Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review

Senders, Joeky T.; Staples, Patrick; Karhade, Aditya V.; Zaki, Mark M.; Gormley, William B.; Broekman, Marike L D; Smith, Timothy R.; Arnaout, Omar

doi:10.1016/j.wneu.2017.09.149

Cited by 348 publications

(236 citation statements)

References 42 publications

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“…In contrast to regression‐based approaches, ML is rapidly emerging as a valuable tool for predicting surgical outcomes. For example, ML algorithms have been reported to significantly enhance prediction of outcomes following neurosurgery, when compared to logistic regression . ML has also been proposed to improve prediction of transplant outcomes.…”

Section: Introductionmentioning

confidence: 99%

“…For example, ML algorithms have been reported to significantly enhance prediction of outcomes following neurosurgery, when compared to logistic regression. 11 ML has also been proposed to improve prediction of transplant outcomes. Recently, several studies have attempted to improve HTx outcome prediction using ML techniques in adults or combined pediatric and adult populations.…”

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confidence: 99%

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Prediction of mortality following pediatric heart transplant using machine learning algorithms

Miller

Tumin

Cooper

et al. 2019

Pediatric Transplantation

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Background Optimizing transplant candidates’ priority for donor organs depends on the accurate assessment of post‐transplant outcomes. Due to the complexity of transplantation and the wide range of possible serious complications, recipient outcomes are difficult to predict accurately using conventional multivariable regression. Therefore, we evaluated the utility of 3 ML algorithms for predicting mortality after pediatric HTx. Methods We identified patients <18 years of age receiving HTx in 2006‐2015 in the UNOS Registry database. Mortality within 1, 3, or 5 years was predicted using classification and regression trees, RFs, and ANN. Each model was trained using cross‐validation, then validated in a separate testing set. Model performance was primarily evaluated by the area under the receiver operating characteristic (AUC) curve. Results The training set included 2802 patients, whereas 700 were included in the testing set. RF achieved the best fit to the training data with AUCs of 0.74, 0.68, and 0.64 for 1‐, 3‐, and 5‐year mortality, respectively, and performed best in the testing data, with AUCs of 0.72, 0.61, and 0.60, respectively. Nevertheless, sensitivity was poor across models (training: 0.22‐0.58; testing: 0.07‐0.49). Discussion ML algorithms demonstrated fair predictive utility in both training and testing data, but the sensitivity of these algorithms was generally poor. With the registry missing data on many determinants of long‐term survival, the ability of ML methods to predict mortality after pediatric HTx may be fundamentally limited.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Prediction of mortality following pediatric heart transplant using machine learning algorithms

Miller

Tumin

Cooper

et al. 2019

Pediatric Transplantation

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“…To summarize detailed information such as machine learning approaches, features, and results. [11] Machine learning for predicting neurosurgical outcome 30 Systematic review…”

Section: -2018mentioning

confidence: 99%

Topics and trends in artificial intelligence assisted human brain research

Chen

Cheng

et al. 2020

PLoS ONE

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Artificial intelligence (AI) assisted human brain research is a dynamic interdisciplinary field with great interest, rich literature, and huge diversity. The diversity in research topics and technologies keeps increasing along with the tremendous growth in application scope of AIassisted human brain research. A comprehensive understanding of this field is necessary to assess research efficacy, (re)allocate research resources, and conduct collaborations. This paper combines the structural topic modeling (STM) with the bibliometric analysis to automatically identify prominent research topics from the large-scale, unstructured text of AIassisted human brain research publications in the past decade. Analyses on topical trends, correlations, and clusters reveal distinct developmental trends of these topics, promising research orientations, and diverse topical distributions in influential countries/regions and research institutes. These findings help better understand scientific and technological AIassisted human brain research, provide insightful guidance for resource (re)allocation, and promote effective international collaborations.

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“…The algorithms, namely Trees (e.g., random forest), Support vector Machine, and neural networks, are modeling approaches that can be used with different types of learning tasks, including supervised, unsupervised, semisupervised, and reinforcement learning. We searched project titles, project abstracts, and project terms using the following keywords: 10,11 "artificial intelligence," "Bayesian learning," "boosting," "gradient boosting," "computational intelligence," "computer reasoning," "deep learning," "machine intelligence," "machine learning," "naive Bayes," "neural network," "neural networks," "networks analysis," "natural language processing," "support vector machines," "random forest," "computer vision systems," and "deep networks." Alternative versions of these keywords have been tested to ascertain if abstracts could be identified, and those found useful have been included in the final list of keywords.…”

Section: Study Sample and Search Strategymentioning

confidence: 99%

The National Institutes of Health funding for clinical research applying machine learning techniques in 2017

et al. 2020

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Machine learning (ML) techniques have become ubiquitous and indispensable for solving intricate problems in most disciplines. To determine the extent of funding for clinical research projects applying ML techniques by the National Institutes of Health (NIH) in 2017, we searched the NIH Research Portfolio Online Reporting Tools Expenditures and Results (RePORTER) system using relevant keywords. We identified 535 projects, which together received a total of $264 million, accounting for 2% of the NIH extramural budget for clinical research.npj Digital Medicine (2020) 3:13 ; https://doi.

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Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review

Cited by 348 publications

References 42 publications

Prediction of mortality following pediatric heart transplant using machine learning algorithms

Prediction of mortality following pediatric heart transplant using machine learning algorithms

Topics and trends in artificial intelligence assisted human brain research

The National Institutes of Health funding for clinical research applying machine learning techniques in 2017

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