Predicting the occurrence of complications following corrective cervical deformity surgery: Analysis of a prospective multicenter database using predictive analytics

2019 ◽  
Vol 59 ◽  
pp. 155-161 ◽  
Author(s):  
Peter G. Passias ◽  
Cheongeun Oh ◽  
Samantha R. Horn ◽  
Han Jo Kim ◽  
D. Kojo Hamilton ◽  
...  
Neurosurgery ◽  
2017 ◽  
Vol 80 (3S) ◽  
pp. S70-S85 ◽  
Author(s):  
Justin S. Smith ◽  
Christopher I. Shaffrey ◽  
Shay Bess ◽  
Mohammed F. Shamji ◽  
Darrel Brodke ◽  
...  

Abstract BACKGROUND: Over the last several decades, significant advances have occurred in the assessment and management of spinal deformity. OBJECTIVE: The primary focus of this narrative review is on recent advances in adult thoracic, thoracolumbar, and lumbar deformities, with additional discussions of advances in cervical deformity and pediatric deformity. METHODS: A review of recent literature was conducted. RESULTS: Advances in adult thoracic, thoracolumbar, and lumbar deformities reviewed include the growing applications of stereoradiography, development of new radiographic measures and improved understanding of radiographic alignment objectives, increasingly sophisticated tools for radiographic analysis, strategies to reduce the occurrence of common complications, and advances in minimally invasive techniques. In addition, discussion is provided on the rapidly advancing applications of predictive analytics and outcomes assessments that are intended to improve the ability to predict risk and outcomes. Advances in the rapidly evolving field of cervical deformity focus on better understanding of how cervical alignment is impacted by thoracolumbar regional alignment and global alignment and how this can affect surgical planning. Discussion is also provided on initial progress toward development of a comprehensive cervical deformity classification system. Pediatric deformity assessment has been substantially improved with low radiation-based 3-D imaging, and promising clinical outcomes data are beginning to emerge on the use of growth-friendly implants. CONCLUSION: It is ultimately through the reviewed and other recent and ongoing advances that care for patients with spinal deformity will continue to evolve, enabling better informed treatment decisions, more meaningful patient counseling, reduced complications, and achievement of desired clinical outcomes.


2017 ◽  
Vol 17 (10) ◽  
pp. S242-S243
Author(s):  
Peter G. Passias ◽  
Cheongeun Oh ◽  
Samantha R. Horn ◽  
Jessica Lavery ◽  
Han Jo Kim ◽  
...  

Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.


TAPPI Journal ◽  
2019 ◽  
Vol 18 (11) ◽  
pp. 679-689
Author(s):  
CYDNEY RECHTIN ◽  
CHITTA RANJAN ◽  
ANTHONY LEWIS ◽  
BETH ANN ZARKO

Packaging manufacturers are challenged to achieve consistent strength targets and maximize production while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain robust and accurate in the face of changing machine conditions. These adaptive quality “soft sensors” allow for more informed, on-the-fly process changes; fast change detection; and process control optimization without requiring periodic model tuning. The use of predictive modeling in the paper industry has increased in recent years; however, little attention has been given to packaging finished quality. The use of machine learning to maintain prediction relevancy under everchanging machine conditions is novel. In this paper, we demonstrate the process of establishing real-time, adaptive quality predictions in an industry focused on reel-to-reel quality control, and we discuss the value created through the availability and use of real-time critical quality.


Controlling ◽  
2020 ◽  
Vol 32 (1) ◽  
pp. 58-64
Author(s):  
Daniel Schlatter ◽  
Christopher Stoll ◽  
Klaus Möller
Keyword(s):  

Trotz deutlicher technologischer Fortschritte wird Predictive Analytics in der Praxis noch immer nur selten für die finanzielle Prognose eingesetzt. Notwendig für eine erfolgreiche Anwendung ist ein ganzheitlicher Ansatz bei der Implementierung, der über die rein technisch „richtige“ Anwendung hinausgeht. Aus der Analyse verschiedener Implementierungsprojekte wurden daher die Erfolgsfaktoren für Predictive Analytics Projekte abgeleitet und in einem ganzheitlichen Konzept zusammengefasst. Damit können Verbesserungen in den Bereichen Prognosegenauigkeit, Ressourceneinsatz und Steuerungswirkung realisiert werden.


2018 ◽  
Vol 11 (2) ◽  
pp. 94-102 ◽  
Author(s):  
A. G. Filimonov ◽  
N. D. Chichirova ◽  
A. A. Chichirov ◽  
A. A. Filimonovа

Energy generation, along with other sectors of Russia’s economy, is on the cusp of the era of digital transformation. Modern IT solutions ensure the transition of industrial enterprises from automation and computerization, which used to be the targets of the second half of the last century, to digital enterprise concept 4.0. The international record of technological and structural solutions in digitization may be used in Russia’s energy sector to the full extent. Specifics of implementation of such systems in different countries are only determined by the level of economic development of each particular state and the attitude of public authorities as related to the necessity of creating conditions for implementation of the same. It is shown that a strong legislative framework is created in Russia for transition to the digital economy, with research and applied developments available that are up to the international level. The following digital economy elements may be used today at enterprises for production of electrical and thermal energy: — dealing with large amounts of data (including operations exercised via cloud services and distributed data bases); — development of small scale distributed generation and its dispatching; — implementation of smart elements in both electric power and heat supply networks; — development of production process automation systems, remote monitoring and predictive analytics; 3D-modeling of parts and elements; real time mathematic simulation with feedback in the form of control actions; — creating centres for analytical processing of statistic data and accounting in financial and economic activities with business analytics functions, with expansion of communication networks and computing capacities. Examples are presented for implementation of smart systems in energy production and distribution. It is stated in the paper that state-of art information technologies are currently being implemented in Russia, new unique digital transformation projects are being launched in major energy companies. Yet, what is required is large-scale and thorough digitization and controllable energy production system as a multi-factor business process will provide the optimum combination of efficient economic activities, reliability and safety of power supply.


MIS Quarterly ◽  
2016 ◽  
Vol 40 (4) ◽  
pp. 869-888 ◽  
Author(s):  
David Martens ◽  
◽  
Foster Provost ◽  
Jessica Clark ◽  
Enric Junqué de Fortuny ◽  
...  

Author(s):  
Yulia V. Paukova ◽  
◽  
Konstantin V. Popov ◽  

The present article considers the need to predict migration flows using Predictive Analytics. The Russian Federation is a center of migration activity. The modern world is changing rapidly. An effective migration policy requires effective monitoring of migration flows, assessing the current situation in our and other countries and forecasting migration processes. There are information systems in Russia that contain a wide range of information about foreign citizens and stateless persons that provide the requested information about specific foreign citizens, including grouping it on various grounds. However, it is not possible to analyze and predict it automatically using thousands of parameters. Special attention in Russia is paid to digitalization. Using information technologies (artificial intelligence, machine learning and big data analysis) to forecast migration flows in conditions of variability of future events will allow to take into account a number of events and most accurately predict the quantitative and so-called "qualitative" structure of arrivals. The received information will help to develop state policy and to take appropriate measures in the field of migration regulation. The authors come to the conclusion that it is necessary to amend existing legal acts in order to implement information technologies of Predictive Analytics into the practice of migration authorities.


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