The large-scale parallel computation that became available on the new generation of graphics processing units (GPUs) and on cloud-based services can be exploited for use in healthcare data analysis. Furthermore, computation workstations suited for deep learning are usually equipped with multiple GPUs allowing for workload distribution among multiple GPUs for larger datasets while exploiting parallelism in each GPU. In this paper, we utilize distributed and parallel computation techniques to efficiently analyze healthcare data using deep learning techniques. We demonstrate the scalability and computational benefits of this approach with a case study of longitudinal assessment of approximately 150 000 type 2 diabetic patients. Type 2 diabetes mellitus (T2DM) is the fourth case of mortality worldwide with rising prevalence. T2DM leads to adverse events such as acute myocardial infarction, major amputations, and avoidable hospitalizations. This paper aims to establish a relation between laboratory and medical assessment variables with the occurrence of the aforementioned adverse events and its prediction using machine learning techniques. We use a raw database provided by Basque Health Service, Spain, to conduct this study. This database contains 150 156 patients diagnosed with T2DM, from whom 321 laboratory and medical assessment variables recorded over four years are available. Predictions of adverse events on T2DM patients using both classical machine learning and deep learning techniques were performed and evaluated using accuracy, precision, recall and F1-score as metrics. The best performance for the prediction of acute myocardial infarction is obtained by linear discriminant analysis (LDA) and support vector machines (SVM) both balanced and weight models with an accuracy of 97%; hospital admission for avoidable causes best performance is obtained by LDA balanced and SVMs balanced both with an accuracy of 92%. For the prediction of the incidence of at least one adverse event, the model with the best performance is the recurrent neural network trained with a balanced dataset with an accuracy of 94.6%. The ability to perform and compare these experiments was possible through the use of a workstation with multi-GPUs. This setup allows for scalability to larger datasets. Such models are also cloud ready and can be deployed on similar architectures hosted on AWS for even larger datasets.

Scalable Healthcare Assessment for Diabetic Patients Using Deep Learning on Multiple GPUs

Artificial intelligence is already ubiquitous, and is increasingly being used to autonomously make ever more consequential decisions. However, there has been relatively little research into the consequences for equity of the use of narrow AI and automated decision systems in medicine and public health. A narrative review using a hermeneutic approach was undertaken to explore current and future uses of AI in medicine and public health, issues that have emerged, and longer-term implications for population health. Accounts in the literature reveal a tremendous expectation on AI to transform medical and public health practices, especially regarding precision medicine and precision public health. Automated decisions being made about disease detection, diagnosis, treatment, and health funding allocation have significant consequences for individual and population health and wellbeing. Meanwhile, it is evident that issues of bias, incontestability, and erosion of privacy have emerged in sensitive domains where narrow AI and automated decision systems are in common use. As the use of automated decision systems expands, it is probable that these same issues will manifest widely in medicine and public health applications. Bias, incontestability, and erosion of privacy are mechanisms by which existing social, economic and health disparities are perpetuated and amplified. The implication is that there is a significant risk that use of automated decision systems in health will exacerbate existing population health inequities. The industrial scale and rapidity with which automated decision systems can be applied to whole populations heightens the risk to population health equity. There is a need therefore to design and implement automated decision systems with care, monitor their impact over time, and develop capacities to respond to issues as they emerge.

The Risk to Population Health Equity Posed by Automated Decision Systems: A Narrative Review.

The objective of this study was to propose an analytic framework for heat vulnerability based on the detailed spatial unit of a city. Data were analyzed for Daegu city, South Korea, during the period from June 1, 2018, to October 30, 2018. Four types of physical and environmental indicators were used: building density (BD), urbanization density (UD), green density (GD), and park density (PD). By synthesizing these indicators with the principal component analysis (PCA) technique, we created a comprehensive urbanization rate (CUR) index that contributes to the relative comparison of the degree of urbanization between different regions. To verify the generated index, the distribution of the CUR index was compared with heat, and a positive correlation was confirmed. Finally, a heat prediction model was proposed that included the CUR index as the input variable. To resolve the limitation of spatial autocorrelation in traditional linear regression, a deep learning algorithm was introduced. The final model had a root-mean-squared error (RMSE) value of 0.296. In the future, it is expected that the proposed CUR index and the developed heat prediction model can be utilized as an analytic framework for heat vulnerability within a city. 

Deep Learning-Based analytic framework using comprehensive urbanization index for heat vulnerability assessment in urban areas

This study investigates the use of Deep Neural Learning to predict propensity for disease from demographic information alone, with thyroid disease as the test application. The imbalanced dataset of 747,301 samples contained 13 demographic predictor variables that were not known to be associated with the disease, and had much missing information. A TensorFlow feed-forward neural network was trained to predict thyroid disease. Different activation functions and a variety of up-sampling and down-sampling methods were employed. The lift statistic was used to evaluate success in identifying patients with a propensity for thyroid disease. The DNN model outperformed the Random Forest model with a 36.63% improvement in the lift statistic. These results suggest that deep learning may be successfully employed to select candidates for early intervention for improved health outcomes, utilizing a large dataset with only minimal demographic variables, similar to datasets that are held by the marketing arms of healthcare providers.

Applying Deep Learning to Public Health: Using Unbalanced Demographic Data to Predict Thyroid Disorder

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

John LaGue

Author Tools

Chat about Author

Papers

Applying Deep Learning to Public Health: Using Unbalanced Demographic Data to Predict Thyroid Disorder