# An Overview of Healthcare Data Analytics With Applications to the COVID-19 Pandemic

arXiv: https://arxiv.org/abs/2111.14623 · HTML: https://mlsanity.com/paper/2111.14623/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 173 references that are in the map.
- First posted on arXiv 25 Nov 2021.

## Builds on (most cited first, 24 of 24)

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- [COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images](https://mlsanity.com/paper/2003.09871/) (2020)
- [Medical Image Analysis using Convolutional Neural Networks: A Review](https://mlsanity.com/paper/1709.02250/) (2017)
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- [High-Dimensional Inference: Confidence Intervals, p-Values and R-Software hdi](https://mlsanity.com/paper/1408.4026/) (2014)
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- [A Time-dependent SIR model for COVID-19 with Undetectable Infected Persons](https://mlsanity.com/paper/2003.00122/) (2020)
- [Applications of artificial intelligence in drug development using real-world data](https://mlsanity.com/paper/2101.08904/) (2021)
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- [Orthogonal Subsampling for Big Data Linear Regression](https://mlsanity.com/paper/2105.14647/) (2021)
- [The Effects of Evolutionary Adaptations on Spreading Processes in Complex Networks](https://mlsanity.com/paper/1810.04545/) (2018)
- [Coronavirus Covid-19 spreading in Italy: optimizing an epidemiological model with dynamic social distancing through Differential Evolution](https://mlsanity.com/paper/2004.00553/) (2020)
- [Positively Correlated Samples Save Pooled Testing Costs](https://mlsanity.com/paper/2011.09794/) (2020)

## Abstract (arXiv)

In the era of big data, standard analysis tools may be inadequate for making inference and there is a growing need for more efficient and innovative ways to collect, process, analyze and interpret the massive and complex data. We provide an overview of challenges in big data problems and describe how innovative analytical methods, machine learning tools and metaheuristics can tackle general healthcare problems with a focus on the current pandemic. In particular, we give applications of modern digital technology, statistical methods, data platforms and data integration systems to improve diagnosis and treatment of diseases in clinical research and novel epidemiologic tools to tackle infection source problems, such as finding Patient Zero in the spread of epidemics. We make the case that analyzing and interpreting big data is a very challenging task that requires a multi-disciplinary effort to continuously create more effective methodologies and powerful tools to transfer data information into knowledge that enables informed decision making.