Boston MLHC (ML in HealthCare) 2017
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Accepted Papers
- Piecewise-constant parametric approximations for survival learning Jeremy Weiss*, Carnegie Mellon University
- Spatially-Continuous Plantar Pressure Reconstruction Using Compressive SensingAmirreza Farnoosh, Northeastern University; Mehrdad Nourani, University of Texas at Dallas; Sarah Ostadabbas*, Northeastern University
- Classifying Lung Cancer Severity with Ensemble Machine Learning in Health Care Claims DataSavannah Bergquist*, Harvard University; Gabriel Brooks, Dartmouth-Hitchcock Medical Center; Nancy Keating, Harvard Medical School, Brigham and Women's Hospital; Mary Beth Landrum, Harvard Medical School; Sherri Rose, Harvard Medical School
- Predicting long-term mortality with first week post-operative data after Coronary Artery Bypass Grafting using Machine Learning modelsJosŽ Forte*, University of Groningen; Marco Wiering, University of Groningen; Hjalmar Bouma, University Medical Center Groningen; Fred de Geus, University Medical Center Groningen; Anne Epema, University Medical Center Groningen
- ShortFuse: Biomedical Time Series Representations in the Presence of Structured InformationMadalina Fiterau*, Stanford University; Suvrat Bhooshan, Stanford University; Jason Fries, Stanford University; Charles Bournhonesque, Stanford University; Jennifer Hicks, Stanford University; Eni Halilaj, Stanford University; Christopher Re, Stanford University; Scott Delp, Stanford University
- Towards Vision-based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene ComplianceAlbert Haque*, Stanford University; Michelle Guo, Stanford University; Alexandre Alahi, Stanford University; Amit Singh, Lucile Packard Children's Hospital; Serena Yeung, Stanford University; N. Lance Downing, Stanford; Terry Platchek, Lucile Packard Children's Hospital; Li Fei-Fei, Stanford University
- Surgeon Technical Skill Assessment using Computer Vision based AnalysisHei Law*, University of Michigan; Jia Deng, University of Michigan, Ann Arbor; Khurshid Ghani, University of Michigan
- Predicting Surgery Duration with Neural Heteroscedastic RegressionZachary Lipton*, UCSD; Nathan Ng, UCSD; Rodney Gabriel , UCSD; Charles Elkan, UCSD; Julian McAuley, UC San Diego
- Temporal prediction of multiple sclerosis evolution from patient-centered outcomesESamuele Fiorini, University of Genoa; Andrea Tacchino, Italian Multiple Sclerosis Foundation - Scientific Research Area; Giampaolo Brichetto, Italian Multiple Sclerosis Foundation - Scientific Research Area; Alessandro Verri, University of Genova, Italy; Annalisa Barla*, Universitˆ degli Studi di Genova
- Clustering Patients with Tensor DecompositionMatteo Ruffini*, UPC; Ricard Gavaldˆ, UPC; Esther Lim—n, Institut Catalˆ de la Salut
- Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning ApproachAniruddh Raghu*, MIT; Marzyeh Ghassemi, MIT; Matthieu Komorowski, Imperial College London; Leo Celi, MIT; Pete Szolovits, MIT
- Modeling Progression Free Survival in Breast Cancer with Tensorized Recurrent Neural Networks and Accelerated Failure Time ModelYinchong Yang*, Siemens AG, LMU MŸnchen; Volker Tresp, Siemens AG and Ludwig Maximilian University of Munich ; Peter Fasching, Department of Gynecology and Obstetrics, University Hospital Erlangen
- Hawkes Process Modeling of Adverse Drug Reactions with Longitudinal Observational DataYujia Bao*, University of Wisconsin-Madison; Zhaobin Kuang, University of Wisconsin, Madison; Peggy Peissig, Marshfield Clinic Research Foundation; David Page, University of Wisconsin, Madison; Rebecca Willett, University of Wisconsin, Madison
- Patient Similarity Using Population Statistics and Multiple Kernel LearningBryan Conroy*, Philips Research North America; Minnan Xu-Wilson, Philips Research North America; Asif Rahman, Philips Reserach
- A Video-Based Method for Automatically Rating AtaxiaRonnachai Jaroensri*, MIT CSAIL; Amy Zhao, MIT; Fredo Durand, MIT; John Guttag, MIT; Jeremy Schmahmann, Massachusetts General Hospital; Guha Balakrishnan, MIT; Derek Lo, Yale University
- Visualizing Clinical Significance with Prediction and Tolerance RegionsMaria Jahja*, North Carolina State University; Daniel Lizotte, UWO
- Predictive Hierarchical Clustering: Learning clusters of CPT codes for improving surgical outcomesElizabeth C. Lorenzi, Stephanie L. Brown, Zhifei Sun, and Katherine Heller
- An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis DetectionJoseph Futoma, Sanjay Hariharan, Katherine Heller, Mark Sendak, Nathan Brajer, Meredith Clement, Armando Bedoya, and Cara O'BrienMarked Point Process for Severity of Illness AssessmentKazi Islam*, UC Riverside; Christian Shelton, UC Riverside
- Diagnostic Inferencing via Improving Clinical Concept Extraction with Deep Reinforcement Learning: A Preliminary StudyYuan Ling, Philips Research North America; Sadid A. Hasan*, Philips Research North America; Vivek Datla, Philips Research North America; Ashequl Qadir, Philips Research North America; Kathy Lee, Philips Research North America; Joey Liu, Philips Research North America; Oladimeji Farri, Philips Research North America
- Generating Multi-label Discrete Patient Records using Generative Adversarial NetworksEdward Choi*, Georgia Institute of Technology; Siddharth Biswal, Georgia Institute of Technology; Bradley Malin, Vanderbilt University; Jon Duke, Georgia Institute of Technology; Walter Stewart, Sutter Health; Jimeng Sun, CS
- Quantifying Mental Health from Social Media using Learned User EmbeddingsSilvio Moreira*, INESC-ID; Glen Copperfield, qntfy.io; Paula Carvalho, INESC-ID; M‡rio Silva, INESC-ID; Byron Wallace, Northeastern
- Clinical Intervention Prediction and Understanding using Deep NetworksNathan Hunt*, MIT; Marzyeh Ghassemi, MIT; Harini Suresh, MIT; Pete Szolovits, MIT; Leo Celi, MIT; Alistair Johnson, MIT
- Understanding Coagulopathy using Multi-view Data in the Presence of Sub-Cohorts: A Hierarchical Subspace ApproachArya Pourzanjani*, UCSB; Tie Bo Wu, UCSB; Richard M. Jiang, UCSB; Mitchell J. Cohen, Denver Health Medical Center; Linda R. Petzold, UCSB
- Towards a directory of rare disease specialists: Identifying experts from publication historyZihan Wang*, University of Toronto; Michael Brudno, U Turonto; Orion Buske, Centre for Computational Medicine, SickKids Hospital
- Reproducibility in critical care: a mortality prediction case studyAlistair Johnson*, MIT; Tom Pollard, MIT; Roger Mark, MIT
Accepted Clinical Abstracts
- Extracting Information from Electronic Health Records Using Natural Language Processing – Knowledge Discovery from Unstructured InformationVasua Chandrasekaran, Jinghua He, Monica Reed Chase, Aman Bhandari, Christopher Frederick, and Paul Dexter
- Using Machine Learning to Recommend Oncology Clinical TrialsAnasuya Das, Leifur Thorbergsson, Aleksandr Grigorenko, David Sontag, Iker Huerga
- Accounting for diagnostic uncertainty when training a Machine Learning algorithm to detect patients with the Acute Respiratory Distress SyndromeNarathip Reamaroon, Michael W. Sjoding, Kayvan Najarian
- Visual Supervision of Unsupervised Clustering of Patients with ClustervisionAdam Perer*, IBM Research; Bum Chul Kwon, IBM Research; Janu Verma, IBM Research; Kenney Ng, IBM Research; Ben Eysenbach, MIT; Christopher deFilippi, INOVA; Walter Stewart, Sutter Health
- MS Mosaic: First Steps (and Stumbles) Toward a Patient-Centered Mobile Platform for Multiple Sclerosis Research and CareLee Hartsell
- Light Field Otoscope 3D Imaging of Diseased Ears in an Alaska Native PopulationManuel Martinello, Harshavardhan Binnamangalam, Philip Hofstetter, John Kokesh, Samantha Kleindienst, Tiffany Romain, Noah Bedard, and Ivana Tosic