Carla E. Brodley
Northeastern University
Machine learningData miningPattern recognitionComputer scienceCluster analysis
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Publications 134
Feb 26, 2020 in SIGCSE (Technical Symposium on Computer Science Education)
#1Carla E. Brodley (NU: Northeastern University)H-Index: 49
#2Megan Barry (NU: Northeastern University)
Last. Amit Shesh (NU: Northeastern University)H-Index: 7
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#1Emery D. Berger (UMass: University of Massachusetts Amherst)H-Index: 29
#2Stephen M. Blackburn (ANU: Australian National University)H-Index: 24
Last. Lexing Xie (ANU: Australian National University)H-Index: 31
view all 9 authors...
6 CitationsSource
#1Emery D. Berger (UMass: University of Massachusetts Amherst)H-Index: 29
#2Stephen M. Blackburn (ANU: Australian National University)H-Index: 24
Last. Lexing Xie (ANU: Australian National University)H-Index: 31
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 Zhao (Tufts University)H-Index: 2
#2Brian C. Healy (Harvard University)H-Index: 41
Last. Sreeram V. RamagopalanH-Index: 45
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Objective To explore the value of machine learning methods for predicting multiple sclerosis disease course. Methods 1693 CLIMB study patients were classified as increased EDSS≥1.5 (worsening) or not (non-worsening) at up to five years after baseline visit. Support vector machines (SVM) were used to build the classifier, and compared to logistic regression (LR) using demographic, clinical and MRI data obtained at years one and two to predict EDSS at five years follow-up. Results Baseline data al...
40 CitationsSource
#1Bilal AhmedH-Index: 3
#2Thomas ThesenH-Index: 3
Last. Carla E. BrodleyH-Index: 49
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2 Citations
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: 84
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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
#1Carla E. Brodley (Purdue University)H-Index: 49
#2Terran LaneH-Index: 24
Last. Timothy M. StoughH-Index: 2
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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...
#1Bilal Ahmed (Tufts University)H-Index: 3
#2Thomas Thesen (Comprehensive Epilepsy Center)H-Index: 27
Last. Carla E. Brodley (NU: Northeastern University)H-Index: 49
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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 ...
4 Citations
#1Yijun Zhao (Tufts University)H-Index: 2
#2Tanuja Chitnis (Brigham and Women's Hospital)H-Index: 51
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...