Development of machine learning algorithms for prediction of discharge disposition after elective inpatient surgery for lumbar degenerative disc disorders

Karhade, Aditya V.; Ogink, Paul T.; Thio, Quirina C. B. S.; Broekman, Marike; Cha, Thomas; Gormley, William B.; Hershman, Stuart H.; Peul, Wilco C.; Bono, Christopher M.; Schwab, Joseph H.

doi:10.3171/2018.8.focus18340

Cited by 80 publications

(60 citation statements)

References 26 publications

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“…[53][54][55][56][57] Other models have also been created using EMR data to predict physical disability, return to work, major complications, readmission rates, walking ability, need for inpatient rehab following spine surgery, discharge, and disposition. [58][59][60][61] More specific algorithms have been created to predict preoperative factors impacting survival, discharge, and readmission rates in patients following spine surgery for spinal metastasis. 56,62,63 While some of these studies have used the National Surgical Quality Improvement Program database and insurance databases in the past, similar algorithms could be produced for other indications using large EMR datasets.…”

Section: Clinical Prognosticationmentioning

confidence: 99%

“…66 Notably, many studies reviewed in this article used large publicly available databases rather than attempt to gather the same data from their own institutional EMR systems. 52,57,59,63,67 EMR systems do not follow standardized protocols for data storage and application programming interfaces, making it difficult to interface EMRs with other systems. 1 Not only does this impact data extraction, but it also affects clinical integration.…”

Section: Electronic Medical Record Systemsmentioning

confidence: 99%

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Applications of Machine Learning Using Electronic Medical Records in Spine Surgery

et al. 2019

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Developments in machine learning in recent years have precipitated a surge in research on the applications of artificial intelligence within medicine. Machine learning algorithms are beginning to impact medicine broadly, and the field of spine surgery is no exception. Electronic medical records are a key source of medical data that can be leveraged for the creation of clinically valuable machine learning algorithms. This review examines the current state of machine learning using electronic medical records as it applies to spine surgery. Studies across the electronic medical record data domains of imaging, text, and structured data are reviewed. Discussed applications include clinical prognostication, preoperative planning, diagnostics, and dynamic clinical assistance, among others. The limitations and future challenges for machine learning research using electronic medical records are also discussed.

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Section: Clinical Prognosticationmentioning

confidence: 99%

Section: Electronic Medical Record Systemsmentioning

confidence: 99%

Applications of Machine Learning Using Electronic Medical Records in Spine Surgery

et al. 2019

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“…Patients discharged to a setting other than home (i.e., nonroutine discharges) often experience a great-er length-of-stay and are associated with a greater economic burden than those who are discharged directly home. In the context of lumbar spine disease, Karhade et al 39 created a neural network predicting nonroutine discharge for patients undergoing surgery for lumbar degenerative disc disease. The neural network used a sample of over 26,000 patients from the National Surgical Quality Improvement Program database and extracted 8 key variables (e.g., age, body mass, comorbid disease status) to classify patients as either routine discharge or nonroutine discharge.…”

Section: Machine Learning Algorithms In Nontraumatic Spinal Cord Injurymentioning

confidence: 99%

“…To overcome this issue, applications such as Shiny can be used to create web-based tools that incorporate ML models. The development of these web-based tools (e.g., as seen in the study of Karhade et al [37][38][39] ) is a necessary step in allowing ML models to be more easily applicable in the clinical setting.…”

Section: Future Directions and Recommendationsmentioning

confidence: 99%

Predictive Modeling of Outcomes After Traumatic and Nontraumatic Spinal Cord Injury Using Machine Learning: Review of Current Progress and Future Directions

et al. 2019

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Machine learning represents a promising frontier in epidemiological research on spine surgery. It consists of a series of algorithms that determines relationships between data. Machine learning maintains numerous advantages over conventional regression techniques, such as a reduced requirement for a priori knowledge on predictors and better ability to manage large datasets. Current studies have made extensive strides in employing machine learning to a greater capacity in spinal cord injury (SCI). Analyses using machine learning algorithms have been done on both traumatic SCI and nontraumatic SCI, the latter of which typically represents degenerative spine disease resulting in spinal cord compression, such as degenerative cervical myelopathy. This article is a literature review of current studies published in traumatic and nontraumatic SCI that employ machine learning for the prediction of a host of outcomes. The studies described utilize machine learning in a variety of capacities, including imaging analysis and prediction in large epidemiological data sets. We discuss the performance of these machine learning-based clinical prognostic models relative to conventional statistical prediction models. Finally, we detail the future steps needed for machine learning to become a more common modality for statistical analysis in SCI.

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“…Developing an appropriate criterion in an interdisciplinary setting will eventually lead to avoid complications and reduce the nancial burden associated with it. Although studies have applied machine learning algorithm to predict postoperative outcomes using machine learning algorithms [30][31][32][33] . However, to date, a few studies focused on using logistic regression for developing and validating the machine learning algorithm 34,35 .…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms Using Logistic Regression for Predicting Neurosurgical Outcomes

Fatima¹

2020

Preprint

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Background: Preoperative prognostication of clinical and surgical outcome in patients with neurosurgical diseases can improve the risk stratification, thus can guide in implementing targeted treatment to minimize these events. Therefore, the author aims to highlight the development and validation of predictive models determining neurosurgical outcomes through machine learning algorithms using logistic regression.Methods: Logistic regression (enter, backward and forward) and least absolute shrinkage and selection operator (LASSO) method for selection of variables from selected database can eventually lead to multiple candidate models. The final model with a set of predictive variables must be selected based upon the clinical knowledge and numerical results.Results: The predictive model which performed best on the discrimination, calibration, Brier score and decision curve analysis must be selected to develop machine learning algorithms. Logistic regression should be compared with the LASSO model. Usually for the big databases, the predictive model selected through logistic regression gives higher Area Under the Curve (AUC) than those with LASSO model. The predictive probability derived from the best model could be uploaded to an open access web application which is easily deployed by the patients and surgeons to make a risk assessment world-wide.Conclusions: Machine learning algorithms provide promising results for the prediction of outcomes following cranial and spinal surgery. These algorithms can provide useful factors for patient-counselling, assessing peri-operative risk factors, and predicting post-operative outcomes after neurosurgery.

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Development of machine learning algorithms for prediction of discharge disposition after elective inpatient surgery for lumbar degenerative disc disorders

Cited by 80 publications

References 26 publications

Applications of Machine Learning Using Electronic Medical Records in Spine Surgery

Applications of Machine Learning Using Electronic Medical Records in Spine Surgery

Predictive Modeling of Outcomes After Traumatic and Nontraumatic Spinal Cord Injury Using Machine Learning: Review of Current Progress and Future Directions

Machine Learning Algorithms Using Logistic Regression for Predicting Neurosurgical Outcomes

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