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Translational Vision Science &... Feb 2020To present an overview of current machine learning methods and their use in medical research, focusing on select machine learning techniques, best practices, and deep...
PURPOSE
To present an overview of current machine learning methods and their use in medical research, focusing on select machine learning techniques, best practices, and deep learning.
METHODS
A systematic literature search in PubMed was performed for articles pertinent to the topic of artificial intelligence methods used in medicine with an emphasis on ophthalmology.
RESULTS
A review of machine learning and deep learning methodology for the audience without an extensive technical computer programming background.
CONCLUSIONS
Artificial intelligence has a promising future in medicine; however, many challenges remain.
TRANSLATIONAL RELEVANCE
The aim of this review article is to provide the nontechnical readers a layman's explanation of the machine learning methods being used in medicine today. The goal is to provide the reader a better understanding of the potential and challenges of artificial intelligence within the field of medicine.
Topics: Artificial Intelligence; Deep Learning; Machine Learning; Neural Networks, Computer; Ophthalmology
PubMed: 32704420
DOI: 10.1167/tvst.9.2.14 -
Circulation Nov 2015Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks,... (Review)
Review
Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome.
Topics: Algorithms; Humans; Machine Learning; Medicine
PubMed: 26572668
DOI: 10.1161/CIRCULATIONAHA.115.001593 -
Molecular Diversity Aug 2021Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time... (Review)
Review
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.
Topics: Algorithms; Animals; Artificial Intelligence; Big Data; Chemistry Techniques, Synthetic; Data Mining; Deep Learning; Drug Design; Drug Development; Drug Discovery; Humans; Machine Learning; Models, Molecular; Quantitative Structure-Activity Relationship; Research Design; Support Vector Machine
PubMed: 33844136
DOI: 10.1007/s11030-021-10217-3 -
Journal of Internal Medicine Dec 2018Machine learning (ML) is a burgeoning field of medicine with huge resources being applied to fuse computer science and statistics to medical problems. Proponents of ML... (Review)
Review
Machine learning (ML) is a burgeoning field of medicine with huge resources being applied to fuse computer science and statistics to medical problems. Proponents of ML extol its ability to deal with large, complex and disparate data, often found within medicine and feel that ML is the future for biomedical research, personalized medicine, computer-aided diagnosis to significantly advance global health care. However, the concepts of ML are unfamiliar to many medical professionals and there is untapped potential in the use of ML as a research tool. In this article, we provide an overview of the theory behind ML, explore the common ML algorithms used in medicine including their pitfalls and discuss the potential future of ML in medicine.
Topics: Algorithms; Decision Support Systems, Clinical; Forecasting; Humans; Machine Learning; Medicine; Precision Medicine; Supervised Machine Learning; Unsupervised Machine Learning
PubMed: 30102808
DOI: 10.1111/joim.12822 -
Behavior Therapy Sep 2020Machine learning is increasingly used in mental health research and has the potential to advance our understanding of how to characterize, predict, and treat mental... (Review)
Review
Machine learning is increasingly used in mental health research and has the potential to advance our understanding of how to characterize, predict, and treat mental disorders and associated adverse health outcomes (e.g., suicidal behavior). Machine learning offers new tools to overcome challenges for which traditional statistical methods are not well-suited. This paper provides an overview of machine learning with a specific focus on supervised learning (i.e., methods that are designed to predict or classify an outcome of interest). Several common supervised learning methods are described, along with applied examples from the published literature. We also provide an overview of supervised learning model building, validation, and performance evaluation. Finally, challenges in creating robust and generalizable machine learning algorithms are discussed.
Topics: Algorithms; Humans; Machine Learning; Supervised Machine Learning
PubMed: 32800297
DOI: 10.1016/j.beth.2020.05.002 -
Minerva Cardiology and Angiology Feb 2022This paper reviews recent cardiology literature and reports how artificial intelligence tools (specifically, machine learning techniques) are being used by physicians in... (Review)
Review
This paper reviews recent cardiology literature and reports how artificial intelligence tools (specifically, machine learning techniques) are being used by physicians in the field. Each technique is introduced with enough details to allow the understanding of how it works and its intent, but without delving into details that do not add immediate benefits and require expertise in the field. We specifically focus on the principal Machine learning based risk scores used in cardiovascular research. After introducing them and summarizing their assumptions and biases, we discuss their merits and shortcomings. We report on how frequently they are adopted in the field and suggest why this is the case based on our expertise in machine learning. We complete the analysis by reviewing how corresponding statistical approaches compare with them. Finally, we discuss the main open issues in applying machine learning tools to cardiology tasks, also drafting possible future directions. Despite the growing interest in these tools, we argue that there are many still underutilized techniques: while neural networks are slowly being incorporated in cardiovascular research, other important techniques such as semi-supervised learning and federated learning are still underutilized. The former would allow practitioners to harness the information contained in large datasets that are only partially labeled, while the latter would foster collaboration between institutions allowing building larger and better models.
Topics: Artificial Intelligence; Cardiology; Machine Learning; Neural Networks, Computer; Supervised Machine Learning
PubMed: 34338485
DOI: 10.23736/S2724-5683.21.05709-4 -
Molecules (Basel, Switzerland) Nov 2020The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the... (Review)
Review
The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.
Topics: Algorithms; Bayes Theorem; Computational Biology; Databases, Factual; Deep Learning; Drug Discovery; Humans; Internet; Machine Learning; Monte Carlo Method; Reproducibility of Results; Software; Support Vector Machine
PubMed: 33198233
DOI: 10.3390/molecules25225277 -
The Journal of Infection Oct 2023Artificial intelligence (AI), machine learning and deep learning (including generative AI) are increasingly being investigated in the context of research and management... (Review)
Review
BACKGROUND
Artificial intelligence (AI), machine learning and deep learning (including generative AI) are increasingly being investigated in the context of research and management of human infection.
OBJECTIVES
We summarise recent and potential future applications of AI and its relevance to clinical infection practice.
METHODS
1617 PubMed results were screened, with priority given to clinical trials, systematic reviews and meta-analyses. This narrative review focusses on studies using prospectively collected real-world data with clinical validation, and on research with translational potential, such as novel drug discovery and microbiome-based interventions.
RESULTS
There is some evidence of clinical utility of AI applied to laboratory diagnostics (e.g. digital culture plate reading, malaria diagnosis, antimicrobial resistance profiling), clinical imaging analysis (e.g. pulmonary tuberculosis diagnosis), clinical decision support tools (e.g. sepsis prediction, antimicrobial prescribing) and public health outbreak management (e.g. COVID-19). Most studies to date lack any real-world validation or clinical utility metrics. Significant heterogeneity in study design and reporting limits comparability. Many practical and ethical issues exist, including algorithm transparency and risk of bias.
CONCLUSIONS
Interest in and development of AI-based tools for infection research and management are undoubtedly gaining pace, although the real-world clinical utility to date appears much more modest.
Topics: Humans; Artificial Intelligence; Deep Learning; COVID-19; Machine Learning; Algorithms
PubMed: 37468046
DOI: 10.1016/j.jinf.2023.07.006 -
Clinical Microbiology and Infection :... May 2020Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious... (Review)
Review
BACKGROUND
Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID).
OBJECTIVES
We aim to inform clinicians about the use of ML for diagnosis, classification, outcome prediction and antimicrobial management in ID.
SOURCES
References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, ACM Digital Library, arXiV and IEEE Xplore Digital Library up to July 2019.
CONTENT
We found 60 unique ML-clinical decision support systems (ML-CDSS) aiming to assist ID clinicians. Overall, 37 (62%) focused on bacterial infections, 10 (17%) on viral infections, nine (15%) on tuberculosis and four (7%) on any kind of infection. Among them, 20 (33%) addressed the diagnosis of infection, 18 (30%) the prediction, early detection or stratification of sepsis, 13 (22%) the prediction of treatment response, four (7%) the prediction of antibiotic resistance, three (5%) the choice of antibiotic regimen and two (3%) the choice of a combination antiretroviral therapy. The ML-CDSS were developed for intensive care units (n = 24, 40%), ID consultation (n = 15, 25%), medical or surgical wards (n = 13, 20%), emergency department (n = 4, 7%), primary care (n = 3, 5%) and antimicrobial stewardship (n = 1, 2%). Fifty-three ML-CDSS (88%) were developed using data from high-income countries and seven (12%) with data from low- and middle-income countries (LMIC). The evaluation of ML-CDSS was limited to measures of performance (e.g. sensitivity, specificity) for 57 ML-CDSS (95%) and included data in clinical practice for three (5%).
IMPLICATIONS
Considering comprehensive patient data from socioeconomically diverse healthcare settings, including primary care and LMICs, may improve the ability of ML-CDSS to suggest decisions adapted to various clinical contexts. Currents gaps identified in the evaluation of ML-CDSS must also be addressed in order to know the potential impact of such tools for clinicians and patients.
Topics: Anti-Infective Agents; Artificial Intelligence; Clinical Decision-Making; Communicable Diseases; Decision Support Systems, Clinical; Early Diagnosis; Humans; Machine Learning; Patient Outcome Assessment; Sepsis
PubMed: 31539636
DOI: 10.1016/j.cmi.2019.09.009 -
BMC Medical Informatics and Decision... Dec 2019Supervised machine learning algorithms have been a dominant method in the data mining field. Disease prediction using health data has recently shown a potential... (Comparative Study)
Comparative Study
BACKGROUND
Supervised machine learning algorithms have been a dominant method in the data mining field. Disease prediction using health data has recently shown a potential application area for these methods. This study ai7ms to identify the key trends among different types of supervised machine learning algorithms, and their performance and usage for disease risk prediction.
METHODS
In this study, extensive research efforts were made to identify those studies that applied more than one supervised machine learning algorithm on single disease prediction. Two databases (i.e., Scopus and PubMed) were searched for different types of search items. Thus, we selected 48 articles in total for the comparison among variants supervised machine learning algorithms for disease prediction.
RESULTS
We found that the Support Vector Machine (SVM) algorithm is applied most frequently (in 29 studies) followed by the Naïve Bayes algorithm (in 23 studies). However, the Random Forest (RF) algorithm showed superior accuracy comparatively. Of the 17 studies where it was applied, RF showed the highest accuracy in 9 of them, i.e., 53%. This was followed by SVM which topped in 41% of the studies it was considered.
CONCLUSION
This study provides a wide overview of the relative performance of different variants of supervised machine learning algorithms for disease prediction. This important information of relative performance can be used to aid researchers in the selection of an appropriate supervised machine learning algorithm for their studies.
Topics: Algorithms; Bayes Theorem; Clinical Decision Rules; Data Mining; Humans; Machine Learning; Risk Factors; Support Vector Machine
PubMed: 31864346
DOI: 10.1186/s12911-019-1004-8