Clinical big data and deep learning: Applications, challenges, and future outlooks

Abstract

The explosion of digital healthcare data has led to a surge of data-driven medical research based on machine learning. In recent years, as a powerful technique for big data, deep learning has gained a central position in machine learning circles for its great advantages in feature representation and pattern recognition. This article presents a comprehensive overview of studies that employ deep learning methods to deal with clinical data. Firstly, based on the analysis of the characteristics of clinical data, various types of clinical data (e.g., medical images, clinical notes, lab results, vital signs, and demographic informatics) are discussed and details provided of some public clinical datasets. Secondly, a brief review of common deep learning models and their characteristics is conducted. Then, considering the wide range of clinical research and the diversity of data types, several deep learning applications for clinical data are illustrated: auxiliary diagnosis, prognosis, early warning, and other tasks. Although there are challenges involved in applying deep learning techniques to clinical data, it is still worthwhile to look forward to a promising future for deep learning applications in clinical big data in the direction of precision medicine.

Forecasting Insurance and Patient Charges using Linear Regression

“…Nowadays, with the improving technology and resources available, the development of Machine Learning algorithms, Artificial Intelligence, Deep Learning with Neural networks, almost all of the industries have started using these novel techniques in their processes. The insurance companies are no different, and in fact these are one of the main industries where further advancements using machine learning and data analysis [2] techniques could be made.…”

“…But with the advent of Internet of Things in the technological world, there was large amount of data that got collected but there was no way available to analyse them, and on time. And then there was the rise of Data Analysis [2] , R programming and SAS [3] (Statistical Analysis Software). Constant improvements made to them allowed for the analysis of Big Data within seconds, with better efficiency and accuracy.…”

“…Python has a lot of packages like pandas, NumPy, scikit, plotly, SciPy, statsmodels, etc. These packages are specifically present for data analysis [2] , regression analysis, etc. Python also has the added advantage of ease of use.…”

“…The goodness of fit [7] can be determined by the number of outliers and their impact on the eventual result obtained from the dataset. Further implementation focuses more on actual data analysis [2] rather than judging the dataset. Regression plots can be used to understand the behaviour of the dataset.…”

Modeling Method for Leveraging Data Quality in Healthcare Big Data

“…The work carried out by Yu et al [15] have discussed about the significance of the learning mechanism applied over medical big data. According to the study, it says that irrespective of challenges associated with the deep learning mechanism, it is still the best solution.…”