Machine learning in healthcare: review, opportunities and challenges

Nayyar, Anand; Gadhavi, Lata; Jhanjhi, N. Z.

doi:10.1016/b978-0-12-821229-5.00011-2

Cited by 48 publications

(9 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the medical domain, ML can predict diagnoses, prognoses, and formulate comprehensive treatment plans for newly identified diseases, facilitating the monitoring and assessment of treatment efficacy. ML technology can offer healthcare professionals more accurate and timely solutions [54].…”

Section: Challenges and Future Directions For ML Applicationmentioning

confidence: 99%

Application of Machine Learning Based on Structured Medical Data in Gastroenterology

Kim,

Gong,

Bang

2023

Biomimetics

View full text Add to dashboard Cite

The era of big data has led to the necessity of artificial intelligence models to effectively handle the vast amount of clinical data available. These data have become indispensable resources for machine learning. Among the artificial intelligence models, deep learning has gained prominence and is widely used for analyzing unstructured data. Despite the recent advancement in deep learning, traditional machine learning models still hold significant potential for enhancing healthcare efficiency, especially for structured data. In the field of medicine, machine learning models have been applied to predict diagnoses and prognoses for various diseases. However, the adoption of machine learning models in gastroenterology has been relatively limited compared to traditional statistical models or deep learning approaches. This narrative review provides an overview of the current status of machine learning adoption in gastroenterology and discusses future directions. Additionally, it briefly summarizes recent advances in large language models.

show abstract

Section: Challenges and Future Directions For ML Applicationmentioning

confidence: 99%

Application of Machine Learning Based on Structured Medical Data in Gastroenterology

Kim,

Gong,

Bang

2023

Biomimetics

View full text Add to dashboard Cite

show abstract

“…Covering a different range of operating principles for the algorithms ensures an investigation of the data without bias caused by a single chosen algorithm. However, all the algorithms have been used in the literature for several biomedical studies, which shows their feasibility [68][69][70][71].…”

Section: Machine Learning Analysismentioning

confidence: 99%

Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture

et al. 2022

View full text Add to dashboard Cite

Fractures of the femur are a frequent problem in elderly people, and it has been demonstrated that treating them with a diagnostic–therapeutic–assistance path within 48 h of admission to the hospital reduces complications and shortens the length of the hospital stay (LOS). In this paper, the preoperative data of 1082 patients were used to further extend the previous research and to generate several models that are capable of predicting the overall LOS: First, the LOS, measured in days, was predicted through a regression analysis; then, it was grouped by weeks and was predicted with a classification analysis. The KNIME analytics platform was applied to divide the dataset for a hold-out cross-validation, perform a multiple linear regression and implement machine learning algorithms. The best coefficient of determination (R2) was achieved by the support vector machine (R2 = 0.617), while the mean absolute error was similar for all the algorithms, ranging between 2.00 and 2.11 days. With regard to the classification analysis, all the algorithms surpassed 80% accuracy, and the most accurate algorithm was the radial basis function network, at 83.5%. The use of these techniques could be a valuable support tool for doctors to better manage orthopaedic departments and all their resources, which would reduce both waste and costs in the context of healthcare.

show abstract

“…One of the most important ways in which AI can be used in healthcare is to analyze large amounts of medical data. By using machine learning algorithms to identify patterns and trends in these data, AI can help medical professionals make more accurate diagnoses, predict which patients are at risk of developing certain diseases, and develop more personalized treatment plans [ 33 ]. AI can also be used to monitor patients’ health and vital signs in real-time, and to alert medical professionals to potential problems.…”

Section: Introductionmentioning

confidence: 99%

Reviewing Federated Machine Learning and Its Use in Diseases Prediction

Moshawrab

Adda

Bouzouane

et al. 2023

Sensors

View full text Add to dashboard Cite

Machine learning (ML) has succeeded in improving our daily routines by enabling automation and improved decision making in a variety of industries such as healthcare, finance, and transportation, resulting in increased efficiency and production. However, the development and widespread use of this technology has been significantly hampered by concerns about data privacy, confidentiality, and sensitivity, particularly in healthcare and finance. The “data hunger” of ML describes how additional data can increase performance and accuracy, which is why this question arises. Federated learning (FL) has emerged as a technology that helps solve the privacy problem by eliminating the need to send data to a primary server and collect it where it is processed and the model is trained. To maintain privacy and improve model performance, FL shares parameters rather than data during training, in contrast to the typical ML practice of sending user data during model development. Although FL is still in its infancy, there are already applications in various industries such as healthcare, finance, transportation, and others. In addition, 32% of companies have implemented or plan to implement federated learning in the next 12–24 months, according to the latest figures from KPMG, which forecasts an increase in investment in this area from USD 107 million in 2020 to USD 538 million in 2025. In this context, this article reviews federated learning, describes it technically, differentiates it from other technologies, and discusses current FL aggregation algorithms. It also discusses the use of FL in the diagnosis of cardiovascular disease, diabetes, and cancer. Finally, the problems hindering progress in this area and future strategies to overcome these limitations are discussed in detail.

show abstract

Machine learning in healthcare: review, opportunities and challenges

Cited by 48 publications

References 32 publications

Application of Machine Learning Based on Structured Medical Data in Gastroenterology

Application of Machine Learning Based on Structured Medical Data in Gastroenterology

Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture

Reviewing Federated Machine Learning and Its Use in Diseases Prediction

Contact Info

Product

Resources

About