Top Research Papers on Machine Learning in Healthcare

The restricted and monitored application of ML in healthcare may hasten the healthcare system, save time, help to make efficient decisions in non-invasive ways, and may open up new possibilities in the healthcare system.

This study elaborates on the CRISP-ML methodology on the determination, measurement, and achievement of the necessary level of interpretability of ML solutions in the Public Healthcare sector, and concludes with the three main directions for the development of the presented cross-industry standard process.

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

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.

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 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.

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.

An attempt is made to analyze the exhaustive role of ANNs and MLs in the field of Healthcare Industry and about the dependency of Healthcare sector towards these techniques.

A proposed system that uses AI and ML algorithms to previously available datasets to provide medical prescriptions and disease prediction based on the symptoms provided by the user, and integrates state-of-the-art AI technology, including Natural Language Processing and Deep Learning to enable the chatbot to provide contextually appropriate descriptions of medical conditions and treatments.

The importance role of ML-DQA in healthcare projects is demonstrated and this study provides teams a framework to conduct these essential activities and demonstrates the importance of rules-based transformations.

The IoNT offers a medium to connect various nanodevices with the help of high speed networks and has the potential to foster innovation while simultaneously improving productivity and delivering better outcomes across the value chain.

This review examines how Probabilistic machine learning can advance healthcare, and considers challenges in the predictive model building pipeline where probabilistic models can be beneficial including calibration and missing data.

This article serves as a primer to illuminate challenges of learning in a clinical setting and highlights opportunities for members of the machine learning community to contribute to healthcare.

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.

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.

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.

This paper aims to present, analyze and discuss some of the latest advancements in machine learning from the healthcare point of view, and draw a conclusion whether there is a need and possibility for potential further development of machine learning algorithms in healthcare.

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.

Various machine learning algorithms used for developing efficient decision support for healthcare applications are reviewed to help in reducing the research gap for building efficient decisionSupport system for medical applications.

This research has to examine various methods used to create effective decision assistance for medical applications to contribute to minimizing the gap of generating effective decision support systems for healthcare applications.

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.

The problem healthcare as a multi-faceted systems level problem that necessitates careful of different notions of fairness in healthcare to corresponding concepts in machine learning is elucidated via different real world examples.

A brief history of machine learning, some basic knowledge regarding the techniques, and the current state of this technology in healthcare are examined.

The potential of utilizing machine learning technologies in healthcare is discussed and various industry initiatives using machine learning initiatives in the healthcare sector are outlined.

The landscape of recent advances to address the challenges model interpretability in healthcare and also how one would go about choosing the right interpretable machine learnig algorithm for a given problem in healthcare are explored.

The role of these subfields in machine learning algorithms in healthcare such as bioinformatics, gene detection for cancer diagnosis, epileptic seizure, brain-computer interface, and medical image processing through deep learning are reviewed.

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.

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 review discusses how ML can transform healthcare epidemiology, providing examples of successful applications, and presents special considerations for those healthcare epidemiologists who want to use and apply ML.

The present review discusses the basics of Artificial Intelligence, Machine Learning and Internet of Things, along with their applications in the field of healthcare.

This review builds the bridge by exposing fairness problems, summarizing possible biases, sorting out mitigation methods and pointing out challenges along with opportunities for the future.

In this chapter, the application of machine learning technique in healthcare is discussed in detail.

Over the past few years, there is a race to implement Artificial Intelligence (AI) and ML in this sector and the proposed models are nowhere near the actual implementation of these models in the real world.

The Medical chat bot is created to diagnose diseases and provide basic information before consulting a physician by doing so, healthcare costs will be reduced and medical knowledge will improve.

This work provides guidance across the full pipeline of MLHOps from conception to initial and ongoing deployment and ethical considerations (including bias, fairness, interpretability, and privacy).

This thesis explores online mental health forums as a digital mental health platform and the possibility to automate treatments and diagnostics based on user-shared information.

A critical review of the associated fairness metrics from a machine learning standpoint is provided and biases and mitigation strategies across the stages of the ML lifecycle are examined, discussing the relationship between biases and their countermeasures.

This work is the review of the contribution of the emerging technology of TinyML in healthcare applications at the edge, requiring the integration of Machine Learning algorithms, followed by the solutions it can bring, especially in wearable devices.

The scope of the current paper is to survey the utilization of feature selection and techniques of Machine Learning, such as Classification and Clustering in the specific context of disease diagnosis and early prediction in healthcare analytics for the benefit of practitioners and researchers.

This paper gives an overview of how combining these two technologies can help in healthcare sectors by combining machine learning with the security and reliability of Blockchain Technology.

It is hypothesized that AI-enabled medicine will continue to depend largely on human intelligence to be efficient at least for the foreseeable future, and intelligent machines should be viewed as co-workers with man.

This report aims to critically analyse the implementation of ML in the healthcare industry by understanding how ML is revolutionizing the industry and what its full potential is for the industry.

This paper presents a large-scale simulation of the dynamic response of the immune system to shocks and shows real-time fluctuations in the response of animals to shocks.

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.

An overview of all the areas where machine learning / artificial intelligence techniques can be applied in healthcare whether it is the diagnosis, treatment etc.

This book aims to provide a “snapshot” of the state of current research at the interface between machine learning and healthcare, and has placed special emphasis on machine learning projects that are (or are close to) achieving improvement in patient outcomes.

The fundamentals of four traditional machine learning algorithms (DT, RF, SVM, KNN and one deep learning neural network (DNN) are introduced and how these algorithms function in assisting clinical diagnosis and disease prediction are illustrated.

Data collection issues, ML model selection, and the varied uses of ML, from personalized treatment plans to disease detection, are discussed in this paper, which aims to create futuristic healthcare systems which are more efficient and focused on patient care.

The cross-lingual method demonstrates improvements in Aphasia detection over unilingual baselines, and the early results on the newly collected dataset show the promise to achieve a strong baseline in Alzheimer's disease detection.

This review article, which serves as the introduction to the special session on deep learning, provides state-of-the-art models and summarizes current understanding on this type of learning method, which is used to tackle a variety of difficult categorization tasks.