I2b2 2008

•Als ODP, PDF herunterladen•

2 gefällt mir•352 views

University of Minnesota, Duluth

Technologie Bildung

Learning High Precision Rules to Make Predictions of Morbidities in Discharge Summaries ,[object Object],[object Object],[object Object],[object Object]

i2b2 Obesity Challenge (2008) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Training Examples ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Early Working Title “ Is it Possible to Perform Accurate Supervised Learning of Highly Skewed Data with Minority Classes That Have Distributional Characteristics Significantly Less Prevalent Than Commonly Observed Values of Standard Deviations in Cross Validation Studies Done Across a Wide Range of Machine Learning Datasets?”

Supervised Learning is Noisy ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

What Now? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

...Remove Them... ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Focus on High Precision ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Cross Validation Studies ,[object Object],[object Object],[object Object]

Features ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Machine Learning Methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Rule Learners Rule ,[object Object],[object Object],[object Object],[object Object],[object Object]

Why Rule Learners Ruled? ,[object Object],[object Object],[object Object]

Best CV Results Entries to Challenge ,[object Object],[object Object],[object Object]

Intuitive Task Performance ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Textual Task Performance ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Post Challenge ,[object Object],[object Object],[object Object],[object Object]

JRip Rules for Asthma (intuitive) ,[object Object],[object Object],[object Object],[object Object]

JRip rules for Asthma (textual) ,[object Object],[object Object]

JRip rules for Depression (intuitive) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],(

JRip rules for Depression (textual) ,[object Object],[object Object]

JRip Rules for Gallstones (intuitive) ,[object Object],[object Object],[object Object],[object Object],[object Object]

JRip Rules for Gallstones (textual) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

JRip Rules for Obesity (intuitive) ,[object Object],[object Object],[object Object],[object Object]

JRip Rules for Obesity (textual) ,[object Object],[object Object],[object Object]

Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object]

Software Used ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Weitere ähnliche Inhalte

Andere mochten auch

Acm ihi-2010-pedersen-final

University of Minnesota, Duluth

Eurolan 2005 Pedersen

University of Minnesota, Duluth

What it's like to do a Master's thesis with me (Ted Pedersen)

University of Minnesota, Duluth

Feb20 mayo-webinar-21feb2012

University of Minnesota, Duluth

Eacl 2006 Pedersen

University of Minnesota, Duluth

Pedersen masters-thesis-oct-10-2014

University of Minnesota, Duluth

Screening Twitter Users for Depression and PTSD

University of Minnesota, Duluth

Andere mochten auch (7)

Acm ihi-2010-pedersen-final

Eurolan 2005 Pedersen

What it's like to do a Master's thesis with me (Ted Pedersen)

Feb20 mayo-webinar-21feb2012

Eacl 2006 Pedersen

Pedersen masters-thesis-oct-10-2014

Screening Twitter Users for Depression and PTSD

Ähnlich wie I2b2 2008

Rohan's Masters presentation

rohan_anil

Learning to Search Henry Kautz

butest

Learning to Search Henry Kautz

butest

Lecture 9 slides: Machine learning for Protein Structure ...

butest

Nighthawk: A Two-Level Genetic-Random Unit Test Data Generator

CS, NcState

forest-cover-type

Kayleigh Beard

Probability density estimation using Product of Conditional Experts

Chirag Gupta

Machine learning for_finance

Stefan Duprey

Evolutionary Optimization Algorithms & Large-Scale Machine Learning

University of Maribor

Machine Learning, Data Mining, Genetic Algorithms, Neural ...

butest

A dataset is a shred of crucial evidence to describe a task. However, each data point in the dataset does not have the same potential, as some of the data points can be more representative or informative than others. This unequal importance among the data points may have a large impact in rehearsal-based continual learning, where we store a subset of the training examples (coreset) to be replayed later to alleviate catastrophic forgetting. In continual learning, the quality of the samples stored in the coreset directly affects the model's effectiveness and efficiency. The coreset selection problem becomes even more important under realistic settings, such as imbalanced continual learning or noisy data scenarios. To tackle this problem, we propose Online Coreset Selection (OCS), a simple yet effective method that selects the most representative and informative coreset at each iteration and trains them in an online manner. Our proposed method maximizes the model's adaptation to a target dataset while selecting high-affinity samples to past tasks, which directly inhibits catastrophic forgetting. We validate the effectiveness of our coreset selection mechanism over various standard, imbalanced, and noisy datasets against strong continual learning baselines, demonstrating that it improves task adaptation and prevents catastrophic forgetting in a sample-efficient manner.

Online Coreset Selection for Rehearsal-based Continual Learning

MLAI2

Gradient Boosted Regression Trees in Scikit Learn by Gilles Louppe & Peter Pr...

PyData

Download It

butest

About the webinar Flexible Clinical Trial Design | Survival, Stepped-Wedge & MAMS Designs As clinical trials increase in complexity, the requirement is for trial designs to adapt to these complications. From dealing with non-proportional hazards in survival analysis to creating seamless Phase II/III clinical trials, it is an exciting time to be involved in clinical trial design and analysis. In this free webinar, we will explore a select few topics that highlight the additional flexibility available when designing modern clinical trials. In this free webinar you will learn about: Flexible Survival Analysis Designs Non-proportional hazards and other complex survival curves have become of increasing interest, due to being commonly seen in immunotherapy development. This has led to interest in assessing the robustness of standard methods and alternative methods that better adapt to deviations. In this webinar, we will look at power analysis assuming complex survival curves and the weighted log-rank test as one candidate model to deal with a delayed survival effect. Stepped-Wedge designs Cluster-randomized designs are often adopted when there is a high risk of contamination if cluster members were randomized individually. Stepped-wedge designs are useful in cases where it is difficult to apply a particular treatment to half of the clusters at the same time. In this webinar, we will introduce stepped-wedge designs and provide an insight into the more complex, flexible randomization schedules available. Multi-Arm Multi-Stage (MAMS) MAMs designs provide the ability to assess more treatments in less time than could be done with a series of two-arm trials and can offer smaller sample size requirements when compared to that required for the equivalent number of two-arm trials. In this webinar, we will look at the design of a Group Sequential MAMS design and explore its design requirements. Duration - 60 minutes Speaker: Ronan Fitzpatrick, Head of Statistics, Statsols For more webinars check out https://www.statsols.com/webinars

Flexible Clinical Trial Design - Survival, Stepped-Wedge & MAMS Designs

nQuery

L1 intro2 supervised_learning

Yogendra Singh

Optimization of water distribution systems design parameters using genetic al...

Tanay Kulkarni

Presented at 15th International Conference on BioInformatics and BioEngineering (BIBE2014) Prognostic modeling is central to medicine, as it is often used to predict patients’ outcome and response to treatments and to identify important medical risk factors. Logistic regression is one of the most used approaches for clinical prediction modeling. Traumatic brain injury (TBI) is an important public health issue and a leading cause of death and disability worldwide. In this study, we adapt CPXR (Contrast Pattern Aided Regression, a recently introduced regression method), to develop a new logistic regression method called CPXR(Log), for general binary outcome prediction (including prognostic modeling), and we use the method to carry out prognostic modeling for TBI using admission time data. The models produced by CPXR(Log) achieved AUC as high as 0.93 and specificity as high as 0.97, much better than those reported by previous studies. Our method produced interpretable prediction models for diverse patient groups for TBI, which show that different kinds of patients should be evaluated differently for TBI outcome prediction and the odds ratios of some predictor variables differ significantly from those given by previous studies; such results can be valuable to physicians.

A new CPXR Based Logistic Regression Method and Clinical Prognostic Modeling ...

Vahid Taslimitehrani

Paper presentation on 'Understanding Balck-box Predictions via Influence Func...

Zabir Al Nazi Nabil

Understanding Black-box Predictions via Influence Functions

Zabir Al Nazi Nabil

Introduction

butest

Ähnlich wie I2b2 2008 (20)

Rohan's Masters presentation

Learning to Search Henry Kautz

Lecture 9 slides: Machine learning for Protein Structure ...

Nighthawk: A Two-Level Genetic-Random Unit Test Data Generator

forest-cover-type

Probability density estimation using Product of Conditional Experts

Machine learning for_finance

Evolutionary Optimization Algorithms & Large-Scale Machine Learning

Machine Learning, Data Mining, Genetic Algorithms, Neural ...

Online Coreset Selection for Rehearsal-based Continual Learning

Gradient Boosted Regression Trees in Scikit Learn by Gilles Louppe & Peter Pr...

Download It

Flexible Clinical Trial Design - Survival, Stepped-Wedge & MAMS Designs

L1 intro2 supervised_learning

Optimization of water distribution systems design parameters using genetic al...

A new CPXR Based Logistic Regression Method and Clinical Prognostic Modeling ...

Paper presentation on 'Understanding Balck-box Predictions via Influence Func...

Understanding Black-box Predictions via Influence Functions

Introduction

Mehr von University of Minnesota, Duluth

Muslims in Machine Learning workshop (NeurlPS 2021) - Automatically Identifyi...

University of Minnesota, Duluth

Automatically Identifying Islamophobia in Social Media

University of Minnesota, Duluth

Hate speech is language intended to cause harm against a particular individual or group, often based on their racial, ethnic, religious, or gender identity. Hate speech is widespread on social media, and is increasingly common in mainstream political discourse. That said, there is no clear consensus as to what constitutes hate speech. In addition, human moderators come with their own biases, and automatic computer algorithms are often easy to fool. All of these factors complicate the efforts of social media platforms to filter or reduce such content. During this interactive workshop we will discuss examples from Twitter in the hopes of reaching some consensus as to what is and is not hate speech. We will also try to determine what kind of knowledge a human moderator or an automatic algorithm would need to have in order to make this determination. We will try to avoid particularly graphic examples of hate speech and focus on more subtle cases.

What Makes Hate Speech : an interactive workshop

University of Minnesota, Duluth

Algorithmic Bias - What is it? Why should we care? What can we do about it?

University of Minnesota, Duluth

Algorithmic Bias : What is it? Why should we care? What can we do about it?

University of Minnesota, Duluth

Duluth at Semeval 2017 Task 6 - Language Models in Humor Detection

University of Minnesota, Duluth

Who's to say what's funny? A computer using Language Models and Deep Learning...

University of Minnesota, Duluth

Duluth at Semeval 2017 Task 7 - Puns upon a Midnight Dreary, Lexical Semantic...

University of Minnesota, Duluth

Puns upon a midnight dreary, lexical semantics for the weak and weary

University of Minnesota, Duluth

The horizon isn't found in a dictionary : Identifying emerging word senses a...

University of Minnesota, Duluth

Duluth : Word Sense Discrimination in the Service of Lexicography

University of Minnesota, Duluth

These are the slides used in my tutorial at MICAI 2013 (presented November 25, 2013). The tutorial is on Measuring the Similarity and Relatedness of Concepts, and focuses on methods that rely on information from ontologies, possibly augmented with corpus statistics. This tutorial includes background on the measures, plus information on software that implements different measures, plus reference standard data sets that can be used to evaluate measures.

MICAI 2013 Tutorial Slides - Measuring the Similarity and Relatedness of Conc...

University of Minnesota, Duluth

Pedersen naacl-2013-demo-poster-may25

University of Minnesota, Duluth

Pedersen semeval-2013-poster-may24

University of Minnesota, Duluth

Talk at UAB, April 12, 2013

University of Minnesota, Duluth

Ihi2012 semantic-similarity-tutorial-part1

University of Minnesota, Duluth

Pedersen ACL Disco-2011 workshop

University of Minnesota, Duluth

Pedersen acl2011-business-meeting

University of Minnesota, Duluth

Pedersen naacl-2010-poster

University of Minnesota, Duluth

Aaai 2006 Pedersen

University of Minnesota, Duluth

Mehr von University of Minnesota, Duluth (20)