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predictive algorithm for the regional spread of coronavirus infection across the Russian Federation

BMC MEDICAL INFORMATICS AND DECISION MAKING 2023年第1期23卷 1-19页

作者： Reshetnikov, Andrey Berdutin, Vitalii Zaporozhtsev, Alexander Romanov, Sergey Abaeva, Olga Prisyazhnaya, Nadezhda Vyatkina, Nadezhda Sechenov First Moscow State Med Univ Inst Social Sci Moscow Russia Fed Med & Biol Agcy Contract Dept Fed Budgetary Inst Healthcare Volga Dist Med Ctr Nizhnii Novgorod Russia Nizhnii Novgorod State Tech Univ Fed State Budgetary Educ Inst Higher Educ Dept Theoret & Appl Mech Nizhnii Novgorod Russia Sechenov First Moscow State Med Univ Dept Sociol Med Hlth Econ & Hlth Insurance Moscow Russia

BackgroundOutbreaks of infectious diseases are a complex phenomenon with many interacting factors. Regional health authorities need prognostic modeling of the epidemic *** these purposes, various mathematical algorithms can be used, which are a useful tool for studying the infections spread dynamics. Epidemiological models act as evaluation and prognosis models. The authors outlined the experience of developing a short-term predictive algorithm for the spread of the COVID-19 in the region of the Russian Federation based on the SIR model: Susceptible (vulnerable), Infected (infected), Recovered (recovered). The article describes in detail the methodology of a short-term predictive algorithm, including an assessment of the possibility of building a predictive model and the mathematical aspects of creating such forecast *** show that the predicted results (the mean square of the relative error of the number of infected and those who had recovered) were in agreement with the real-life situation: sigma(I) = 0.0129 and sigma(R) = 0.0058, *** present study shows that despite a large number of sophisticated modifications, each of which finds its scope, it is advisable to use a simple SIR model to quickly predict the spread of coronavirus infection. Its lower accuracy is fully compensated by the adaptive calibration of parameters based on monitoring the current situation with updating indicators in real-time.

关键词： COVID-19 Dynamics of viral disease Epidemiological model predictive algorithm

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predictive algorithm FOR SUSTAINABLE SYSTEM ARCHITECTURE

PREDICTIVE ALGORITHM FOR SUSTAINABLE SYSTEM ARCHITECTURE

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26th IEEE Annual Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Grebenyuk, Vladimir Ascent Syst Technol Sun Peaks BC Canada

ISBN: (纸本)9781479900336;9781479900312

Every system, however big or small, is always a part of a bigger system and in turn consists of a number of smaller subsystems. This is true for both technical and natural systems, including biological, ecological and social. The discipline of Systems Architecture teaches that to be successful, any complex technical system has to take into account this system hierarchy as well as interdependency and interaction between the components of the system on each level. The principles of Systems Architecture: modularity, cohesion, scalability, evolvability and others are universal, but there is hardly a more important and more challenging area where they need to be applied than the area of sustainability and sustainable development. In recent years, issues of pollution, global warming, rising energy costs, natural and human-induced disasters, and other environmental and economic problems are becoming of more concern than ever. Problems such as this can be solved only with a system approach to sustainable development. An interesting consequence of applying the principles of Systems Architecture to sustainable development is the conclusion that the system itself must be sustainable. This paper presents an approach to sustainable system architecture, using a predictive algorithm based on a specific example of a building-integrated energy control system.

关键词： sustainable system architecture predictive algorithm integrated energy control system high-performance building

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Development and evaluation of a predictive algorithm for unsatisfactory response among patients with pulmonary arterial hypertension using health insurance claims data

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CURRENT MEDICAL RESEARCH AND OPINION 2022年第6期38卷 1019-1030页

作者： Gauthier-Loiselle, Marjolaine Tsang, Yuen Lefebvre, Patrick Agron, Peter Royer, Jimmy Lynum, Karimah S. Bell Bennett, Lucas Panjabi, Sumeet Anal Grp Inc 1190 Ave Canadiens De MontrealSuite 1500 Montreal PQ H3B 0M7 Canada Janssen Sci Affairs Titusville NJ USA

Objective This study aimed to develop and validate a predictive algorithm for unsatisfactory response to initial pulmonary arterial hypertension (PAH) therapy using health insurance claims. Methods Adult patients with PAH initiated on a first PAH therapy (index date) were identified from Optum's de-identified Clinformatics Data Mart Database (1/1/2010-12/31/2019). A random survival forest algorithm was developed using patient-month data and predicted the "survival function" (i.e. risk of not having unsatisfactory response) over time. For each patient-month observation, risk factors were assessed in the 12 months prior. Unsatisfactory response was defined as the first instance of (1) new PAH therapy, (2) PAH-related hospitalization or emergency room visit, (3) lung transplant or atrial septostomy, (4) PAH-related death or (5) chronic oxygen therapy initiation. To facilitate use in clinical practice, a simplified risk score was also developed based on a linear combination of the most important risk factors identified in the algorithm. Results In total, 4781 patients were included (median age = 69.0 years;58.6% female). Over a median follow-up of 14.0 months, 3169 (66.3%) had an unsatisfactory response. The most important risk factors included in the algorithm were healthcare resource use (i.e. PAH-related outpatient visits, pulmonologist visits, cardiologist visits, all-cause hospitalizations), time since first PAH diagnosis, time since index date, Charlson Comorbidity Index, dyspnea, and age. predictive accuracy was good for the full algorithm (C-statistic: 0.732) but was slightly lower for the simplified risk score (C-statistic: 0.668). Conclusion The present claims-based algorithm performed well in predicting time to unsatisfactory response following initial PAH therapy.

关键词： Pulmonary arterial hypertension unsatisfactory response risk assessment machine learning predictive algorithm health-insurance claims combination therapy

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Observational study to calculate addictive risk to opioids: a validation study of a predictive algorithm to evaluate opioid use disorder

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PHARMACOGENOMICS & PERSONALIZED MEDICINE 2017年 10卷 187-195页

作者： Brenton, Ashley Richeimer, Steven Sharma, Maneesh Lee, Chee Kantorovich, Svetlana Blanchard, John Meshkin, Brian Proove Biosci 15326 Alton Pkwy Irvine CA 92618 USA Univ Southern Calif Keck Sch Med Los Angeles CA USA Univ Southern Calif Dept Anesthesiol Los Angeles CA USA Univ Southern Calif Dept Psychiat Los Angeles CA USA Intervent Pain Inst Baltimore MD USA

Background: Opioid abuse in chronic pain patients is a major public health issue, with rapidly increasing addiction rates and deaths from unintentional overdose more than quadrupling since 1999. Purpose: This study seeks to determine the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm incorporating phenotypic risk factors and neuroscience-associated single-nucleotide polymorphisms (SNPs). Patients and methods: The Proove Opioid Risk (POR) algorithm determines the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm incorporating phenotypic risk factors and neuroscience-associated SNPs. In a validation study with 258 subjects with diagnosed opioid use disorder (OUD) and 650 controls who reported using opioids, the POR successfully categorized patients at high and moderate risks of opioid misuse or abuse with 95.7% sensitivity. Regardless of changes in the prevalence of opioid misuse or abuse, the sensitivity of POR remained >95%. Conclusion: The POR correctly stratifies patients into low-, moderate-, and high-risk categories to appropriately identify patients at need for additional guidance, monitoring, or treatment changes.

关键词： opioid use disorder addiction personalized medicine pharmacogenetics genetic testing predictive algorithm

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Design and development adaptive optical system installed on small-aperture telescope with predictive algorithm 26

Design and development adaptive optical system installed on ...

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26th International Symposium on Atmospheric and Ocean Optics, Atmospheric Physics

作者： Bolbasova, L. A. Gritsuta, A. N. Lavrinov, V. V. Lukin, V. P. Kopylov, E. A. Selin, A. A. Soin, E. L. Russian Acad Sci VE Zuev Inst Atmospher Opt Siberian Branch 1 Academician Zuev Sq Tomsk 634055 Russia Tomsk State Univ 36 Lenin Str Tomsk 634050 Russia FSRC Crystallog & Photon RAS Svyatoozerskaya 1 Shatura 140700 Moscow Region Russia

ISBN: (纸本)9781510639423

The temporal error is the main problem for adaptive optical systems operating in the atmosphere. One way to solve this problem is to optimize the adaptive optics system by predictive control algorithms. In study the adaptive optical system installed on the small-aperture telescope with the predictive algorithm are developed. The predictive algorithm uses measurement of center gravity of light intensity at subapertures of the Shack-Hartmann wavefront sensor has been developed. In results it not depends on the type and design of the adaptive mirror. For implementation the Shack-Hartmann wavefront sensor measuring phase distortion, atmospheric turbulence, and transverse wind velocity are created. The design of the wavefront sensor allows replacement of the microlens array with different sizes, focal lengths and operated in wide range of phase aberrations. As a result, the adaptive optics system measure the level of optical atmospheric turbulence for replace the microlens array and it to operated in different turbulent atmospheric conditions.

关键词： adaptive optics atmospheric turbulence predictive algorithm

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Application of predictive algorithm in Head-Tracking System

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Journal of Beijing Institute of Technology 2002年第3期11卷 306-310页

作者：姜明李科杰李鑫北京理工大学机电工程学院北京100081 北京理工大学光电工程系北京100081

The worldwide research status of head tracking is introduced and the works made in the research of the predictive algorithm and in the exploration of the rule of the head tracking are set forth. A time delay model for the telerobotic scout system is built. In respect of eliminating error caused by time delay and making reasonable prediction to the data stream, many methods are experimented in order to realize the aim of real time tracking. The application of extrapolation algorithm and auto recursive algorithm in the orientation tracking is described in detail. These two algorithms are realized in Matlab environment. Through analysis of the curves generated by using these two predictive algorithms, an appropriate method was applied in the telerobotic scout system. The effect is satisfying.

关键词： teleoperation head tracking predictive algorithm

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Artificial intelligence to optimize water consumption in agriculture: A predictive algorithm-based irrigation management system

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 223卷

作者： Preite, Luca Vignali, Giuseppe Univ Parma Dept Engn & Architecture Parco Area Sci 181-A I-43124 Parma Italy

Water scarcity is a pressing global issue that needs to be faced. The United Nations highlights that only about 31 percent of the population is not characterized by water stress, meaning that the world's freshwater resources are unevenly distributed and unsustainably managed. This phenomenon is both natural and man-made, as the human footprint is linked to many disciplines, with the largest impact coming from the agricultural sector. Scientific literature highlights how 4.0 technologies are the key drivers to reduce this sector's impact on valuable resources, such as water and soil. Based on these premises, the proposed work aims at improving the irrigation management in agriculture by implementing a three-layer architecture system to optimize water consumption and prevent soil percolation, by avoiding situations where the soil moisture exceeds its capacity point. To achieve this, experimental activities allow the evaluation of the soil capacity point and thus the definition of a confidence interval to guide watering decisions. The latter interval, soil and environmental data, and three-day weather forecasts are aggregate to create a consistent dataset for training and testing three different machine learning algorithms based on a classification problem to predict the state of the irrigation network. As a result, the implemented multi-layer perceptron neural network, support vector machine, and k-neighbors classifier achieved an accuracy of nearly 99%. Despite this, the neural network produced superior decision region boundaries, resulting in fewer false predictions. A Monte Carlo simulation was then applied to evaluate the water and energy savings, which were up to 27 % and 57 %, respectively. In summary, the predictive algorithm-based irrigation management system is a cost-effective solution for optimizing water management in agriculture that it is truly scalable to any crop by assessing the appropriate soil capacity level.

关键词： Water management Precision agriculture Sustainable farming Machine learning predictive algorithm

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Control system with a non-parametric predictive algorithm for a high-speed rotating machine with magnetic bearings

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BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES 2021年第6期69卷

作者： Kurnyta-Mazurek, Paulina Szolc, Tomasz Henzel, Maciej Falkowski, Krzysztof Mil Univ Technol Fac Mechatron Armament & Aerosp Ul Gen Sylwestra Kaliskiego 2 PL-00908 Warsaw Poland Polish Acad Sci Inst Fundamental Technol Res Ul Adolfa Pawinskiego 5B PL-02106 Warsaw Poland

This paper deals with research on the magnetic bearing control systems for a high-speed rotating machine. Theoretical and experimental characteristics of the control systems with the model algorithmic control ( MAC) algorithm and the proportional-derivative (PD) algorithm are presented. The MAC algorithm is the non-parametric predictive control method that uses an impulse response model. A laboratory model of the rotor-bearing unit under study consists of two active radial magnetic bearings and one active axial (thrust) magnetic bearing. The control system of the rotor position in air gaps consists of the fast prototyping control unit with a signal processor, the input and output modules, power amplifiers, contactless eddy current sensors and the host PC with dedicated software. Rotor displacement and control current signals were registered during investigations using a data acquisition (DAQ) system. In addition, measurements were performed for various rotor speeds, control algorithms and disturbance signals generated by the control system. Finally, the obtained time histories were presented, analyzed and discussed in this paper.

关键词： magnetic bearing predictive algorithm high speed rotating machine

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Derivation and Validation of a predictive algorithm for Long-Term Care Admission or Death

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JOURNAL OF THE AMERICAN MEDICAL DIRECTORS ASSOCIATION 2021年第8期22卷 1658-+页

作者： Madrigal, Caroline Halladay, Christopher W. McConeghy, Kevin Correa, Natalie A. Cersonsky, Tess E. K. Strauss, Daniel Gravenstein, Stefan Besdine, Richard W. O'Toole, Thomas P. Rudolph, James L. Providence VA Med Ctr Ctr Innovat Long Term Serv & Supports 830 Chalkstone Ave Providence RI 02908 USA Brown Sch Publ Hlth Providence RI USA Brown Univ Warren Alpert Med Sch Providence RI 02912 USA

Objectives: Older veterans prefer to remain in their homes and communities as long as possible. Although targeted delivery of home- and community-based services for veterans might delay long-term care placement, often, access to these services is inconsistently organized or delayed. To aid in early recognition of veterans at high risk for long-term care placement or death, we developed and validated a predictive algorithm, "Choose Home." Design: A retrospective observational cohort analysis was used. Setting and Participants: Two cohorts of Veterans Health Administration (VHA;a large integrated health care system) users were assembled: Derivation (4.6 million) and Confirmation (4.7 million). The Derivation Cohort included Veterans Administration users from fiscal year 2013;the Confirmation Cohort included Veterans Administration users from fiscal year 2014. Methods: A total of 148 predictor variables, including demographics, comorbidities, and utilizations were selected using logistic regression to predict placement in a long-term care facility for >90 days or death within 2 years. Results: Veterans were predominantly male [92.8% (Derivation), 92.5% (Confirmation)] and older [61.7 +/- 15.5 (Derivation), 61.5 +/- 15.6 years (Confirmation)], with a high prevalence of comorbid conditions. Between the Derivation and Confirmation Cohorts, the areas under the receiver operating characteristic curves were found to be 0.80 [95% confidence interval (CI) 0.799, 0.802] and 0.80 (95% CI 0.800, 0.802), respectively, indicating good discrimination for determining at-risk veterans. Conclusions and Implications: We created a predictive algorithm that identifies veterans at highest risk for long-term institutionalization or death. This algorithm provides clinicians with information that can proactively inform clinical decision making and care coordination. This study provides the groundwork for future investigations on how home- and community-based services can target older adults a

关键词： Long-term care home-based primary care predictive algorithm veterans VA

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Validation Study of a predictive algorithm to Evaluate Opioid Use Disorder in a Primary Care Setting

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HEALTH SERVICES RESEARCH AND MANAGERIAL EPIDEMIOLOGY 2017年 4卷 2333392817717411-2333392817717411页

作者： Sharma, Maneesh Lee, Chee Kantorovich, Svetlana Tedtaotao, Maria Smith, Gregory A. Brenton, Ashley Good Samaritan Hosp Baltimore MD USA Proove Biosci Inc Irvine CA USA Zoe Family Care Lynn Haven FL USA Red Pill Med Inc Redondo Beach CA USA

Background: Opioid abuse in chronic pain patients is a major public health issue. Primary care providers are frequently the first to prescribe opioids to patients suffering from pain, yet do not always have the time or resources to adequately evaluate the risk of opioid use disorder (OUD). Purpose: This study seeks to determine the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm ("profile") incorporating phenotypic and, more uniquely, genotypic risk factors. Methods and Results: In a validation study with 452 participants diagnosed with OUD and 1237 controls, the algorithm successfully categorized patients at high and moderate risk of OUD with 91.8% sensitivity. Regardless of changes in the prevalence of OUD, sensitivity of the algorithm remained >90%. Conclusion: The algorithm correctly stratifies primary care patients into low-, moderate-, and high-risk categories to appropriately identify patients in need for additional guidance, monitoring, or treatment changes.

关键词： opioid use disorder addiction personalized medicine pharmacogenetics genetic testing predictive algorithm primary care precision medicine

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