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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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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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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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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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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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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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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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Application of Regularized Ranking and Collaborative Filtering in predictive Alarm algorithm for Nocturnal Hypoglycemia Prevention 9

Application of Regularized Ranking and Collaborative Filteri...

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9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Kriukova, Galyna Shvai, Nadiya Pereverzyev, Sergei V. Natl Univ Kyiv Mohyla Acad Fac Comp Sci Dept Math Skovorody Str 2 Kiev Ukraine Austrian Acad Sci Johann Radon Inst Computat & Appl Math Altenberger Str 69 Linz Austria

ISBN: (纸本)9781538606971

In this paper we propose a predictive alarm ranking algorithm for prevention of nocturnal hypoglycemia (NH) in patients with insulin-treated diabetes. We adapt collaborative filtering method for prognosis of risk of NH occurrence in diabetic patients. We illustrate the proposed algorithm and test it to quantify its accuracy with medical records obtained within the framework of the European FP7-funded project DIAdvisor. Results show that the algorithm with proper ranking and preprocessed data outperforms well-known NH-prediction approaches in terms of sensitivity, specificity and F1-score.

关键词： predictive algorithm regularization ranking collaborative filtering

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A predictive Application Offloading algorithm Using Small Datasets for Cloud Robotics

A Predictive Application Offloading Algorithm Using Small Da...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Penmetcha, Manoj Kannan, Shyam Sundar Min, Byung-Cheol Purdue Univ Smart Lab Dept Comp & Informat Technol W Lafayette IN 47907 USA

ISBN: (纸本)9781665442077

Many robotic applications that are critical for robot performance require immediate feedback, hence execution time is a critical concern. Furthermore, it is common that robots come with a fixed quantity of hardware resources;if an application requires more computational resources than the robot can accommodate, its onboard execution might be extended to a degree that degrades the robot's performance. Cloud computing, on the other hand, features on-demand computational resources;by enabling robots to leverage those resources, application execution time can be reduced. The key to enabling robot use of cloud computing is designing an efficient offloading algorithm that makes optimum use of the robot's onboard capabilities and also forms a quick consensus on when to offload without any prior knowledge or information about the application. In this paper, we propose a predictive algorithm to anticipate the time needed to execute an application for a given application data input size with the help of a small number of previous observations. To validate the algorithm, we train it on the previous N observations, which include independent (input data size) and dependent (execution time) variables. To understand how algorithm performance varies in terms of prediction accuracy and error, we tested various N values using linear regression and a mobile robot path planning application. From our experiments and analysis, we determined the algorithm to have acceptable error and prediction accuracy when N > 40.

关键词： Cloud robotics AWS robot navigation predictive algorithm linear regression dynamic application offloading

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