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Risk Stratification of Early-Stage Cervical Cancer with Intermediate-Risk Factors: Model Development and Validation Based on machine learning algorithm

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ONCOLOGIST 2021年第12期26卷 E2217-E2226页

作者： Chu, Ran Zhang, Yue Qiao, Xu Xie, Lin Chen, Wei Zhao, Ying Xu, Yintao Yuan, Zeng Liu, Xiaolin Yin, Aijun Wang, Zhiwen Zhang, Qing Yang, Xingsheng Su, Xuantao Kong, Beihua Song, Kun Shandong Univ Qilu Hosp Cheeloo Coll Med Dept Obstet & Gynecol Jinan Shandong Peoples R China Shandong Univ Sch Control Sci & Engn Jinan Shandong Peoples R China Jining 1 Peoples Hosp Dept Obstet & Gynecol Jining Shandong Peoples R China Shandong Univ Qilu Hosp Gynecol Oncol Key Lab Jinan Shandong Peoples R China

Background Adjuvant therapy for patients with cervical cancer (CC) with intermediate-risk factors remains controversial. The objectives of the present study are to assess the prognoses of patients with early-stage CC with pathological intermediate-risk factors and to provide a reference for adjuvant therapy choice. Materials and Methods This retrospective study included 481 patients with stage IB-IIA CC. Cox proportional hazards regression analysis, machine learning (ML) algorithms, Kaplan-Meier analysis, and the area under the receiver operating characteristic curve (AUC) were used to develop and validate prediction models for disease-free survival (DFS) and overall survival (OS). Results A total of 35 (7.3%) patients experienced recurrence, and 20 (4.2%) patients died. Two prediction models were built for DFS and OS using clinical information, including age, lymphovascular space invasion, stromal invasion, tumor size, and adjuvant treatment. Patients were divided into high-risk or low-risk groups according to the risk score cutoff value. The Kaplan-Meier analysis showed significant differences in DFS (p = .001) and OS (p = .011) between the two risk groups. In the traditional Sedlis criteria groups, there were no significant differences in DFS or OS (p > .05). In the ML-based validation, the best AUCs of DFS at 2 and 5 years were 0.69/0.69, and the best AUCs of OS at 2 and 5 years were 0.88/0.63. Conclusion Two prognostic assessment models were successfully established, and risk grouping stratified the prognostic risk of patients with CC with pathological intermediate-risk factors. Evaluation of long-term survival will be needed to corroborate these findings. Implications for Practice The Sedlis criteria are intermediate-risk factors used to guide postoperative adjuvant treatment in patients with cervical cancer. However, for patients meeting the Sedlis criteria, the choice of adjuvant therapy remains controversial. This study developed two prognostic models based

关键词： Cervical cancer Intermediate-risk factor Adjuvant therapy Sedlis criteria Prediction model machine learning algorithm

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Binary classification model based on machine learning algorithm for the DC serial arc detection in electric vehicle battery system

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IET POWER ELECTRONICS 2019年第1期12卷 112-119页

作者： Xia, Kun Guo, Haotian He, Sheng Yu, Wei Xu, Jingjun Dong, Hui Univ Shanghai Sci & Technol Elect Engn Dept 516 JunGong Rd Shanghai 200093 Peoples R China Hella Shanghai Elect Co Ltd R&D Ctr Shanghai 201201 Peoples R China

Direct current (DC) serial arc faults usually occur in the damaged insulation lines or line connections, which will cause serious accidents such as fires and explosions. With the rapid increase of electric vehicles, DC serial arc faults are more and more dangerous to battery system. Therefore, a binary classification model based on machine learning algorithm was proposed to detect DC serial arc faults effectively in this study. It was optimised according to the characteristic signals of the arc to be satisfied with different loads for higher detection accuracy and robustness. In the simulative experiments for the power system electric vehicle, while the loads changing to the motor, the resistor or the inverter, it will all reach a highly successful detection rate, respectively.

关键词： DC motors power engineering computing arcs (electric) learning (artificial intelligence) fault diagnosis pattern classification electric vehicles battery powered vehicles binary classification model machine learning algorithm DC serial arc detection electric vehicle battery system direct current serial arc faults damaged insulation lines line connections electric vehicles DC serial arc faults higher detection accuracy robustness power system electric vehicle

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A machine-learning algorithm integrating baseline serum proteomic signatures predicts exercise responsiveness in overweight males with prediabetes

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CELL REPORTS MEDICINE 2023年第2期4卷 100944-100944页

作者： Diaz-Canestro, Candela Chen, Jiarui Liu, Yan Han, Hao Wang, Yao Honore, Eric Lee, Chi-Ho Lam, Karen S. L. Tse, Michael Andrew Xu, Aimin Univ Hong Kong State Key Lab Pharmaceut Biotechnol Hong Kong Peoples R China Univ Hong Kong Dept Med Hong Kong Peoples R China Univ Cote Azur CNRS INSERM Inst Pharmacol Mol & CellulaireLabex ICST Valbonne France Univ Hong Kong Ctr Sports & Exercise Hong Kong Peoples R China Univ Hong Kong Dept Pharmacol & Pharm Hong Kong Peoples R China

The molecular transducers conferring the benefits of chronic exercise in diabetes prevention remain to be comprehensively investigated. Herein, serum proteomic profiling of 688 inflammatory and metabolic biomarkers in 36 medication-naive overweight and obese men with prediabetes reveals hundreds of exer-cise-responsive proteins modulated by 12-week high-intensity interval exercise training, including regula-tors of metabolism, cardiovascular system, inflammation, and apoptosis. Strong associations are found between proteins involved in gas tro-intestinal mucosal immunity and metabolic outcomes. Exercise -induced changes in trefoil factor 2 (TFF2) are associated with changes in insulin resistance and fasting in-sulin, whereas baseline levels of the pancreatic secretory granule membrane major glycoprotein GP2 are related to changes in fasting glucose and glucose tolerance. A hybrid set of 23 proteins including TFF2 are differentially altered in exercise responders and non-responders. Furthermore, a machine-learning al-gorithm integrating baseline proteomic signatures accurately predicts individualized metabolic respon-siveness to exercise training.

关键词： proteomics exercise metabolic outcomes prediabetes insulin resistance personalized medicine machine learning algorithm

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Single resistive sensor for selective detection of multiple VOCs employing SnO2 hollowspheres and machine learning algorithm: A proof of concept

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SENSORS AND ACTUATORS B-CHEMICAL 2020年 321卷 128484-128484页

作者： Acharyya, Snehanjan Jana, Biswabandhu Nag, Sudip Saha, Goutam Guha, Prasanta Kumar Indian Inst Technol Kharagpur Adv Technol Dev Ctr Kharagpur 721302 W Bengal India Indian Inst Technol Kharagpur Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India

Selective detection of harmful gasses and volatile organic compounds (VOCs) in the ambient has become a major challenge. Primarily, semiconducting metal-oxide based gas sensors sense various gases simultaneously, hence their selectivity is poor. This paper presents a single chemiresistive metal-oxide gas sensor for identification of multiple VOCs accurately by employing highly sensitive microstructure and machine learning tools. Tin oxide (SnO2) hollowspheres were taken as sensing material that were prepared through optimized hydrothermal route. Different characterizations were carried out to confirm the formation of desired morphology and structural features. The sensor device was fabricated by controlled drop cast technique over gold based interdigitated electrodes. The sensor showed remarkable response towards the target VOCs with high sensitivity and fast recovery time. Incorporation of machine learning algorithm on the obtained sensor data provided accurate identification of all the VOCs (best performance shown by random forest). In addition, the quantitative prediction of gas concentration was performed for each target gas using regression model. In comparison to e-noses (having array of sensors with different sensing material), a single chemiresistive metal-oxide sensor with proper machine learning tool is simple, economic, compact and easy to fabricate.

关键词： Indoor air quality Volatile organic compound Selectivity Chemiresistive gas sensor Tin oxide hollowspheres machine learning algorithm

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A New Approach for Surface Urban Heat Island Monitoring Based on machine learning algorithm and Spatiotemporal Fusion Model

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IEEE ACCESS 2020年 8卷 164268-164281页

作者： Yao, Yuan Chang, Cun Ndayisaba, Felix Wang, Shuang Chengdu Univ Sch Architecture & Civil Engn Chengdu 610106 Peoples R China Chengdu Univ Inst Higher Educ Sichuan Prov Key Lab Pattern Recognit & Intelligent Informat P Chengdu 610106 Peoples R China Chinese Acad Sci Xinjiang Inst Ecol & Geog State Key Lab Desert & Oasis Ecol Urumqi 830011 Peoples R China Univ Ghent Fac Biosci Engn B-9000 Ghent Belgium Protestant Inst Arts & Social Sci Dept Geog Huye Rwanda Xinjiang Agr Univ Sch Management Urumqi 830000 Peoples R China

Land surface temperature (LST) is an important indicator for assessing the surface urban heat island (SUHI) effect. This paper presents a novel approach to derive LST estimates by integrating machine learning algorithm and spatiotemporal fusion model at high spatial and temporal resolution. The spatial resolutions of Landsat TM and Landsat 8 LST data were first downscaled using random forest (RF) algorithm from 120 m and 100 m, respectively, to 30 m. The resultant LST data were fused with MODerate-resolution Imaging Spectroradiometer (MODIS) LST data, by means of the Flexible Spatiotemporal Data Fusion method (FSDAF), in order to generate high spatiotemporal resolution summer daytime LST data covering the center of Chengdu city in China. The proposed new method was used to estimate the spatiotemporal variations of the summer daytime SUHI from 2009 to 2018 over Chengdu city. Results show that: (1) RF performs way better than the classical downscaling algorithm-thermal sharpening algorithm (TsHARP) for LST, and produces higher accuracy for different land covers;(2) the fused high spatiotemporal resolution summer daytime LST values were evaluated with in situ LST obtained from Chengdu Meteorological Office and the final validation results indicated that the proposed method, in generating LST dataset, can provide more details of urban thermal environment and produce higher accuracy than the traditional FSDAF;(3) significantly increasing trends of summer daytime SUHI intensity (SUHII) in the study area were observed. SUHII increased from 2.78 degrees C in 2009 to 4.04 degrees C in 2018. The highest and lowest summer daytime LST estimates were recorded over impervious surface area (ISA) and waters, respectively.

关键词： Remote sensing Earth Artificial satellites Spatial resolution Land surface temperature Urban areas Spatiotemporal phenomena machine learning algorithm land surface temperature spatiotemporal fusion surface urban heat island (SUHI)

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Research and Analysis of machine learning algorithm in Artificial Intelligence

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Artificial Intelligence Advances 2020年第2期2卷 88-91页

作者： Yang Li Xueyuan Du Yiheng Xu University of Liverpool LiverpoolL693BXEngland Xi’an Jiaotong-Liverpool University SuzhouJiangsu215213China

This article firstly explains the concepts of artificial intelligence and algorithm separately,then determines the research status of artificial intelligence and machine learning in the background of the increasing popularity of artificial intelligence,and finally briefly describes the machine learning algorithm in the field of artificial intelligence,as well as puts forward appropriate development prospects,in order to provide theoretical reference for industry insider.

关键词： artificial intelligence machine learning algorithm application of science and technology

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Primary care clinician engagement in implementing a machine-learning algorithm for targeted screening of familial hypercholesterolemia

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AMERICAN JOURNAL OF PREVENTIVE CARDIOLOGY 2024年 19卷 100710页

作者： Kim, Kain Faruque, Samir C. Kulp, David Lam, Shivani Sperling, Laurence S. Eapen, Danny J. Emory Sch Med Atlanta GA 30306 USA Washington Univ Div Gen Med Sch Med St Louis MO 63110 USA Emory Univ Wayne Rollins Res Ctr Dept Biol Atlanta GA 30306 USA Emory Univ Hubert Dept Global Hlth Rollins Sch Publ Hlth Atlanta GA 30306 USA Emory Ctr Heart Dis Prevent Atlanta GA 30306 USA Emory Clin Cardiovasc Res Inst Atlanta GA 30306 USA Emory Sch Med Dept Med Atlanta GA 30306 USA

Objective: To assess the impact of a multi-pronged educational approach on the knowledge, attitudes, and behaviors regarding Familial Hypercholesterolemia (FH) management at a large academic medical center with the aim of empowering primary care clinicians (PCC) to diagnose and treat FH. Methods: A comprehensive educational program for PCCs on FH management was developed and piloted from July 2022 to March 2024. Components of our intervention included: 1. Implementation of a novel clinical decision support tool in the electronic medical record for FH management, 2. Development and dissemination of an interactive educational website focused on FH and its management, 3. Delivery of virtual instructional sessions to increase awareness of the tool, provide education on its use, and obtain support from institutional leadership, and 4. Direct outreach to a pilot subset of PCCs whose patients had been detected using the validated FIND FH (R) (R) machine learning algorithm. Participating clinicians were surveyed at baseline before the intervention and after the educational session. Results: 70 PCC consented to participate in the study with a survey completion rate of 79 % (n n = 55) and 42 % (n n = 23) for the baseline and follow-up surveys, respectively. Objective PCC knowledge scores improved from 40 to 65 % of responders correctly responding to at least 2/3rds of survey questions. Despite the fact that 87 % identified PCC's as most effective for early detection of FH, 100% of PCCs who received direct outreach chose to defer care to an outpatient cardiologist over pursuing workup in the primary care setting. Conclusion: Empowering PCCs in management of FH serves as a key strategy in addressing this underdiagnosed and undertreated potentially life-threatening condition. A systems-based approach to addressing these aims may include leveraging EMR-based clinical decision support models and cross-disciplinary educational partnerships with medical specialists.

关键词： Familial hypercholesterolemia machine learning algorithm Electronic health record

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machine learning algorithm Identifies Patients at High Risk for Early Complications After Intracranial Tumor Surgery: Registry-Based Cohort Study

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NEUROSURGERY 2019年第4期85卷 E756-E764页

作者： van Niftrik, Christiaan H. B. van der Wouden, Frank Staartjes, Victor E. Fierstra, Jorn Stienen, Martin N. Akeret, Kevin Seboek, Martina Fedele, Tommaso Sarnthein, Johannes Bozinov, Oliver Krayenbuehl, Niklaus Regli, Luca Serra, Carlo Univ Hosp Zurich Dept Neurosurg Frauenklin Str 10 CH-8091 Zurich Switzerland Univ Hosp Zurich Clin Neurosci Ctr Zurich Switzerland Univ Zurich Zurich Switzerland Univ Calif Los Angeles Dept Geog Los Angeles CA 90024 USA Northwestern Univ Kellogg Sch Management Management & Org Dept Evanston IL USA

INTRODUCTION: Reliable preoperative identification of patients at high risk for early postoperative complications occurring within 24 h (EPC) of intracranial tumor surgery can improve patient safety and postoperative management. Statistical analysis using machine learning algorithms may generate models that predict EPC better than conventional statistical methods. OBJECTIVE: To train such a model and to assess its predictive ability. METHODS: This cohort study included patients from an ongoing prospective patient registry at a single tertiary care center with an intracranial tumor that underwent elective neurosurgery between June 2015 and May 2017. EPC were categorized based on the Clavien-Dindo classification score. Conventional statistical methods and different machine learning algorithms were used to predict EPC using preoperatively available patient, clinical, and surgery-related variables. The performance of each model was derived from examining classification performance metrics on an out-of-sample test dataset. RESULTS: EPC occurred in 174 (26%) of 668 patients included in the analysis. Gradient boosting machine learning algorithms provided the model best predicting the probability of an EPC. The model scored an accuracy of 0.70 (confidence interval [CI] 0.59-0.79) with an area under the curve (AUC) of 0.73 and a sensitivity and specificity of 0.80 (CI 0.58-0.91) and 0.67 (CI 0.53-0.77) on the test set. The conventional statistical model showed inferior predictive power (test set: accuracy: 0.59 (CI 0.47-0.71);AUC: 0.64;sensitivity: 0.76 (CI 0.64-0.85);specificity: 0.53 (CI 0.41-0.64)). CONCLUSION: Using gradient boosting machine learning algorithms, it was possible to create a prediction model superior to conventional statistical methods. While conventional statistical methods favor patients' characteristics, we found the pathology and surgery-related (histology, anatomical localization, surgical access) variables to be better predictors of EPC.

关键词： machine learning algorithm Complication Brain tumor Neurosurgery Neurocritical care Prediction model

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Electrical Fault Detection Using machine learning algorithm For Centrifugal Water Pumps 19

Electrical Fault Detection Using Machine Learning Algorithm ...

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19th IEEE International Conference on Environment and Electrical Engineering (EEEIC) / 3rd IEEE Industrial and Commercial Power Systems Europe Conference (I and CPS Europe)

作者： Chakravarthy, Ranganatha Bharadwaj, Sai Charan Umashankar, S. Padmanaban, Sanjeevikumar Dutta, Nabanita Holm-Nielsen, Jens Bo VIT Vellore SELECT Dept Energy & Power Elect Vellore Tamil Nadu India Prince Sultan Univ Coll Engn Renewable Energy Lab Riyadh Saudi Arabia Aalborg Univ Dept Energy Technol Esbjerg Denmark

ISBN: (纸本)9781728106526

The most essential part of any living being is water. Humans utilize water for various purposes such as cooking, bathing, washing, drinking, cultivating, cleaning, power generation and so on. Water pumps are employed to facilitate easy access near the requirement. Pumps can be classified into many groups according to the method of fluid displacement they inculcate to transport the fluid. Various faults are associated with the working of the water pumps due to the way it is handled, environmental conditions, supply imbalance, poor power quality or due to any other mechanical failures. Hence an effective process to determine the different various faults so as to mitigate the damage is required. This paper analyses the various faults in the centrifugal water pumps driven by induction motors used in agriculture fields and proposes a new algorithm to effectively and efficiently identify the fault and classify it according to its category using machine learning algorithms.

关键词： Centrifugal pumps faults machine learning algorithm

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An Energy-Efficient Cmos Image Sensor With Embedded machine learning algorithm

An Energy-Efficient Cmos Image Sensor With Embedded Machine ...

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作者： Kyuseok Lee University of Michigan

学位级别：博士

In this thesis, an energy-efficient CMOS (Complementary MetalâOxide Semiconductor) image sensor for embedded machine-learning algorithms has been studied to provide low-power consumption, minimized hardware resources, and reduced data bandwidth in both digital and analog domains for power-limited applications. In power-limited applications, image sensors for embedded machine learning algorithms typically have these challenges to address: low-power operation from limited energy sources such as batteries or energy harvesting units, large hardware area due to complicated machine-learning algorithms, and high-data bandwidth due to video streaming images and large data movement for evaluating the machine-learning algorithms. This research focuses on developing the architectures, algorithm optimization, and associated electronic circuits for an energy-efficient CMOS image sensor with the embedded machine-learning algorithms. Three interdependent prototypes have been developed to solve major challenges: minimization of energy consumption and hardware resources while preserving a high degree of precision in machine-learning algorithm evaluation. Three prototypes have been fabricated and fully characterized to address these challenges. In the first chip, we implemented 2 bit spatial difference imaging, a customized look-up table (LUT) based gradient orientation assignment, and a cell-based supporting-vector-machine (SVM) to achieve both low-data bandwidth and higher area efficiency for a histogram-of-oriented-gradient (HOG)-based object detection. The proposed HOG-based object detection core operates with the 2D optic flow core to provide the vision-based navigation functionality for the nano-air-vehicle (NAV) application. The system operates at 244 pJ/pixel in 2D optic flow extraction mode and at 272.49 pJ/pixel in hybrid operation mode, respectively. The system achieved 75% reduction in memory size with proposed HOG feature extraction method and cell-based supporting-vec

关键词： CMOS image sensor machine learning algorithm Energy harvesting pixel Electrical Engineering Engineering

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