Analysis | OPEN | Published: 17 June 2019 Multitask learning and benchmarking with clinical time series data
Health care is one of the most exciting frontiers in data mining and machine learning. Successful adoption of electronic health records (EHRs) created an explosion in digital clinical data available for analysis, but progress in machine learning for healthcare research has been difficult to measure because of the absence of publicly available benchmark data sets. To address this problem, we propose four clinical prediction benchmarks using data derived from the publicly available Medical Information Mart for Intensive Care (MIMIC-III) database. These tasks cover a range of clinical problems including modeling risk of mortality, forecasting length of stay, detecting physiologic decline, and phenotype classification. We propose strong linear and neural baselines for all four tasks and evaluate the effect of deep supervision, multitask training and data-specific architectural modifications on the performance of neural models.
Code (1)
Tasks
BenchmarkingBIG-bench Machine LearningComputational PhenotypingLength-of-Stay predictionMortality PredictionPhenotype classificationTime SeriesSimilar Papers 제목 키워드 기반
Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions
In this work we consider multitasking in the context of solving multiple optimization problems simultaneously by conducting a single search process. The principal goal when dealing with this scenario is to dynamically ex…
jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models
We introduce jiant, an open source toolkit for conducting multitask and transfer learning experiments on English NLU tasks. jiant enables modular and configuration-driven experimentation with state-of-the-art models and …
Transfer LearningMultitask Learning for Class-Imbalanced Discourse Classification
Small class-imbalanced datasets, common in many high-level semantic tasks like discourse analysis, present a particular challenge to current deep-learning architectures. In this work, we perform an extensive analysis on …
ClassificationGeneral ClassificationSentenceNAMER: A Node-Based Multitasking Framework for Multi-Hop Knowledge Base Question Answering
We present NAMER, an open-domain Chinese knowledge base question answering system based on a novel node-based framework that better grasps the structural mapping between questions and KB queries by aligning the nodes in …
Data AugmentationKnowledge Base Question AnsweringQuestion AnsweringCVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software
Data-driven research on the automated discovery and repair of security vulnerabilities in source code requires comprehensive datasets of real-life vulnerable code and their fixes. To assist in such research, we propose a…
severity prediction