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A predictive model for identification of pediatric individuals with common variable immunodeficiency through electronic medical records

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Journal of Allergy and Clinical Immunology 2025年

作者： Alsaati, Nouf Penney, Chris Helbig, Ingo Sullivan, Kathleen E. Division of Allergy and Immunology Children's Hospital of Philadelphia Philadelphia Pa United States Division of Neurology Epilepsy NeuroGenetics Initiative (ENGIN) Children's Hospital of Philadelphia Philadelphia Pa United States Department of Biomedical and Health Informatics Children's Hospital of Philadelphia Philadelphia Pa United States Department of Neurology University of Pennsylvania Perelman School of Medicine Philadelphia Pa United States

Introduction: Common variable immunodeficiency (CVID) is characterized by recurrent sinopulmonary infections. However, in the pediatric population, recurrent sinopulmonary infections early in life are common, which can render key clinical features of CVID less distinctive. Accordingly, the diagnosis of CVID is often delayed owing to the heterogeneous nature of the presentation and the broad range of ages of onset. A 10-year lag in diagnosis has been found for CVID, and there is a critical need for improved time to diagnosis. Objective: Our aim was to utilize machine learning techniques to identify a clinical signature of CVID in a pediatric population. Methods: Our selected cohort included 112 individuals with CVID and 627 controls. The controls were restricted from having other medical conditions associated with infection. A machine learning data set was constructed by summing patient-level counts of clinical metrics. A total of 3 supervised machine learning classifiers were trained, tuned, and performance-tested. We validated our findings using a distinct control cohort with high medical complexity and tested a logistic regression approach. Results: Key features associated with CVID were chest radiograph count, number of antibiotic prescriptions, and number of common infections. Our Extreme Gradient Boosting (XGBoost) model best predicted eventual CVID diagnosis, with an F1 score of 0.77, a total of 21 of 29 CVID diagnoses classified correctly (8 false-negative results), and 179 of 183 patients without CVID correctly classified (4 false-positive results) up to 10 years before the eventual clinical diagnosis. Key features with a robust association with pediatric CVID were the frequency of common infections and antibiotic prescriptions. Conclusion: In spite of a high frequency of infections in the comparator population, the clinical signature of pediatric CVID was sufficiently distinctive to enable early identification. © 2025 American Academy of Allergy, Asthma & I

关键词： antibody deficiency artificial intelligence inborn error of immunity machine learning predictive algorithm Primary immunodeficiency

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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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Vehicle Collision Avoidance System Using Localization algorithm And predictive Analysis 6

Vehicle Collision Avoidance System Using Localization Algori...

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6th Annual Conference on Computational Science and Computational Intelligence (CSCI)

作者： John, Samuel Ndueso Noma-Osaghae, Etinosa Okokpujie, Kennedy Okereke, Chinonso Ananaba, Joshua Omoruyi, Osemwegie Nigeria Def Acad Dept Elect & Elect Engn Afaka Kaduna State Nigeria Covenant Univ Dept Elect & Informat Engn Ota Ogun State Nigeria

ISBN: (纸本)9781728155845

Road crashes account for over a million deaths around the world every year. It is one of the leading causes of death for young people between the ages of fifteen and twenty-ine. Road accidents cause a whooping loss of up to three percent of the many nations' Gross Domestic Product (GDP) and ninety percent of these accidents occur in low to middle income countries with a sizable fifty-four percent share of the world's vehicular population. One of the Sustainable Development Goals (SDG5) is the reduction of road accidents around the world by half of its current value by 2020. This goal becomes a hit if low to medium-income nations get safer roads. This paper proposes a collision avoidance system that provides drivers with an automated preemptive response to impending car accidents with the aid of distance predictive analysis via sensors connected to the braking system of the vehicle, which in turn slows down the speed of the vehicle or completely stops it from moving altogether. The proposed collision avoidance system makes use of ultrasonic sensors and a unique localization algorithm to deliver a largely user-based vehicular protection from collision.

关键词： wireless sensor network collision avoidance localization predictive algorithm

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A neonatal sepsis prediction algorithm using electronic medical record data from Mbarara Regional Referral Hospital

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Intelligence-Based Medicine 2025年 11卷 100198-100198页

作者： Ezeobi Dennis, Peace Musiimenta, Angella Wasswa, William Kyoyagala, Stella Mbarara University of Science and Technology (MUST) Uganda Mbarara Regional Referral Hospital (MRRH) Uganda

Introduction: Neonatal sepsis is a global challenge that contributes significantly to neonatal morbidity and mortality. The current diagnostic methods depend on conventional culture methods, a procedure that takes time and leads to delays in making timely treatment decisions. This study proposes a machine learning algorithm utilizing electronic medical record (EMR) data from Mbarara Regional Referral Hospital (MRRH) to enhance early detection and treatment of neonatal sepsis. Methods: We performed a retrospective study on a dataset of neonates hospitalized for at least 48 h in the Neonatal Intensive Care Unit (NICU) at MRRH between October 2015 to September 2019 who received at least one sepsis evaluation. 482 records of neonates met the inclusion criteria and the dataset comprises 38 neonatal sepsis screening parameters. The study considered two outcomes for sepsis evaluations: culture-positive if a blood culture was positive, and clinically positive if cultures were negative but antibiotics were administered for at least 120 h. We implemented k-fold cross-validation with k set to 10 to guarantee robust training and testing of the models. Seven machine learning models were trained to classify inputs as sepsis positive or negative, and their performance was compared with physician diagnoses. Results: The results of this study show that the proposed algorithm, combining maternal risk factors, neonatal clinical signs, and laboratory tests (the algorithm demonstrated a sensitivity and specificity of at least 95 %) outperformed the physician diagnosis (Sensitivity = 89 %, Specificity = 11 %). SVM model with radial basis function, polynomial kernels, and DT model (with the highest AUROC values of 98 %) performed better than the other models. Conclusions: The study shows that the combination of maternal risk factors, neonatal clinical signs, and laboratory tests can help improve the prediction of neonatal sepsis. Further research is warranted to assess the potential perfo

关键词： Early onset sepsis (EOS) Electronic medical record (EMR) Neonatal sepsis prediction predictive algorithm Screening parameters Supervised machine learning

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Non – Invasive, smartphone image-based screening for sickle cell disease at the point-of-need

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Heliyon 2025年第2期11卷 e41830页

作者： Vital, Eudorah F. LiCalzi, Meredith Haak Mannino, Robert G. McGann, Patrick T. Lam, Wilbur A. Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University Atlanta 30332 GA United States Sanguina Inc Atlanta 30092 GA United States Lifespan Comprehensive Sickle Cell Center Providence Rhode Island 02903 United States Alpert Medical School of Brown University Department of Pediatrics Providence Rhode Island 02903 United States Department of Pediatrics Division of Pediatric Hematology/Oncology Aflac Cancer Center and Blood Disorders Service of Children's Healthcare of Atlanta Emory University School of Medicine Atlanta 30322 GA United States Winship Cancer Institute of Emory University Atlanta 30322 GA United States Parker H. Petit Institute of Bioengineering and Biosciences Georgia Institute of Technology Atlanta 30332 GA United States Institute for Matter and Systems Georgia Institute of Technology Atlanta 30332 GA United States

Leveraging the increasing accessibility of smartphones in healthcare settings, we developed a smartphone app aimed at enhancing sickle cell disease (SCD) screening, particularly in resource-limited settings. Our application provides accurate and non-invasive SCD screening with instant results at the point-of-need. The app operates by analyzing patient fingernail images via a smartphone image to gauge anemia severity and by using targeted inquiries to identify SCD-related symptoms. These inputs collectively generate an SCD IMage and Patient profile-based Likelihood Estimation (SIMPLE) score, which estimates disease probability. The accuracy of the score depends on two inputs: the patient's anemia status and their health survey responses. We tested the app on 485 pediatric patients at Children's Healthcare of Atlanta (CHOA), achieving an overall sensitivity of 74 % and specificity of 76 % in screening for SCD among children aged 6 months to 21 years. Notably, the app demonstrated enhanced performance in the target demographic, with 100 % sensitivity and 75 % specificity for screening SCD in children aged 6 months to 5 years. This cost-effective and scalable app efficiently pinpoints and stratifies individuals, particularly those who missed early screening, for formal screening programs. © 2025 The Author(s)

关键词： Non-invasive Point-of-need predictive algorithm Screening Sickle cell disease Smartphone app

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Fast predictive technique for reverse power detection in synchronous generator

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IET ELECTRIC POWER APPLICATIONS 2018年第4期12卷 508-517页

作者： Yaghobi, Hamid Semnan Univ Fac Elect & Comp Engn Semnan Iran

Reverse power relays are utilised to trip turbine generators to avoid prime mover damage and directional relay is most widely used as the main protection for these conditions. An intentional time delay is ordinarily utilised to overcome the possible maloperation of these relays. However, the intentional time delay to prevent maloperation is not an ideal solution. As this time delay increases the reverse power relay operation time, which means that the motoring action of the synchronous generator persist for a longer time, making the prime mover more vulnerable to active power drawn by the generator. This study proposes a new flux-based approach to detect reverse power condition in the synchronous generators. The proposed scheme uses the analysis of angular velocity and acceleration data that are calculated from the estimated magnetic flux at the machine stator terminals. The basic idea of the technique stems from the principle that the stator and rotor magnetic fluxes rotate together at synchronous speed and will not be affected by system disturbances for a short interval according to highly inductive characteristics of the synchronous machine. The main advantage of this predictive algorithm is its speed, security and sensitivity to detect the reverse power conditions.

关键词： prediction theory synchronous generators data analysis magnetic flux stators rotors fast predictive technique reverse power detection synchronous generator turbine generator prime mover damage directional relay reverse power relay operation time flux-based approach angular velocity data analysis acceleration data analysis magnetic flux estimation machine stator terminal stator rotor synchronous machine predictive algorithm security

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Robust cascade-free predictive speed control for PMSM drives based on Extended State Observer

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IET ELECTRIC POWER APPLICATIONS 2021年第2期15卷 214-230页

作者： He, Chun Hu, Jianhui Li, Yong Harbin Inst Technol Coll Elect & Elect Engn Harbin Peoples R China

The direct predictive speed controller (DPSC) deals with the prediction of the motor speed based on precise mechanical parameters. However, the mechanical parameters are dependent on the practical applications and may not match with their actual values, which leads to inaccurate prediction of the motor behaviour and deteriorates the performance of the predictive algorithm. A three-order Extended State Observer is designed for a permanent magnet synchronous motor to observe the unknown overall disturbance of the system and compensate the speed prediction error, which reduces the usage of the mechanical constants and improves the robustness of the system against parameter uncertainties, accurate mechanical parameters become unessential. To improve the current dynamics, by using proper weighting of the position angle observation error, along with the overall disturbance, a new current controller is incorporated in the control law of developed PSC strategy. The proposed strategy is evaluated through experimental results with a two-level voltage source inverter. Finally, the proposed work is experimentally compared with a predictive speed control, by considering several performance indices.

关键词： predictive algorithm two-level voltage source inverter DPSC current controller motor behaviour prediction synchronous motors direct predictive speed controller Control of electric power systems Current control predictive control robust cascade-free predictive speed control electric current control PMSM drives Stability in control theory control system synthesis Control system analysis and synthesis methods invertors mechanical constants position angle observation error parameter uncertainties motor speed Small and special purpose electric machines three-order extended state observer control law system robustness robust control unknown overall disturbance permanent magnet motors Synchronous machines machine control Optimal control permanent magnet synchronous motor precise mechanical parameters PSC strategy speed prediction error observers Simulation, modelling and identification

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Oculomics for sarcopenia prediction: a machine learning approach toward predictive, preventive, and personalized medicine

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EPMA JOURNAL 2022年第3期13卷 367-382页

作者： Kim, Bo Ram Yoo, Tae Keun Kim, Hong Kyu Ryu, Ik Hee Kim, Jin Kuk Lee, In Sik Kim, Jung Soo Shin, Dong-Hyeok Kim, Young-Sang Kim, Bom Taeck Hangil Eye Hosp Dept Ophthalmol Incheon South Korea B & VIIT Eye Ctr B2 GT Tower1317-23 Seocho Dong Seoul South Korea VISUWORKS Seoul South Korea Dankook Univ Dankook Univ Hosp Dept Ophthalmol Coll Med Cheonan South Korea Malgeun Seoul Family Clin Seoul South Korea CHA Univ CHA Bundang Med Ctr Dept Family Med Seongnam South Korea Ajou Univ Dept Family Practice & Community Hlth Sch Med Suwon 16499 Gyeonggi Do South Korea

Aims Sarcopenia is characterized by a gradual loss of skeletal muscle mass and strength with increased adverse outcomes. Recently, large-scale epidemiological studies have demonstrated a relationship between several chronic disorders and ocular pathological conditions using an oculomics approach. We hypothesized that sarcopenia can be predicted through eye examinations, without invasive tests or radiologic evaluations in the context of predictive, preventive, and personalized medicine (PPPM/3PM). Methods We analyzed data from the Korean National Health and Nutrition Examination Survey (KNHANES). The training set (80%, randomly selected from 2008 to 2010) data were used to construct the machine learning models. Internal (20%, randomly selected from 2008 to 2010) and external (from the KNHANES 2011) validation sets were used to assess the ability to predict sarcopenia. We included 8092 participants in the final dataset. Machine learning models (XGBoost) were trained on ophthalmological examinations and demographic factors to detect sarcopenia. Results In the exploratory analysis, decreased levator function (odds ratio [OR], 1.41;P value <0.001), cataracts (OR, 1.31;P value = 0.013), and age-related macular degeneration (OR, 1.38;P value = 0.026) were associated with an increased risk of sarcopenia in men. In women, an increased risk of sarcopenia was associated with blepharoptosis (OR, 1.23;P value = 0.038) and cataracts (OR, 1.29;P value = 0.010). The XGBoost technique showed areas under the receiver operating characteristic curves (AUCs) of 0.746 and 0.762 in men and women, respectively. The external validation achieved AUCs of 0.751 and 0.785 for men and women, respectively. For practical and fast hands-on experience with the predictive model for practitioners who may be willing to test the whole idea of sarcopenia prediction based on oculomics data, we developed a simple web-based calculator application (hutps://***/sarcopenia) to predict th

关键词： Oculomics predictive algorithm Marker patterns predictive preventive personalized medicine (PPPM/3PM), Sarcopenia Risk assessment predictive model for practitioners Ophthalmologic examination Demographic factors Machine learning Age-related macular degeneration Blepharoptosis Cataract

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predictive vector quantisation for image compression

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ELECTRONICS LETTERS 1998年第24期34卷 2325-2326页

作者： Tsai, JC Hsieh, CH Chinese Army Acad Dept Elect Engn Kaohsiung 830 Taiwan I Shou Univ Dept Informat Engn Kaohsiung 840 Taiwan

A new vector quantisation algorithm is presented which fully exploits the interblock correlation of an image. For an input block, a predicted block from the previously encoded data is searched. If the prediction is good enough, only a Bag code is sent to the receiver. The algorithm results in a significant reduction in bit rate with better picture quality, as compared to the conventional full-search VQ.

关键词： predictive algorithm picture quality Image and video coding flag code vector quantisation image coding prediction theory image compression bit rate data encoding interblock correlation

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Generalized Quantum Neural predictive Networks

Generalized Quantum Neural Predictive Networks

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27th Chinese Control Conference

作者： Nan Dongxiang Zhang Yunsheng Kunming Univ Sci & Technol Dept Mech & Elect Engn Kunming 650093 Yunnan Peoples R China Kunming Univ Sci & Technol Dept Automat & Informat Engn Kunming 650051 Yunnan Peoples R China

ISBN: (纸本)9787900719706

A nonlinear system such as prediction of coal and methane outbursts, mechanical faults diagnosis and so on, which has the character coupled, randomized and sudden changed to the system variants. It is a difficult problem to predict this kind of nonlinear system with using the accurate and effective approach. We proposed a novel generalized quantum neural predictive networks which can be solved this problem better. To construct the model of a nonlinear system, ANN-PID (artificial neural networks with proportional, integral and derivative) has good nonlinear property, so that it can be constructed the model of the nonlinear system according to the knowledge of history data. And then, according to the projected property, the model can be launched from generalized space to Hilbert space, so that the model with superposition quantum states can be developed. In the quantum mechanicalism, a special state which is so called predictive state can be inverted with more and more bight probability from the superposition stats of sample data, before its predictive results has been produced. We have constructed the model of mapping and a predictive algorithm for the nonlinear system, which can be realized the hidden relations between the system inputs and outputs. The results of calculation shows that generalized quantum neural predictive networks predicts the nonlinear system is effective and accurate.

关键词： Generalized quantum neural predictive network Nonlinear system Mapping model predictive algorithm

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