Deep Learning for Medical Decision Support Systems

International Journal of Technical Research & Science, 2021

Today a variety of health care practices have been evolved to maintain and restore health by the latest prevention and best treatment. This implements biomedical sciences, biomedical research, genetics and medical technology to diagnose, treat, and prevent injury and disease, typically through pharmaceuticals or surgery, therapies as divers as psychotherapy, external splints and traction, medical devices, biologics, and ionizing radiation. With advances in technology, the health sciences are constantly pushing toward more effective treatments and cures. With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also provoked increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reform the future of artificial intelligence. Rapid improvements in computational power, fast data storage, and parallelization have also contributed to the rapid uptake of the technology in addition to its predictive power and ability to generate automatically optimized high-level features and semantic interpretation from the input data. This paper presents a comprehensive review of research employing deep learning in health informatics, providing a critical analysis of the relative merit, and potential pitfalls of the technique as well as its future outlook. The paper mainly focuses on key applications of deep learning in the fields of translational bioinformatics, medical imaging, pervasive sensing, medical informatics, and public health. Finally the limitations and challenges of deep learning in the field of health informatics have been discussed.

IAEME Publication, 2020

Artificial intelligence (AI) has been developing rapidly in recent years in terms of software algorithms, hardware implementation, and applications in a vast number of areas. In this review, we summarize the latest developments of applications of AI in biomedicine, including disease diagnostics, living assistance, biomedical information processing, and biomedical research. Various automated systems and tools like Braincomputer interfaces (BCIs), arterial spin labelling (ASL) imaging, ASL-MRI, biomarkers, Natural language processing (NLP) and various algorithms helps to minimize errors and control disease progression. The computer assisted diagnosis, decision support systems, expert systems and implementation of software may assist physicians to minimize the intra and inter-observer variability. In this paper, a detailed literature review on application and implementation of Machine Learning, Deep Learning and Artificial Intelligence in the healthcare industry by various researchers.

Journal of Clinical Practice and Research, 2024

Artificial intelligence (AI) is the ability of machines to carry out tasks by imitating human intelligence. In recent years, AI methods have begun to be applied in many different areas, with healthcare being one of the most prominent. Diagnosis, treatment, patient care, new drug production, and preventive care can be listed as some of the applications of AI in healthcare. In this review, deep learning methods, which are a sub-branch of AI, are mentioned. Deep learning methods frequently used in the literature are convolutional neural networks (CNNs), stacked autoencoders (SAEs), and recurrent neural networks (RNNs). These deep learning methods include CNNs for image recognition and classification, SAEs for unsupervised feature learning and dimensionality reduction, and RNNs for analyzing sequential data like time-series. However, it should be noted that these methods can also be applied to other application areas. This paper presents studies in the literature on medical image analysis, drug discovery and development, and remote patient monitoring in which these deep learning methods are used.

2019

Medical care has always presented quite wide ranged and challenging problems. However, machine learning techniques and methods as well as deep learning never stopped evolving and tackling those challenges issued by medicine, medical and health care. In order to have a more close up look on how machine learning and deep learning has been affecting medical care in general, we review in this paper some machine learning and deep learning techniques used in a variety of medical care sections such as medical imaging, medical decision, diagnostic, medical records and big data, and disease prediction.

In today's scenario deep learning is the primarily used technique in Computer-Aided Diagnosis (CAD) for prediction of diseases. Deep learning has empowered the evolution of more data-driven solutions in the field of health informatics by authorizing automatic feature generation and lessening human intervention. In domains such as health informatics, without human intervention the generation of this automatic feature set has several advantages, for example, in medical imaging some features might be more sophisticated and difficult to interpret. Especially in prediction of tumors, different neurodegenerative disorders and their specialized features, DNA & RNA sequences, structure of the protein and many more. In this chapter we have discussed different deep learning methods used in CAD and in the health sector. Also we have discussed many application areas in the field of medical imaging and health informatics.

2018

Journal of Health & Medical Informatics, 2018

Objective: The purpose of this paper is to review the PubMed/MEDLINE literature for articles that discuss the use of machine learning (ML) and deep learning (DL) for clinical decision support systems (CDSSs). Materials and Methods: To identify relevant articles, we searched PubMed/MEDLINE through December 2 nd , 2017. We identified a total of 283 studies. Results: The number of ML and DL associated CDSS articles increased significantly beginning around 2010. The most common type of advanced artificial intelligence (AI) methodologies that the articles evaluated was neural networks also known as DL (n=109) followed by ML (n=86). The most common types of ML algorithm were support vector machines (n=78), logistic regression analysis (n=38), random forest (n=26), decision tree (n=25), and k-nearest neighbour (n=21). Cardiology, oncology, radiology, surgery, and critical care/ED were the most commonly represented specialties. Only 19 out of 283 (6.7%) ML and DL associated CDSS articles reported an effect on the process of care or patient outcomes. Discussion: The current decade has seen research efforts and attention increase significantly in creating CDSS tools with the advanced AI methodologies of DL and ML. Although the research experiments demonstrate success, the scope of AI technology is still limited to a well-defined task. Also, most of these studies lack patient-oriented outcomes necessary to justify its widespread application in healthcare. Conclusion: There is a clear upwards trend in ML and DL research in healthcare. However, in order to effectively translate successful AI research into the patient care, more clinically-relevant studies must be pursued.

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024

Through the combination of machine learning (ML) and deep learning (DL) approaches, substantial progress has been made in the field of medical picture categorization, which is an essential component in the field of medical diagnostics. Within the context of medical picture categorization, this paper provides an in-depth examination of the development, methodology, and applications of machine learning and deep learning. By making use of handmade features, traditional machine learning techniques, such as support vector machines and decision trees, have laid the groundwork for early achievements in the field. On the other hand, the introduction of deep learning, and more specifically convolutional neural networks (CNNs), has brought about a revolution in the industry by making it possible to automatically extract features and obtaining greater performance. This article takes a look at a number of different deep learning architectures, including ResNet, VGG, and Inception, and highlights the contributions that these designs have made to tasks such as illness categorization, organ segmentation, and tumor identification. In addition to this, it discusses alternative solutions such as data augmentation, transfer learning, and model optimization after addressing problems such as the lack of data, the interpretability of the data, and the demands placed on the computing resources. In addition, the evaluation takes into account the ethical concerns, as well as the need for rigorous validation in order to guarantee clinical application. This study highlights the revolutionary influence that machine learning and deep learning have had on medical imaging by conducting a comparative analysis of current research. It also highlights the ongoing need for innovation and cooperation across disciplines in order to improve diagnostic accuracy and patient outcomes.

2017

Deep learning is an emerging area of machine learning (ML) research. It comprises multiple hidden layers of artificial neural networks. The deep learning methodology applies nonlinear transformations and model abstractions of high level in large databases. The recent advancements in deep learning architec-tures within numerous fields have already provided significant contributions in artificial intelligence. This article presents a state of the art survey on the contributions and the novel applications of deep learning. The following review chronologically presents how and in what major applications deep learning algorithms have been utilized. Furthermore, the superior and beneficial of the deep learning methodology and its hierarchy in layers and nonlinear operations are presented and compared with the more conventional algorithms in the common applications. The state of the art survey further provides a general overview on the novel concept and the ever-increasing advantages and popularity of deep learning.

2023 4th IEEE Global Conference for Advancement in Technology (GCAT), 2023

The potential for machine learning and deep learning to revolutionize the healthcare industry is examined in this thorough survey study. These cutting-edge technologies have the potential to completely transform healthcare by providing accurate diagnoses, customizing drugs for each patient, and ultimately enhancing patient outcomes. The study offers a thorough investigation of a number of applications, such as clinical decision support systems, electronic health record analysis, illness diagnosis and prediction, personalized medicine, and drug development. In this article, we focus on the essential methodologies, obstacles, and opportunities related to the use of machine learning and deep learning in healthcare. This source aims to be a helpful resource for researchers, medical practitioners, and decision-makers who are looking to maximize the benefits of modern technologies to improve the provision of healthcare services. Additionally, we would like to contribute to a better and more effective healthcare environment by bridging the technology and healthcare barrier.