its effect from the data as other features will be correlated with age. Motivated by the need to find homogeneous groups of multiple sclerosis (MS) patients, we...
3 CitationsSource