The rise and fall of machine learning methods in biomedical research

Koohy, Hashem

doi:10.12688/f1000research.13016.2

Cited by 18 publications

(8 citation statements)

References 10 publications

(10 reference statements)

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“…In the last decade, there has been increased use knowledge discovery techniques of artificial intelligence in pharmacoepidemiology. This result is in line with those of Koohy (2017) who showed an increased popularity of machine learning methods for biomedical research from 1990 to 2017. We strongly believe that one of the major consequences for the increased interest in applying machine learning techniques over the years is the dramatic growth in size and complexity of clinical and biological data that have led to the necessity of combining mathematics, statistics, and computer science to extract actionable insight.…”

Section: Discussionsupporting

confidence: 91%

Artificial Intelligence in Pharmacoepidemiology: A Systematic Review. Part 1—Overview of Knowledge Discovery Techniques in Artificial Intelligence

et al. 2020

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Aim: To perform a systematic review on the application of artificial intelligence (AI) based knowledge discovery techniques in pharmacoepidemiology. Study Eligibility Criteria: Clinical trials, meta-analyses, narrative/systematic review, and observational studies using (or mentioning articles using) artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded.

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Section: Discussionsupporting

confidence: 91%

Artificial Intelligence in Pharmacoepidemiology: A Systematic Review. Part 1—Overview of Knowledge Discovery Techniques in Artificial Intelligence

et al. 2020

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“…Overall, they found that SVMs significantly outperformed random forests, although random forests outperformed SVMs in some cases[ 42 ]. Perhaps in part due to these highly cited studies, SVMs and random forests have been used heavily in diverse types of biomedical research over the past two decades[ 58 ].…”

Section: Discussionmentioning

confidence: 99%

The ability to classify patients based on gene-expression data varies by algorithm and performance metric

et al. 2022

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By classifying patients into subgroups, clinicians can provide more effective care than using a uniform approach for all patients. Such subgroups might include patients with a particular disease subtype, patients with a good (or poor) prognosis, or patients most (or least) likely to respond to a particular therapy. Transcriptomic measurements reflect the downstream effects of genomic and epigenomic variations. However, high-throughput technologies generate thousands of measurements per patient, and complex dependencies exist among genes, so it may be infeasible to classify patients using traditional statistical models. Machine-learning classification algorithms can help with this problem. However, hundreds of classification algorithms exist—and most support diverse hyperparameters—so it is difficult for researchers to know which are optimal for gene-expression biomarkers. We performed a benchmark comparison, applying 52 classification algorithms to 50 gene-expression datasets (143 class variables). We evaluated algorithms that represent diverse machine-learning methodologies and have been implemented in general-purpose, open-source, machine-learning libraries. When available, we combined clinical predictors with gene-expression data. Additionally, we evaluated the effects of performing hyperparameter optimization and feature selection using nested cross validation. Kernel- and ensemble-based algorithms consistently outperformed other types of classification algorithms; however, even the top-performing algorithms performed poorly in some cases. Hyperparameter optimization and feature selection typically improved predictive performance, and univariate feature-selection algorithms typically outperformed more sophisticated methods. Together, our findings illustrate that algorithm performance varies considerably when other factors are held constant and thus that algorithm selection is a critical step in biomarker studies.

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“…Modern health care can take advantage of the potential that AI offers, and research has been growing across medical disciplines. From 2001 to 2017, ML biomedical publications grew 6% per year 20 . A PubMed/MEDLINE search of [“Machine Learning” AND “Spine”] yielded 153 original research articles on the application of ML to spine surgery alone (Fig.…”

Section: What Is Ml?mentioning

confidence: 99%

Machine Learning for the Orthopaedic Surgeon

Alsoof

McDonald

Kuris

et al. 2022

Journal of Bone and Joint Surgery

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Machine learning is a subset of artificial intelligence in which computer algorithms are trained to make classifications and predictions based on patterns in data. The utilization of these techniques is rapidly expanding in the field of orthopaedic research.There are several domains in which machine learning has application to orthopaedics, including radiographic diagnosis, gait analysis, implant identification, and patient outcome prediction.Several limitations prevent the widespread use of machine learning in the daily clinical environment. However, future work can overcome these issues and enable machine learning tools to be a useful adjunct for orthopaedic surgeons in their clinical decision-making.

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The rise and fall of machine learning methods in biomedical research

Cited by 18 publications

References 10 publications

Artificial Intelligence in Pharmacoepidemiology: A Systematic Review. Part 1—Overview of Knowledge Discovery Techniques in Artificial Intelligence

Artificial Intelligence in Pharmacoepidemiology: A Systematic Review. Part 1—Overview of Knowledge Discovery Techniques in Artificial Intelligence

The ability to classify patients based on gene-expression data varies by algorithm and performance metric

Machine Learning for the Orthopaedic Surgeon

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