Similarities and Differences between Machine Learning and Traditional Advanced Statistical Modeling in Healthcare Analytics

• URL: http://arxiv.org/abs/2201.02469v1
• Date: Fri, 7 Jan 2022 14:36:46 GMT
• Title: Similarities and Differences between Machine Learning and Traditional Advanced Statistical Modeling in Healthcare Analytics
• Authors: Michele Bennett, Karin Hayes, Ewa J. Kleczyk, and Rajesh Mehta
• Abstract summary: Machine learning and statistical modeling are complementary, based on similar mathematical principles. Good analysts and data scientists should be well versed in both techniques and their proper application.
• Score: 0.6999740786886537
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data scientists and statisticians are often at odds when determining the best approach, machine learning or statistical modeling, to solve an analytics challenge. However, machine learning and statistical modeling are more cousins than adversaries on different sides of an analysis battleground. Choosing between the two approaches or in some cases using both is based on the problem to be solved and outcomes required as well as the data available for use and circumstances of the analysis. Machine learning and statistical modeling are complementary, based on similar mathematical principles, but simply using different tools in an overall analytics knowledge base. Determining the predominant approach should be based on the problem to be solved as well as empirical evidence, such as size and completeness of the data, number of variables, assumptions or lack thereof, and expected outcomes such as predictions or causality. Good analysts and data scientists should be well versed in both techniques and their proper application, thereby using the right tool for the right project to achieve the desired results.

Related papers

• Using causal inference to avoid fallouts in data-driven parametric analysis: a case study in the architecture, engineering, and construction industry [0.7566148383213173]
  The decision-making process in real-world implementations has been affected by a growing reliance on data-driven models. We investigated the synergetic pattern between the data-driven methods, empirical domain knowledge, and first-principles simulations.
  arXiv  Detail & Related papers  (2023-09-11T13:54:58Z)
• Why we should respect analysis results as data [0.0]
  It is commonly overlooked that analyzing clinical study data also produces data in the form of results. Although integrating and putting findings into context is a cornerstone of scientific work, analysis results are often neglected as a data source. We propose a solution to "calculate once, use many times" by combining analysis results standards with a common data model.
  arXiv  Detail & Related papers  (2022-04-21T08:34:07Z)
• Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets [53.34152466646884]
  In this paper, we show how bringing recent results on equivariant representation learning instantiated on structured spaces together with simple use of classical results on causal inference provides an effective practical solution. We demonstrate how our model allows dealing with more than one nuisance variable under some assumptions and can enable analysis of pooled scientific datasets in scenarios that would otherwise entail removing a large portion of the samples.
  arXiv  Detail & Related papers  (2022-03-29T04:54:06Z)
• Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis [5.064097093575691]
  We propose a new hypothesis testing approach based on the conditional randomization test. Our methodology is solely based on the randomization of factors, and hence is free from assumptions.
  arXiv  Detail & Related papers  (2022-01-20T18:23:12Z)
• Two ways towards combining Sequential Neural Network and Statistical Methods to Improve the Prediction of Time Series [0.34265828682659694]
  We propose two different directions to integrate the two, a decomposition-based method and a method exploiting the statistic extraction of data features. We evaluate the proposal using time series data with varying degrees of stability. Performance results show that both methods can outperform existing schemes that use models and learning separately.
  arXiv  Detail & Related papers  (2021-09-30T20:34:58Z)
• Model-agnostic multi-objective approach for the evolutionary discovery of mathematical models [55.41644538483948]
  In modern data science, it is more interesting to understand the properties of the model, which parts could be replaced to obtain better results. We use multi-objective evolutionary optimization for composite data-driven model learning to obtain the algorithm's desired properties.
  arXiv  Detail & Related papers  (2021-07-07T11:17:09Z)
• Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms [91.3755431537592]
  We analyze four broad meta-learning strategies which rely on plug-in estimation and pseudo-outcome regression. We highlight how this theoretical reasoning can be used to guide principled algorithm design and translate our analyses into practice.
  arXiv  Detail & Related papers  (2021-01-26T17:11:40Z)
• Double Robust Representation Learning for Counterfactual Prediction [68.78210173955001]
  We propose a novel scalable method to learn double-robust representations for counterfactual predictions. We make robust and efficient counterfactual predictions for both individual and average treatment effects. The algorithm shows competitive performance with the state-of-the-art on real world and synthetic data.
  arXiv  Detail & Related papers  (2020-10-15T16:39:26Z)
• Marginal likelihood computation for model selection and hypothesis testing: an extensive review [66.37504201165159]
  This article provides a comprehensive study of the state-of-the-art of the topic. We highlight limitations, benefits, connections and differences among the different techniques. Problems and possible solutions with the use of improper priors are also described.
  arXiv  Detail & Related papers  (2020-05-17T18:31:58Z)
• Machine learning for causal inference: on the use of cross-fit estimators [77.34726150561087]
  Doubly-robust cross-fit estimators have been proposed to yield better statistical properties. We conducted a simulation study to assess the performance of several estimators for the average causal effect (ACE) When used with machine learning, the doubly-robust cross-fit estimators substantially outperformed all of the other estimators in terms of bias, variance, and confidence interval coverage.
  arXiv  Detail & Related papers  (2020-04-21T23:09:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.