Top Research Papers on Machine Learning in 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.

Ethics of ML in healthcare is frame through the lens of social justice, and ongoing efforts and challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to postdeployment considerations are described.

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.

Important challenges present in healthcare applications such as processing high-dimensional and unstructured data, generalization to out-of-distribution samples and temporal relationships, that despite the great effort from the research community remain to be solved are discussed.

Considerations for the use and application of ML in healthcare settings include assessing the quality of data inputs and decision-making that serve as the foundations of the ML model, ensuring the end-product is interpretable, transparent, and ethical concerns are considered throughout the development process.

The proliferation of synthetic data in artificial intelligence for medicine and healthcare raises concerns about the vulnerabilities of the software and the challenges of current policy.

In the agriculture and healthcare industries, AI has been deployed to achieve better crop production, disease prediction, continuous monitoring, efficient supply chain management, improved operational efficiency, and reduced water waste, with the main goal of designing standard, reliable product quality control methods and the search for new ways of reaching and serving society while maintaining low cost. Machine learning and deep learning are two of the most often used AI approaches. Individuals, businesses, and government agencies utilize these models to anticipate and learn from data. Machi...

The emergence and development of noninvasive biosensors largely facilitate the collection of physiological signals and the processing of health‐related data and their prospects in real‐time monitoring, out‐of‐clinic diagnosis, and onsite food safety detection are proposed.

This review undertakes the first in-depth study to identify how machine learning models that ingest structured electronic health record data can be applied to clinical decision support tasks and translated into clinical practice.

An overview of interpretability approaches is given and examples of practical interpretability of machine learning in different areas of healthcare, including prediction of health-related outcomes, optimizing treatments or improving the efficiency of screening for specific conditions are provided.

Machine Learning (ML) applications are making a considerable impact on healthcare. ML is a subtype of Artificial Intelligence (AI) technology that aims to improve the speed and accuracy of physicians' work. Countries are currently dealing with an overburdened healthcare system with a shortage of skilled physicians, where AI provides a big hope. The healthcare data can be used gainfully to identify the optimal trial sample, collect more data points, assess ongoing data from trial participants, and eliminate data-based errors. ML-based techniques assist in detecting early indicators of an epidem...

This article provides an overview of key unsupervised machine learning techniques including K-means clustering, hierarchical clustered, principal component analysis, and factor analysis.

With the advent of machine learning (ML) and deep learning (DL) empowered applications for critical applications like healthcare, the questions about liability, trust, and interpretability of their outputs are raising. The black-box nature of various DL models is a roadblock to clinical utilization. Therefore, to gain the trust of clinicians and patients, we need to provide explanations about the decisions of models. With the promise of enhancing the trust and transparency of black-box models, researchers are in the phase of maturing the field of eXplainable ML (XML). In this paper, we provide...

A systematic approach to identifying ML-HCA ethical concerns is outlined, starting with a conceptual model of the pipeline of the conception, development, implementation of ML-HCAs, and the parallel pipeline of evaluation and oversight tasks at each stage.

This research delineates the use of three interpretable supervised ML models: Naïve Bayes classifiers, random forest classifier, and J48 decision tree models to be trained and tested using the Pima Indians diabetes dataset in R programming language.

This survey paper attempts to create a self-contained guide that gives an in-depth overview of the field of Differential Privacy ML, and proposes a set of specific best practices for stating guarantees.

Machine learning is an established and frequently used technique in industry and academia, but a standard process model to improve success and efficiency of machine learning applications is still missing. Project organizations and machine learning practitioners face manifold challenges and risks when developing machine learning applications and have a need for guidance to meet business expectations. This paper therefore proposes a process model for the development of machine learning applications, covering six phases from defining the scope to maintaining the deployed machine learning applicat...

This paper aims to present a comprehensive survey of existing machine learning and deep learning approaches utilized in healthcare prediction and identify the inherent obstacles to applying these approaches in the healthcare domain.

A comprehensive research ethics framework that can apply to the systematic inquiry of ML research across its development cycle and suggest adaptations to traditional paradigms to suit ML while maintaining ethical rigor and the protection of individuals is provided.

The existing literature in the field of automated machine learning (AutoML) is reviewed to help healthcare professionals better utilize machine learning models "off-the-shelf" with limited data science expertise to help there to be widespread adoption of AutoML in healthcare.