Applying Bayesian Ridge Regression AI Modeling in Virus Severity
Prediction
- URL: http://arxiv.org/abs/2310.09485v3
- Date: Mon, 4 Dec 2023 21:11:45 GMT
- Title: Applying Bayesian Ridge Regression AI Modeling in Virus Severity
Prediction
- Authors: Jai Pal, Bryan Hong
- Abstract summary: We review the strengths and weaknesses of Bayesian Ridge Regression, an AI model that can be used to bring cutting edge virus analysis to healthcare professionals.
The model's accuracy assessment revealed promising results, with room for improvement.
In addition, the severity index serves as a valuable tool to gain a broad overview of patient care needs.
- Score: 0.0
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Artificial intelligence (AI) is a powerful tool for reshaping healthcare
systems. In healthcare, AI is invaluable for its capacity to manage vast
amounts of data, which can lead to more accurate and speedy diagnoses,
ultimately easing the workload on healthcare professionals. As a result, AI has
proven itself to be a power tool across various industries, simplifying complex
tasks and pattern recognition that would otherwise be overwhelming for humans
or traditional computer algorithms. In this paper, we review the strengths and
weaknesses of Bayesian Ridge Regression, an AI model that can be used to bring
cutting edge virus analysis to healthcare professionals around the world. The
model's accuracy assessment revealed promising results, with room for
improvement primarily related to data organization. In addition, the severity
index serves as a valuable tool to gain a broad overview of patient care needs,
aligning with healthcare professionals' preference for broader categorizations.
Related papers
- Zero Shot Health Trajectory Prediction Using Transformer [11.660997334071952]
Enhanced Transformer for Health Outcome Simulation (ETHOS) is a novel application of the transformer deep-learning architecture for analyzing health data.
ETHOS is trained using Patient Health Timelines (PHTs)-detailed, tokenized records of health events-to predict future health trajectories.
arXiv Detail & Related papers (2024-07-30T18:33:05Z) - AI-Driven Healthcare: A Survey on Ensuring Fairness and Mitigating Bias [2.398440840890111]
AI applications have significantly improved diagnostic accuracy, treatment personalization, and patient outcome predictions.
These advancements also introduce substantial ethical and fairness challenges.
These biases can lead to disparities in healthcare delivery, affecting diagnostic accuracy and treatment outcomes across different demographic groups.
arXiv Detail & Related papers (2024-07-29T02:39:17Z) - The Potential and Perils of Generative Artificial Intelligence for Quality Improvement and Patient Safety [27.753117791280857]
Generative artificial intelligence (GenAI) has the potential to improve healthcare through automation that enhances the quality and safety of patient care.
We posit that the imminent application of GenAI in healthcare will be through well-defined, low risk, high value, and narrow applications.
We contrast this with a general, all-purpose AI model for end-to-end clinical decision making that improves clinician performance.
arXiv Detail & Related papers (2024-06-23T15:01:11Z) - A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring.
As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs.
The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
arXiv Detail & Related papers (2024-05-21T06:44:40Z) - Ensuring Trustworthy Medical Artificial Intelligence through Ethical and
Philosophical Principles [4.705984758887425]
AI-based computer-assisted diagnosis and treatment tools can democratize healthcare by matching the clinical level or surpassing clinical experts.
The democratization of such AI tools can reduce the cost of care, optimize resource allocation, and improve the quality of care.
integrating AI into healthcare raises several ethical and philosophical concerns, such as bias, transparency, autonomy, responsibility, and accountability.
arXiv Detail & Related papers (2023-04-23T04:14:18Z) - Safe AI for health and beyond -- Monitoring to transform a health
service [51.8524501805308]
We will assess the infrastructure required to monitor the outputs of a machine learning algorithm.
We will present two scenarios with examples of monitoring and updates of models.
arXiv Detail & Related papers (2023-03-02T17:27:45Z) - Robust and Efficient Medical Imaging with Self-Supervision [80.62711706785834]
We present REMEDIS, a unified representation learning strategy to improve robustness and data-efficiency of medical imaging AI.
We study a diverse range of medical imaging tasks and simulate three realistic application scenarios using retrospective data.
arXiv Detail & Related papers (2022-05-19T17:34:18Z) - MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence
using Federated Evaluation [110.31526448744096]
We argue that unlocking this potential requires a systematic way to measure the performance of medical AI models on large-scale heterogeneous data.
We are building MedPerf, an open framework for benchmarking machine learning in the medical domain.
arXiv Detail & Related papers (2021-09-29T18:09:41Z) - Explainable AI meets Healthcare: A Study on Heart Disease Dataset [0.0]
The aim is to enlighten practitioners on the understandability and interpretability of explainable AI systems using a variety of techniques.
Our paper contains examples based on the heart disease dataset and elucidates on how the explainability techniques should be preferred to create trustworthiness.
arXiv Detail & Related papers (2020-11-06T05:18:43Z) - UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced
Data [81.00385374948125]
We present UNcertaInTy-based hEalth risk prediction (UNITE) model.
UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data.
We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD)
UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
arXiv Detail & Related papers (2020-10-22T02:28:11Z) - Self-Training with Improved Regularization for Sample-Efficient Chest
X-Ray Classification [80.00316465793702]
We present a deep learning framework that enables robust modeling in challenging scenarios.
Our results show that using 85% lesser labeled data, we can build predictive models that match the performance of classifiers trained in a large-scale data setting.
arXiv Detail & Related papers (2020-05-03T02:36:00Z)
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.