Top Research Papers on Machine Learning in Healthcare

It is fought that the productive execution of ML techniques can help the blend of PC based systems in the social protection condition offering opportunities to energize and overhaul made by therapeutic authorities and finally to improve the adequacy and nature of remedial thought.

This chapter's purpose is to give a general non-technical definition of machine learning, provide a review of its latest implementations in the healthcare domain and add to the ongoing discussion on this subject.

This research presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually cataloging and cataloging medical records to identify patients withcomplex medical needs.

An overview of machine learning-based approaches and learning algorithms including supervised, unsupervised, and reinforcement learning along with examples are provided and the application of ML in several healthcare fields are discussed, including radiology, genetics, electronic health records, and neuroimaging.

Machine learning approaches provide smart healthcare and reduce administrative and supply costs in the field of healthcare.

A clinical risk assessment model for predicting COVID positive pediatric samples with higher risk of being hospitalized, and a machine learning models for predicting such outcomes early using Electronic Health Records (EHR) could potentially help clinical decision making such as advanced and more intensive medical interventions.

Data mining acts as a solution for many healthcare problems and it is helpful in predicting heart disease in early stages and Naive Bayes algorithm is one such data mining technique which helps in the prediction of heart diseaseIn patients.

This study examines numerous applications in the medical sciences of machine learning, a branch of artificial intelligence, and finds support vector algorithms and neural networks may be even more effective than the methods now employed in medicine.

Healthcare is an important and high cost sector that involves many decision-making tasks based on the analysis of data, from its primary activities up till management itself, and a technology that can help in this process is needed.

This study presents a robust predictive model capable of accurately forecasting patient diseases based on input information and various parameters, harnessing the power of extensive datasets encompassing diverse patient populations, and illustrates the substantial potential of ML-driven predictive healthcare models to revolutionize traditional healthcare systems.

A machine learning model is introduced which will identify person with long term diseases by asking some important questions and reports in order to prepare the data set, symptoms, the person's lifestyle choices, and information on doctor consultations were all considered in the overall illness prediction.

The proposed shows that the machine learning processing model is starts from the data mining perspective and has a high specificity rate in which these makes it a handy tool for junior cardiologists to screen out every patients who have a high probability of having the disease and they can be transfer those patients to senior cardiologists for further analysis.

This topical review will highlight how the application of ML/AI in healthcare communication is able to benefit humans and includes chatbots for the COVID-19 health education, cancer therapy, and medical imaging.

On the analysis, machine learning was utilized to determine if the number of flights to Washington D C M etro A rea had an effect on thenumber of cases and deaths reported in the city and surrounding area.

In this research the datasets for many diseases is studied and it will be analyzed that how such deep machine learning will impact to a human life.

A quick rundown of machine learning-based methodologies and learning algorithms, such as supervised, unsupervised, and reinforcement learning, in many healthcare domains, such as genetics, neuroimaging, radiology, and electronic health records.

It is necessary to assume that the mere adaptation ofML-HCAs to the modern healthcare system will be disruptive but sufficient to assumption that themere adaptation of this will not be disruptive.

An overview of machine learning and its applications in the healthcare industry is provided, which can potentially transform various aspects of medical practice, including disease diagnosis, treatment optimization, and public health surveillance.

No longer a pipe dream, AI is already in use in healthcare and here are two real-world examples of machine learning in healthcare.

The various ways in which machine learning is being used in medical websites, the benefits of this technology, and the challenges associated with its implementation are explored.