英文标题：
《Evaluating Hospital Case Cost Prediction Models Using Azure Machine
  Learning Studio》
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作者：
Alexei Botchkarev
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最新提交年份：
2018
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英文摘要：
  Ability for accurate hospital case cost modelling and prediction is critical for efficient health care financial management and budgetary planning. A variety of regression machine learning algorithms are known to be effective for health care cost predictions. The purpose of this experiment was to build an Azure Machine Learning Studio tool for rapid assessment of multiple types of regression models. The tool offers environment for comparing 14 types of regression models in a unified experiment: linear regression, Bayesian linear regression, decision forest regression, boosted decision tree regression, neural network regression, Poisson regression, Gaussian processes for regression, gradient boosted machine, nonlinear least squares regression, projection pursuit regression, random forest regression, robust regression, robust regression with mm-type estimators, support vector regression. The tool presents assessment results arranged by model accuracy in a single table using five performance metrics. Evaluation of regression machine learning models for performing hospital case cost prediction demonstrated advantage of robust regression model, boosted decision tree regression and decision forest regression. The operational tool has been published to the web and openly available for experiments and extensions.
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中文摘要：
准确的医院病例成本建模和预测能力对于有效的医疗保健财务管理和预算规划至关重要。已知各种回归机器学习算法对于医疗成本预测是有效的。本实验的目的是构建Azure机器学习工作室工具，用于快速评估多种类型的回归模型。该工具为在统一实验中比较14种类型的回归模型提供了环境：线性回归、贝叶斯线性回归、决策林回归、增强决策树回归、神经网络回归、泊松回归、高斯回归过程、梯度增强机、非线性最小二乘回归、投影寻踪回归、，随机森林回归，稳健回归，mm型估计稳健回归，支持向量回归。该工具使用五个性能指标在一个表中显示按模型精度排列的评估结果。对回归机器学习模型进行医院病例成本预测的评估显示了稳健回归模型、增强决策树回归和决策林回归的优势。该操作工具已发布到web上，并可公开用于实验和扩展。
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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