Top Research Papers on Machine Learning in Healthcare

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.

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.

This paper explores how BigQuery ML Cloud service helps healthcare researchers and data analysts to build and deploy models using SQL, without need for advanced ML knowledge, and demonstrates that the Boosted Tree model achieved the highest performance among the three models making it highly effective for diabetes prediction.

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

In conclusion, machine learning has enormous potential to improve healthcare by improving diagnoses, customizing treatments, and increasing operational efficiency.

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.

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 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.

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.

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

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 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.

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 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.

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.

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.

This enumerative article provides a thorough review of machine learning in healthcare, covering its terminology, basic concepts, and present and potential uses.

The notion is to create a medical chatbot using Neural Networks that can provide with the info and diagnose the disease and deliver basic information about the disease, and when and where to consult a doctor.

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).

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.

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.

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.

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.

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.

A state-of-the-art review of quantum computing concepts, quantum machine learning framework, and the various applications of quantumMachine learning in the domain of healthcare is presented.

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.

A review of multimodal machine learning approaches in healthcare is provided, offering a comprehensive overview of recent literature and discussing the various data modalities used in clinical diagnosis, with a particular emphasis on imaging data.

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.

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 review paper is mainly considered as an overview of how machine learning is being used to further improve the healthcare sector by making it easier to diagnose and cure diseases too early and also reduce its cost.

This paper analyses how advancement in machine learning can be best utilized in improving health care services and proposes a model that will add value to the existing applications.

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.

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 paper uses real data from more than 10,000 patients over 6 years that includes detailed cancer information, treatment plans, and survival statistics to construct Machine Learning classifiers to suggest treatment plans.

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.

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.

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.

An artificial intelligence framework, grounded in software engineering principles, is proposed for identifying and mitigating biases in data and models while ensuring fairness in healthcare settings to evaluate its impact on promoting health equity.

The brief details about various machine learning approach are provided and the role of these algorithms in field of healthcare system like diabetic, detection of cancer, brain tumor, bioinformatics and many more are reviewed.

An overview of predictive analytics using machine learning in healthcare, including its applications in predicting patient outcomes, optimising healthcare operations, and developing new drugs and therapies is provided.

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.

The study examines why ML remains hard to use directly in medical settings through an analysis of data quality gaps plus ethical and interpretability problems, and examines possible future directions alongside recommended methods to upgrade present restrictions.

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.

The proposed solution describes a multilingual healthcare chatbot application that can perform disease diagnosis based on user symptoms and responds to user queries by calculating sentence similarity by using TF-IDF and Cosine Similarity techniques and choosing the most appropriate response from its knowledge database.

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 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.

A multimodal framework, AutoPrognosis-M, that enables the integration of structured clinical data and medical imaging using automated machine learning and highlights the importance of multimodal machine learning and the power of combining multiple fusion strategies using ensemble learning.