Machine Learning and Deep Learning Methods for Building Intelligent Systems in Medicine and Drug Discovery: A Comprehensive Survey

• URL: http://arxiv.org/abs/2107.14037v1
• Date: Mon, 19 Jul 2021 14:26:03 GMT
• Title: Machine Learning and Deep Learning Methods for Building Intelligent Systems in Medicine and Drug Discovery: A Comprehensive Survey
• Authors: G Jignesh Chowdary, Suganya G, Premalatha M, Asnath Victy Phamila Y, Karunamurthy K
• Abstract summary: Artificail Intelligence based framework is rapidly revolutionizing the healthcare industry. These intelligent systems are built with machine learning and deep learning based robust models for early diagnosis of diseases. This paper focuses on the survey of machine learning and deep learning applications in across 16 medical specialties.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: With the advancements in computer technology, there is a rapid development of intelligent systems to understand the complex relationships in data to make predictions and classifications. Artificail Intelligence based framework is rapidly revolutionizing the healthcare industry. These intelligent systems are built with machine learning and deep learning based robust models for early diagnosis of diseases and demonstrates a promising supplementary diagnostic method for frontline clinical doctors and surgeons. Machine Learning and Deep Learning based systems can streamline and simplify the steps involved in diagnosis of diseases from clinical and image-based data, thus providing significant clinician support and workflow optimization. They mimic human cognition and are even capable of diagnosing diseases that cannot be diagnosed with human intelligence. This paper focuses on the survey of machine learning and deep learning applications in across 16 medical specialties, namely Dental medicine, Haematology, Surgery, Cardiology, Pulmonology, Orthopedics, Radiology, Oncology, General medicine, Psychiatry, Endocrinology, Neurology, Dermatology, Hepatology, Nephrology, Ophthalmology, and Drug discovery. In this paper along with the survey, we discuss the advancements of medical practices with these systems and also the impact of these systems on medical professionals.

Related papers

• A Survey of Models for Cognitive Diagnosis: New Developments and Future Directions [66.40362209055023]
  This paper aims to provide a survey of current models for cognitive diagnosis, with more attention on new developments using machine learning-based methods. By comparing the model structures, parameter estimation algorithms, model evaluation methods and applications, we provide a relatively comprehensive review of the recent trends in cognitive diagnosis models.
  arXiv  Detail & Related papers  (2024-07-07T18:02:00Z)
• Dermacen Analytica: A Novel Methodology Integrating Multi-Modal Large Language Models with Machine Learning in tele-dermatology [1.999925939110439]
  We describe, implement and assess an Artificial Intelligence-empowered system and methodology aimed at assisting the diagnosis process of skin lesions and other skin conditions. The proposed methodology is expected to prove useful in the development of next-generation tele-dermatology applications.
  arXiv  Detail & Related papers  (2024-03-21T09:02:17Z)
• MedKP: Medical Dialogue with Knowledge Enhancement and Clinical Pathway Encoding [48.348511646407026]
  We introduce the Medical dialogue with Knowledge enhancement and clinical Pathway encoding framework. The framework integrates an external knowledge enhancement module through a medical knowledge graph and an internal clinical pathway encoding via medical entities and physician actions.
  arXiv  Detail & Related papers  (2024-03-11T10:57:45Z)
• Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review [2.2268038840298714]
  We review the state-of-the-art machine learning studies that adopted the fusion of biomedical knowledge and data. We provide an overview of diverse forms of knowledge representation and current strategies of knowledge integration into machine learning pipelines.
  arXiv  Detail & Related papers  (2024-01-12T07:01:36Z)
• Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment [65.268245109828]
  We examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology. We organize the ever-growing set of brain-inspired learning schemes into six general families and consider these in the context of backpropagation of errors. The results of this review are meant to encourage future developments in neuro-mimetic systems and their constituent learning processes.
  arXiv  Detail & Related papers  (2023-12-01T05:20:57Z)
• Recent advancement in Disease Diagnostic using machine learning: Systematic survey of decades, comparisons, and challenges [0.0]
  Pattern recognition and machine learning in the biomedical area promise to increase the precision of disease detection and diagnosis. This review article examines machine-learning algorithms for detecting diseases, including hepatitis, diabetes, liver disease, dengue fever, and heart disease.
  arXiv  Detail & Related papers  (2023-07-31T16:35:35Z)
• AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning [72.2614468437919]
  We present a machine learning framework, AutoPrognosis 2.0, to develop diagnostic and prognostic models. We provide an illustrative application where we construct a prognostic risk score for diabetes using the UK Biobank. Our risk score has been implemented as a web-based decision support tool and can be publicly accessed by patients and clinicians worldwide.
  arXiv  Detail & Related papers  (2022-10-21T16:31:46Z)
• A review of machine learning approaches, challenges and prospects for computational tumor pathology [1.2036642553849346]
  Tumor computational pathology challenges data integration, hardware processing, network sharing bandwidth and machine learning technology. This review investigates preprocessing methods in computational pathology from a pathological and technical perspective. The challenges and prospects of machine learning in computational pathology applications are discussed.
  arXiv  Detail & Related papers  (2022-05-31T14:56:01Z)
• Machine Learning Applications for Therapeutic Tasks with Genomics Data [49.98249191161107]
  We review the literature on machine learning applications for genomics through the lens of therapeutic development. We identify twenty-two machine learning in genomics applications across the entire therapeutics pipeline. We pinpoint seven important challenges in this field with opportunities for expansion and impact.
  arXiv  Detail & Related papers  (2021-05-03T21:20:20Z)
• Representation Learning for Networks in Biology and Medicine: Advancements, Challenges, and Opportunities [18.434430658837258]
  We have witnessed a rapid expansion of representation learning techniques into modeling, analysis, and learning with networks. In this review, we put forward an observation that long-standing principles of network biology and medicine can provide the conceptual grounding for representation learning. We synthesize a spectrum of algorithmic approaches that leverage topological features to embed networks into compact vector spaces.
  arXiv  Detail & Related papers  (2021-04-11T00:20:00Z)
• Machine Learning in Nano-Scale Biomedical Engineering [77.75587007080894]
  We review the existing research regarding the use of machine learning in nano-scale biomedical engineering. The main challenges that can be formulated as ML problems are classified into the three main categories. For each of the presented methodologies, special emphasis is given to its principles, applications, and limitations.
  arXiv  Detail & Related papers  (2020-08-05T15:45:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.