Applications of Machine Learning Using Electronic Medical Records in Spine Surgery

Schwartz, Jerome; Gao, Michael; Geng, Eric; Mody, Kush S.; Mikhail, Christopher; Cho, Samuel K.

doi:10.14245/ns.1938386.193

Cited by 33 publications

(26 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this regard, there is a limited degree to which clinical effects can be analyzed in terms of alignment factors alone. Various factors are involved in predicting clinical outcomes, and further research using recent big data technology (e.g., deep learning) is needed for a comprehensive analysis 22,26,29,59) . Patients with alignments suitable for laminoplasty can proceed with postoperative kyphotic alignment or postoperative K-line (-) according to LCL.…”

Section: Discussionmentioning

confidence: 99%

Preoperative Radiological Parameters to Predict Clinical and Radiological Outcomes after Laminoplasty

Lee

Son

Shin

et al. 2021

J Korean Neurosurg Soc

View full text Add to dashboard Cite

Many studies have focused on pre-operative sagittal alignment parameters which could predict poor clinical or radiological outcomes after laminoplasty. However, the influx of too many new factors causes confusion. This study reviewed sagittal alignment parameters, predictive of clinical or radiological outcomes, in the literature. Preoperative kyphotic alignment was initially proposed as a predictor of clinical outcomes. The clinical significance of the K-line and K-line variants also has been studied. Sagittal vertical axis, T1 slope (T1s), T1s-cervical lordosis (CL), anterolisthesis, local kyphosis, the longitudinal distance index, and range of motion were proposed to have relationships with clinical outcomes. The relationship between loss of cervical lordosis (LCL) and T1s has been widely studied, but controversy remains. Extension function, the ratio of CL to T1s (CL/T1s), and Sharma classification were recently proposed as LCL predictors. In predicting postoperative kyphosis, T1s cannot predict postoperative kyphosis, but a low CL/ T1s ratio was associated with postoperative kyphosis.

show abstract

Section: Discussionmentioning

confidence: 99%

Preoperative Radiological Parameters to Predict Clinical and Radiological Outcomes after Laminoplasty

Lee

Son

Shin

et al. 2021

J Korean Neurosurg Soc

View full text Add to dashboard Cite

show abstract

“…12 Schwartz and associates reported that machine learning may effectively harness the value of electronic medical records in spine surgery because of developments in algorithms in reading images and in the ability to predict clinical outcomes of patients. 13 McCoy and co-workers stated that targeted convolutional neural network training in SCI improves algorithm performance for this cohort and provides clinically relevant metrics of cord injury. 41 In future studies, we aim to address the following.…”

Section: Figmentioning

confidence: 99%

“…Also, the requirement for effective outcome prediction in patients with SCI has increased numerous research studies evaluating the efficacy of machine learning algorithms for this cohorts. [10][11][12][13][14] Among different machine learning systems, extreme gradient boosting (XGBoost) is widely used to accomplish state-of-the-art analyses in diverse fields with good accuracy or area under the receiver operating characteristic curve (AUC). 15,16 XGBoost, a decision-tree-based ensemble machine learning algorithm with a gradient boosting framework, was developed by Chen and Guestrin.…”

Section: Introductionmentioning

confidence: 99%

XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury

Inoue

Ichikawa

Ueno

et al. 2020

Neurotrauma Reports

View full text Add to dashboard Cite

The accurate prediction of neurological outcomes in patients with cervical spinal cord injury (SCI) is difficult because of heterogeneity in patient characteristics, treatment strategies, and radiographic findings. Although machine learning algorithms may increase the accuracy of outcome predictions in various fields, limited information is available on their efficacy in the management of SCI. We analyzed data from 165 patients with cervical SCI, and extracted important factors for predicting prognoses. Extreme gradient boosting (XGBoost) as a machine learning model was applied to assess the reliability of a machine learning algorithm to predict neurological outcomes compared with that of conventional methodology, such as a logistic regression or decision tree. We used regularly obtainable data as predictors, such as demographics, magnetic resonance variables, and treatment strategies. Predictive tools, including XGBoost, a logistic regression, and a decision tree, were applied to predict neurological improvements in the functional motor status (ASIA [American Spinal Injury Association] Impairment Scale [AIS] D and E) 6 months after injury. We evaluated predictive performance, including accuracy and the area under the receiver operating characteristic curve (AUC). Regarding predictions of neurological improvements in patients with cervical SCI, XGBoost had the highest accuracy (81.1%), followed by the logistic regression (80.6%) and the decision tree (78.8%). Regarding AUC, the logistic regression showed 0.877, followed by XGBoost (0.867) and the decision tree (0.753). XGBoost reliably predicted neurological alterations in patients with cervical SCI. The utilization of predictive machine learning algorithms may enhance personalized management choices through pre-treatment categorization of patients.

show abstract

“…In their narrative review, Schwartz et al 1 report on the utilization of EHRs in spine surgery through ML techniques. The authors are to be commended for their detailed description of data types commonly found in EHRs, learning concepts to generate structured data (such as NLP and machine vision), applications of ML for prognosis and prediction, and finally the challenges inherent to using unstructured data from EHRs in medical practice and research.…”

mentioning

confidence: 99%

“…Also, the article is not a systematic review, and as such, a number of contributions to the literature with relevance to the current discussion may not have been included. Nonetheless, the overview provided by Schwartz et al 1 enable a thorough examination of the current trends in ML applications to EHR utilization.…”

mentioning

confidence: 99%

Data Mining in Spine Surgery: Leveraging Electronic Health Records for Machine Learning and Clinical Research

Staartjes

Stienen

2019

Neurospine

View full text Add to dashboard Cite

Advances in natural language processing (NLP) and unsupervised learning have recently enabled the long-expected synergy between true "big data" and analytics based on machine learning (ML). The ability to reliably generate structured data from unstructured electronic health records (EHRs) such as free text reports, document scans, or unlabelled medical imaging has on the one hand allowed development of algorithms based on until recently unseen amounts of data, spanning entire hospital or even national populations, and not just databases compiled by human experts. On the other hand, the capacity to primarily generate structured reports from unstructured raw EHRs has proven valuable for hospital analytics, epidemiological studies, and systematic reviews.In their narrative review, Schwartz et al. 1 report on the utilization of EHRs in spine surgery through ML techniques. The authors are to be commended for their detailed description of data types commonly found in EHRs, learning concepts to generate structured data (such as NLP and machine vision), applications of ML for prognosis and prediction, and finally the challenges inherent to using unstructured data from EHRs in medical practice and research. As the authors show, there is no question that ML is already starting to affect surgical practice relevantly in many aspects. Especially the advent of open source algorithms provided by today's tech giants have largely democratized the development of ML models, and as the authors summarize, this has led to an explosion of publications reporting such algorithms. Still, it is important to preserve the methodological quality of papers utilizing ML techniques, which is often not the case. For example, the authors touch on the issue of ensuring generalizability through robust training structures (i.e., some form of resampling) and external validation, before models are rolled out into clinical practice.We especially value that the authors discuss the problem of uninterpretable "black box" models. 2 Currently, many groups are applying complex ML algorithms to relatively small patient samples and for relatively simple tasks. While this might lead to slight benefits in model performance, these complex models (such as deep neural networks for nonimaging applications) are often typical "black box" models with a total loss of the ability to explain what factors lead the algorithm to make a certain decision. Explicability is -unfortunately -often traded in for a small and likely irrelevant increase in model accuracy. Especially in Neurospine 2019;16(4):654-656.

show abstract

Applications of Machine Learning Using Electronic Medical Records in Spine Surgery

Cited by 33 publications

References 81 publications

Preoperative Radiological Parameters to Predict Clinical and Radiological Outcomes after Laminoplasty

Preoperative Radiological Parameters to Predict Clinical and Radiological Outcomes after Laminoplasty

XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury

Data Mining in Spine Surgery: Leveraging Electronic Health Records for Machine Learning and Clinical Research

Contact Info

Product

Resources

About