Applications of Machine Learning Using Electronic Medical Records in Spine Surgery

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.

[1]  J. Denny,et al.  Artificial intelligence, bias and clinical safety , 2019, BMJ Quality & Safety.

[2]  Samuel Kadoury,et al.  Three-dimensional morphology study of surgical adolescent idiopathic scoliosis patient from encoded geometric models , 2016, European Spine Journal.

[3]  Ganesh Chandra Deka,et al.  History and Evolution of GPU Architecture , 2016 .

[4]  Tito Bassani,et al.  Artificial intelligence and machine learning in spine research , 2019, JOR spine.

[5]  A. Ahmed,et al.  External Validation of the SORG 90-day and One-Year Machine Learning Algorithms for Survival in Spinal Metastatic Disease. , 2019, The spine journal : official journal of the North American Spine Society.

[6]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[7]  F. Cabitza,et al.  The proof of the pudding: in praise of a culture of real-world validation for medical artificial intelligence. , 2019, Annals of translational medicine.

[8]  H. Haenssle,et al.  Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists , 2018, Annals of oncology : official journal of the European Society for Medical Oncology.

[9]  Julia Adler-Milstein,et al.  Electronic health record adoption in US hospitals: the emergence of a digital “advanced use” divide , 2017, J. Am. Medical Informatics Assoc..

[10]  Kenji Suzuki,et al.  Overview of deep learning in medical imaging , 2017, Radiological Physics and Technology.

[11]  Aly A. Valliani,et al.  Automated Measurement of Lumbar Lordosis on Radiographs Using Machine Learning and Computer Vision , 2019, Global spine journal.

[12]  Corinne Cath.  Governing artificial intelligence: ethical, legal and technical opportunities and challenges , 2018, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[13]  David Sussillo,et al.  Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks , 2013, Neural Computation.

[14]  Qin Zhang,et al.  Extracting comprehensive clinical information for breast cancer using deep learning methods , 2019, Int. J. Medical Informatics.

[15]  Sean D. Rundell,et al.  Comparison of Natural Language Processing Rules-based and Machine-learning Systems to Identify Lumbar Spine Imaging Findings Related to Low Back Pain. , 2018, Academic radiology.

[16]  S. Burge,et al.  A Time-Motion Study of Primary Care Physicians' Work in the Electronic Health Record Era. , 2018, Family medicine.

[17]  Halil Kilicoglu,et al.  The role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs , 2015, J. Biomed. Informatics.

[18]  Yufeng Zhang,et al.  Computer-Aided Cobb Measurement Based on Automatic Detection of Vertebral Slopes Using Deep Neural Network , 2017, Int. J. Biomed. Imaging.

[19]  Tej D Azad,et al.  Building an electronic health record integrated quality of life outcomes registry for spine surgery. , 2016, Journal of neurosurgery. Spine.

[20]  S. Park,et al.  Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers , 2019, Korean journal of radiology.

[21]  Alan H. Daniels,et al.  Predictive Modeling for Blood Transfusion After Adult Spinal Deformity Surgery: A Tree-Based Machine Learning Approach , 2018, Spine.

[22]  M. McGirt,et al.  Prediction model for outcome after low-back surgery: individualized likelihood of complication, hospital readmission, return to work, and 12-month improvement in functional disability. , 2015, Neurosurgical focus.

[23]  S. Kurtz,et al.  Mortality following the diagnosis of a vertebral compression fracture in the Medicare population. , 2008, The Journal of bone and joint surgery. American volume.

[24]  Vladimir Papic,et al.  Return to Work After Lumbar Microdiscectomy - Personalizing Approach Through Predictive Modeling , 2016, pHealth.

[25]  Summer S. Han,et al.  A machine learning approach for predictive models of adverse events following spine surgery. , 2019, The spine journal : official journal of the North American Spine Society.

[26]  Shelli L Feder,et al.  Data Quality in Electronic Health Records Research: Quality Domains and Assessment Methods , 2018, Western journal of nursing research.

[27]  G. Corrado,et al.  End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography , 2019, Nature Medicine.

[28]  J. Wee,et al.  Effect of Lifestyle Modification Using a Smartphone Application on Obesity With Obstructive Sleep Apnea: A Short-term, Randomized Controlled Study , 2018, Clinical and experimental otorhinolaryngology.

[29]  A. Schoenfeld,et al.  Development of Machine Learning Algorithms for Prediction of 30-Day Mortality After Surgery for Spinal Metastasis. , 2018, Neurosurgery.

[30]  Shuo Li,et al.  Towards Automatic Report Generation in Spine Radiology Using Weakly Supervised Framework , 2018, MICCAI.

[31]  Eta S Berner,et al.  The wave has finally broken: now what? , 2013, Journal of the American Medical Informatics Association : JAMIA.

[32]  Hua Xu,et al.  A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries , 2011, J. Am. Medical Informatics Assoc..

[33]  Sunghwan Sohn,et al.  Use of Natural Language Processing Algorithms to Identify Common Data Elements in Operative Notes for Total Hip Arthroplasty. , 2019, The Journal of bone and joint surgery. American volume.

[34]  D. Wenger,et al.  Electronic Medical Record Adoption: The Effect on Efficiency, Completeness, and Accuracy in an Academic Orthopaedic Practice , 2017, Journal of pediatric orthopedics.

[35]  Michael Kelm,et al.  Automatic detection of lytic and blastic thoracolumbar spine metastases on computed tomography , 2013, European Radiology.

[36]  R S Evans,et al.  Electronic Health Records: Then, Now, and in the Future , 2016, Yearbook of Medical Informatics.

[37]  Zhigang Peng,et al.  Automated Vertebra Detection and Segmentation from the Whole Spine MR Images , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[38]  Justin K Scheer,et al.  Development of a preoperative predictive model for major complications following adult spinal deformity surgery. , 2017, Journal of neurosurgery. Spine.

[39]  Cristian Lorenz,et al.  Machine learning for automated 3-dimensional segmentation of the spine and suggested placement of pedicle screws based on intraoperative cone-beam computer tomography. , 2019, Journal of neurosurgery. Spine.

[40]  Dukyong Yoon,et al.  Rate of electronic health record adoption in South Korea: A nation-wide survey , 2017, Int. J. Medical Informatics.

[41]  Daniel Forsberg,et al.  Detection and Labeling of Vertebrae in MR Images Using Deep Learning with Clinical Annotations as Training Data , 2017, Journal of Digital Imaging.

[42]  Jan Barnsley,et al.  Measuring data reliability for preventive services in electronic medical records , 2012, BMC Health Services Research.

[43]  C. Rivers,et al.  Development of an unsupervised machine learning algorithm for the prognostication of walking ability in spinal cord injury patients. , 2020, The spine journal : official journal of the North American Spine Society.

[44]  Sander M. J. van Kuijk,et al.  External validation of a prediction model for surgical site infection after thoracolumbar spine surgery in a Western European cohort , 2018, Journal of Orthopaedic Surgery and Research.

[45]  Jun S. Kim,et al.  Examining the Ability of Artificial Neural Networks Machine Learning Models to Accurately Predict Complications Following Posterior Lumbar Spine Fusion , 2017, Spine.

[46]  I. Murdoch,et al.  How do paper and electronic records compare for completeness? A three centre study , 2018, Eye.

[47]  David Ledbetter,et al.  The Dependence of Machine Learning on Electronic Medical Record Quality , 2017, AMIA.

[48]  Christopher G Chute,et al.  Classification, Ontology, and Precision Medicine. , 2018, The New England journal of medicine.

[49]  Wei Chen,et al.  The utility of including pathology reports in improving the computational identification of patients , 2016, Journal of pathology informatics.

[50]  Anne E Carpenter,et al.  Opportunities and obstacles for deep learning in biology and medicine , 2017, bioRxiv.

[51]  Luca Antiga,et al.  Automatic differentiation in PyTorch , 2017 .

[52]  The Lancet Respiratory Medicine.  Opening the black box of machine learning. , 2018, The Lancet. Respiratory medicine.

[53]  Cheng Chen,et al.  Artificial Intelligence Applied to Osteoporosis: A Performance Comparison of Machine Learning Algorithms in Predicting Fragility Fractures From MRI Data , 2018, Journal of magnetic resonance imaging : JMRI.

[54]  C. Storlie,et al.  Can machine learning algorithms accurately predict discharge to nonhome facility and early unplanned readmissions following spinal fusion? Analysis of a national surgical registry. , 2019, Journal of neurosurgery. Spine.

[55]  Ahmed Hosny,et al.  Artificial intelligence in radiology , 2018, Nature Reviews Cancer.

[56]  Marcus A. Badgeley,et al.  Automated deep-neural-network surveillance of cranial images for acute neurologic events , 2018, Nature Medicine.

[57]  William B Gormley,et al.  Development of machine learning algorithms for prediction of discharge disposition after elective inpatient surgery for lumbar degenerative disc disorders. , 2018, Neurosurgical focus.

[58]  Marcus Hacker,et al.  Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis , 2018, Front. Phys..

[59]  Nikolas Lessmann,et al.  Iterative fully convolutional neural networks for automatic vertebra segmentation and identification , 2018, Medical Image Anal..

[60]  J. Chapman,et al.  Predicting surgical site infection after spine surgery: a validated model using a prospective surgical registry. , 2014, The spine journal : official journal of the North American Spine Society.

[61]  J. Schwab,et al.  Predicting nonroutine discharge after elective spine surgery: external validation of machine learning algorithms. , 2019, Journal of neurosurgery. Spine.

[62]  Alex Milinovich,et al.  Extracting and utilizing electronic health data from Epic for research. , 2018, Annals of translational medicine.

[63]  Michael Rowe,et al.  An Introduction to Machine Learning for Clinicians. , 2019, Academic medicine : journal of the Association of American Medical Colleges.

[64]  Eric W. Ford,et al.  Predicting the adoption of electronic health records by physicians: when will health care be paperless? , 2006, Journal of the American Medical Informatics Association : JAMIA.

[65]  J. Welton,et al.  Using a Data Quality Framework to Clean Data Extracted from the Electronic Health Record: A Case Study , 2016, EGEMS.

[66]  M. Kelly,et al.  Artificial Intelligence Based Hierarchical Clustering of Patient Types and Intervention Categories in Adult Spinal Deformity Surgery: Towards a New Classification Scheme that Predicts Quality and Value. , 2019, Spine.

[67]  I. Han,et al.  Machine Learning Model to Predict Osteoporotic Spine with Hounsfield Units on Lumbar Computed Tomography , 2019, Journal of Korean Neurosurgical Society.

[68]  G. H. Ha,et al.  Global Evolution of Research in Artificial Intelligence in Health and Medicine: A Bibliometric Study , 2019, Journal of clinical medicine.

[69]  Michael R. Macedonia,et al.  The GPU Enters Computing's Mainstream , 2003, Computer.

[70]  You Chen,et al.  Learning Bundled Care Opportunities from Electronic Medical Records , 2017, J. Biomed. Informatics.

[71]  Borim Ryu,et al.  Impact of an Electronic Health Record-Integrated Personal Health Record on Patient Participation in Health Care: Development and Randomized Controlled Trial of MyHealthKeeper , 2017, Journal of medical Internet research.

[72]  David Suendermann-Oeft,et al.  Medical Speech Recognition: Reaching Parity with Humans , 2017, SPECOM.

[73]  J. Mao,et al.  Development of Machine Learning Algorithms for Prediction of Prolonged Opioid Prescription after Surgery for Lumbar Disc Herniation. , 2019, The spine journal : official journal of the North American Spine Society.

[74]  D. Bates.  Commentary: the role of "technovigilance" in improving care in hospitals. , 2013, The Milbank quarterly.

[75]  Darco Lalevic,et al.  Automated Detection of Radiology Reports that Require Follow-up Imaging Using Natural Language Processing Feature Engineering and Machine Learning Classification , 2019, Journal of Digital Imaging.

[76]  Morgan Price,et al.  Assessing accuracy of an electronic provincial medication repository , 2012, BMC Medical Informatics and Decision Making.

[77]  Keechul Jung,et al.  GPU implementation of neural networks , 2004, Pattern Recognit..

[78]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[79]  Krzysztof J. Cios,et al.  Uniqueness of medical data mining , 2002, Artif. Intell. Medicine.

[80]  E. Hess,et al.  Electronic medical records and physician stress in primary care: results from the MEMO Study. , 2014, Journal of the American Medical Informatics Association : JAMIA.

[81]  S. Tamang,et al.  Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data , 2018, JAMA internal medicine.

[82]  Suman V. Ravuri,et al.  A Clinically Applicable Approach to Continuous Prediction of Future Acute Kidney Injury , 2019, Nature.

[83]  K. Luk,et al.  A Machine Learning-based Surface Electromyography Topography Evaluation for Prognostic Prediction of Functional Restoration Rehabilitation in Chronic Low Back Pain , 2017, Spine.

[84]  Navdeep Jaitly,et al.  Speech recognition for medical conversations , 2017, INTERSPEECH.

[85]  H. Gong,et al.  Prediction of lumbar vertebral strength of elderly men based on quantitative computed tomography images using machine learning , 2019, Osteoporosis International.