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machine learning algorithms as early diagnostic tools for pancreatic fistula following pancreaticoduodenectomy and guide drain removal: A retrospective cohort study

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INTERNATIONAL JOURNAL OF SURGERY 2022年 102卷 106638-106638页

作者： Shen, Ziyun Chen, Haoda Wang, Weishen Xu, Wei Zhou, Yiran Weng, Yuanchi Xu, Zhiwei Deng, Xiaxing Peng, Chenghong Lu, Xiongxiong Shen, Baiyong Shanghai Jiao Tong Univ Ruijin Hosp Pancreat Dis Ctr Dept Gen SurgSch Med Shanghai Peoples R China Shanghai Jiao Tong Univ Inst Translat Med Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Med Res Inst Pancreat Dis Shanghai Peoples R China State Key Lab Oncogenes & Related Genes Shanghai Peoples R China

Objective: Clinically relevant postoperative pancreatic fistula (CR-POPF) remains the major cause of morbidity following pancreaticoduodenectomy (PD). Several model score systems such as the Fistula Risk Score (FRS) have been developed to predict CR-POPF using preoperative and intraoperative data. machine learning (ML) algorithms are increasingly applied in the medical field and they could be used to assess the risk of CR-POPF, identify clinically meaningful data and guide drain removal. Methods: Data from consecutive patients who underwent PD between January 1, 2010 and March 31, 2021 at a single high-volume center was collected retrospectively in this study. Demographics, clinical features, intraoperative parameters, and laboratory values were used to conduct the ML model. Four different ML algorithms (CatBoost, lightGBM, XGBoost and Random Forest) were used to train this model with cross-validation. Results: A total of 2421 patients with 62 clinical parameters were enrolled in this ML model. The majority of patients (76.3%) underwent open PD while others underwent robot-assisted PD. CR-POPF occurred in 424 (17.5%) patients. The CatBoost algorithm outperformed other algorithms with a mean area under the receiver operating characteristic curve (AUC) of 0.81 (95% confidence interval: 0.80-0.82) from the 5-fold cross-validation procedure. In the test dataset, the CatBoost algorithm also achieved the best mean-AUC of 0.83. The most important value was mean drain fluid amylase (DFA) in the first seven postoperative days (POD). The performance of models that used only preoperative data and intraoperative data was marginally lower than that of models that used combined data. Conclusion: Our ML algorithms could be applied as early diagnostic tools for CR-POPF in patients who underwent PD. Such real-time clinical decision support tools can identify patients with a high risk of CR-POPF, help in developing the perioperative management plan and guide the optimal timing of dra

关键词： Pancreatic fistula Pancreaticoduodenectomy machine learning algorithms Drain fluid amylase Drain removal

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Comparing Estimation Achievements by Determining Ideal Training Iteration Numbers in Supervised machine learning algorithms 2

Comparing Estimation Achievements by Determining Ideal Train...

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2017 International Conference on Computer Science and Engineering (UBMK)

作者： Bekmezci, Suleyman Yigit, Tuncay Suleyman Demirel Univ Fen Bilimleri Enstitusu Bilgisayar Muhendisligi Anabilim Dali Isparta Turkey Suleyman Demirel Univ Muhendisl Fak Bilgisayar Muhendisligi Bolumu Isparta Turkey

ISBN: (纸本)9781538609309

Data analysis has become important with the ever-increasing and diversified data from the past to the present. Supervised machine learning algorithms have been one of the preferred methods with time because of the ability to produce fast and effective solutions in data analysis. One of the most important problems of supervised machine learning algorithms is the inability to achieve the ideal predicted values which are obtained by adversely affecting the test values of little or over training. Therefore, determining the ideal number of training iterations for each algorithm is important for algorithm estimation success. In this study, it has been tried to obtain the best results in the estimation performance with the help of an application being developed in determining the ideal training iteration numbers of the supervised machine learning algorithms on a sample dataset and the prediction performances of the algorithms are compared with each others.

关键词： machine learning algorithms prediction success iteraton

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Wheelchair posture classification based on POF pressure sensors and machine learning algorithms

Wheelchair posture classification based on POF pressure sens...

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IEEE Latin American Electron Devices Conference (LAEDC)

作者： Gonzalez-Cely, A. X. Bastos-Filho, T. Diaz, Camilo A. R. Univ Fed Espirito Santo Grad Program Elect Engn Av Fernando Ferrari 514 Vitoria ES Brazil

ISBN: (数字)9781665497671

ISBN: (纸本)9781665497671

A proof of concept of a wheelchair posture monitoring based on Polymer Optical Fiber (POF) pressure array system using machine learning algorithms was made. Twelve lines of sensors were implemented in a wheelchair and ten postures were analyzed using classification algorithms. The classification system used a CAR filter and a SVM algorithm leading to an accuracy of 99.52%, and a cross validation of 98.83%. The best cross validation was 99.16% using a kNN algorithm.

关键词： POF pressure sensor wheelchair posture classification machine learning algorithms

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Accuracy comparison of machine learning algorithms at various wear-locations for activity identification post stroke: A pilot analysis 43

Accuracy comparison of machine learning algorithms at variou...

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43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Veerubhotla, Akhila Ehrenberg, Naphtaly Ibironke, Oluwaseun Pilkar, Rakesh Kessler Fdn W Orange NJ 07052 USA Rutgers New Jersey Med Sch Newark NJ 07103 USA

ISBN: (纸本)9781728111797

Objective and accurate activity identification of physical activities in everyday life is an important aspect in assessing the impact of various post-stroke rehabilitation therapies and interventions. Since post-stroke hemiparesis affects gait and balance in individuals with stroke, activity identification algorithms that consider stroke-specific movement irregularities are needed. While wearable physical activity monitors provide the means to detect activities in the free-living, algorithms using their data are specific to the wear location of the device. This pilot study builds, validates, and compares three machine learning algorithms (linear support vector machine, Random Forest, and RUSBoosted trees) at three popular wear locations (wrist, waist, and ankle) to identify and accurately distinguish mobility-related activities (sitting, standing and walking) in individuals with chronic stroke. A total of 102 minutes of data from two lab visits of three-stroke participants was used to build the classifiers. A 5-fold cross-validation technique was used to validate and compare the accuracy of classifiers. RUSBoosted trees using data from waist and ankle activity monitors, with an accuracy of 99.1%, outperformed other classifiers in detecting three activities of interest.

关键词： Wrist Support vector machines Legged locomotion machine learning algorithms Medical treatment Biology Object recognition

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Monitoring Lung Mechanics during Mechanical Ventilation using machine learning algorithms 40

Monitoring Lung Mechanics during Mechanical Ventilation usin...

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40th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Hezarjaribi, Niloofar Dutta, Rabijit Xing, Tao Murdoch, Gordon K. Mazrouee, Sepideh Mortazavi, Bobak J. Ghasemzadeh, Hassan Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99164 USA Univ Idaho Dept Mech Engn Moscow ID 83843 USA Univ Idaho Dept Anim & Vet Sci Moscow ID 83843 USA Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA Texas A&M Univ Dept Comp Sci & Engn College Stn TX 77843 USA

ISBN: (纸本)9781538636466

Evaluation of lung mechanics is the primary component for designing lung protective optimal ventilation strategies. This paper presents a machine learning approach for bedside assessment of respiratory resistance (R) and compliance (C). We develop machine learning algorithms to track flow rate and airway pressure and estimate R and C continuously and in real-time. An experimental study is conducted, by connecting a pressure control ventilator to a test lung that simulates various R and C values, to gather sensor data for validation of the devised algorithms. We develop supervised learning algorithms based on decision tree, decision table, and Support Vector machine (SVM) techniques to predict R and C values. Our experimental results demonstrate that the proposed algorithms achieve 90.3%, 93.1%, and 63.9% accuracy in assessing respiratory R and C using decision table, decision tree, and SVM, respectively. These results along with our ability to estimate R and C with 99.4% accuracy using a linear regression model demonstrate the potential of the proposed approach for constructing a new generation of ventilation technologies that leverage novel computational models to control their underlying parameters for personalized healthcare and context-aware interventions.

关键词： Lung Ventilation machine learning algorithms Pressure control Feature extraction Diseases Real-time systems

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Performance Comparison of machine learning algorithms for Diagnosis of Cardiotocograms with Class Inequality 22

Performance Comparison of Machine Learning Algorithms for Di...

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22nd Telecommunications Forum Telfor (TELFOR)

作者： Stylios, Ioannis Chr. Vlachos, Vasileios Androulidakis, Iosif Univ Athens Dept Informat & Telecommun Athens 15784 Greece Technol Educ Inst Thessaly Dept Comp Sci & Engn Thessaly Greece Univ Ioannina GR-45110 Ioannina Greece

ISBN: (纸本)9781479961917

The objective of the present paper is to demonstrate the potential of Computational Intelligence in applications pertaining to the automatic identification categorisation of Cardiotocograms using machine learning algorithms and Artificial Neural Networks whose purpose is to distinguish between healthy or pathological cases leading to mortality during birth or fetal cerebral palsy. Interest is also placed on the performance of the machine learning algorithms and the comparison of the classifiers' results.

关键词： Artificial Neural Networks Cardiotocograms machine learning algorithms WEKA

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Enhanced Critical Congenital Cardiac Disease Screening by Combining Interpretable machine learning algorithms 43

Enhanced Critical Congenital Cardiac Disease Screening by Co...

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43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Lai, Zhengfeng Vadlaputi, Pranjali Tancredi, Daniel J. Garg, Meena Koppel, Robert, I Goodman, Mera Hogan, Whitnee Cresalia, Nicole Juergensen, Stephan Manalo, Erlinda Lakshminrusimha, Satyan Chuah, Chen-Nee Siefkes, Heather Univ Calif Davis Dept Elect & Comp Engn Davis CA 95616 USA Univ Calif Davis Dept Pediat Sacramento CA USA UCLA David Geffen Sch Med Los Angeles CA 90095 USA Cohen Childrens Med Ctr New York NY USA Univ Utah Hlth Salt Lake City UT USA Univ Calif San Francisco San Francisco CA 94143 USA Sutter Hlth Sacramento CA USA

ISBN: (纸本)9781728111797

Critical Congenital Heart Disease (CCHD) screening that only uses oxygen saturation (SpO2), measured by pulse oximetry, fails to detect an estimated 900 US newborns annually. The addition of other pulse oximetry features such as perfusion index (PIx), heart rate, pulse delay and photoplethysmography characteristics may improve detection of CCHD, especially those with systemic blood flow obstruction such as Coarctation of the Aorta (CoA). To comprehensively study the most relevant features associated with CCHD, we investigated interpretable machine learning (ML) algorithms by using Recursive Feature Elimination (RFE) to identify an optimal subset of features. We then incorporated the trained ML models into the current SpO2-alone screening algorithm. Our proposed enhanced CCHD screening system, which adds the ML model, improved sensitivity by approximately 10 percentage points compared to the current standard SpO2-alone method with minimal to no impact on specificity.

关键词： Pediatrics machine learning algorithms Sensitivity Pulse measurements machine learning Approximation algorithms Feature extraction

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Predictive Performance of machine learning algorithms Trained with Sparse Data

Predictive Performance of Machine Learning Algorithms Traine...

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IEEE Aerospace Conference (AeroConf)

作者： Dewey, H. Heath DeVries, Derek R. Northrop Grumman 9160 N Hwy 83 Corinne UT 84307 USA

ISBN: (纸本)9781728174365

In the field of prognostic health management of aerospace assets and fleets, accurate prediction of the future state of an asset based on current and historical data has always been a challenge. Several different methods of computing that future state are commonly utilized including physics-based simulation and machine learning algorithms. Physics-based simulations have the advantage of true predictability in that they are able to evolve a model of the system using first principles based on causality and predict future states that have yet to be observed on real assets, however they also have the disadvantage of being difficult, time-consuming, and expensive to construct. machine learning algorithms are the reverse: simple, fast, and cheap to construct but without true predictability since they can only fit input data to the data sets with which they were trained. For some aerospace fleets with a significant amount of historical degradation and failure data to train a model, machine learning algorithms provide very good predictions, but for other fleets with little historical degradation and failure data, machine learning may not provide good predictions of the future states of the assets. This paper explores the accuracy of the predictions made with machine learning algorithms when they are trained with sparse data with varying quality. An empirical study is performed where machine learning models are trained with data representing various historical data set types that are typically encountered in aerospace health management (e.g. low quality dense data, high quality sparse data, etc.), and the results from running those algorithms are compared with an idealized physics-based model. The predictive performance of the machine learning algorithms are quantified, and suggestions on when it is appropriate to utilize machine learning algorithms in aerospace health management systems are presented.

关键词： Degradation Rockets machine learning algorithms Computational modeling machine learning Predictive models Aerospace testing

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Analog/Mixed-Signal Circuit Synthesis Enabled by the Advancements of Circuit Architectures and machine learning algorithms 22

Analog/Mixed-Signal Circuit Synthesis Enabled by the Advance...

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27th Asia and South Pacific Design Automation Conference (ASP-DAC)

作者： Su, Shiyu Zhang, Qiaochu Hassanpourghadi, Mohsen Liu, Juzheng Rasul, Rezwan A. Chen, Mike Shuo-Wei Univ Southern Calif Ming Hsieh Dept Elect & Comp Engn Los Angeles CA 90089 USA

ISBN: (纸本)9781665421355

Analog mixed-signal (AMS) circuit architecture has evolved towards more digital friendly due to technology scaling and demand for higher flexibility/reconfigurability. Meanwhile, the design complexity and cost of AMS circuits has substantially increased due to the necessity of optimizing the circuit sizing, layout, and verification of a complex AMS circuit. On the other hand, machine learning (ML) algorithms have been under exponential growth over the past decade and actively exploited by the electronic design automation (EDA) community. This paper will identify the opportunities and challenges brought about by this trend and overview several emerging AMS design methodologies that are enabled by the recent evolution of AMS circuit architectures and machine learning algorithms. Specifically, we will focus on using neural-network-based surrogate models to expedite the circuit design parameter search and layout iterations. Lastly, we will demonstrate the rapid synthesis of several AMS circuit examples from specification to silicon prototype, with significantly reduced human intervention.

关键词： machine learning algorithms Design automation Costs Layout Prototypes machine learning Market research

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Methods and tools for automatic or semi-automatic recognition of selected emotions using machine learning algorithms

Methods and tools for automatic or semi-automatic recognitio...

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Progress in Applied Electrical Engineering (PAEE)

作者： Schneider, Piotr Mikolajewski, Dariusz Bryniarska, Anna Igras-Cybulska, Magdalena Cybulski, Artur Marcinowicz, Walery Janiszewski, Maciej Kawala-Sterniuk, Aleksandra Marie Curie Sklodowska Univ Inst Comp Sci Dept Neuroinformat & Biomed Engn Lublin Poland Hishoo Sp Zoo Gdynia Poland Kazimierz Wielki Univ Fac Comp Sci Bydgoszcz Poland Opole Univ Technol Fac Elect Engn Automat Control & Informat Opole Poland

ISBN: (纸本)9798331527570;9798331527563

Automatic or semi-automatic recognition of selected emotions using machine learning (ML) algorithms requires practical, fast and accurate implementation of the steps involved in acquiring the actual signals (e.g. speech, facial expression, posture and body movements), recording them in the form of an optimal feature vector/matrix, correctly interpreting the above data and reproducing and generalising this process within the ML system. Depending on the accuracy and speed required and the degree of its 'invasiveness' (e.g. by attaching sensors to the user's body), single or multi-modal interpretations of biometric signals associated with different groups of emotions are possible. This group of studies, despite their relative non-invasiveness, requires the caution prescribed by the Artificial Intelligence (AI) Act and RODO Act and the possibility of being replaced (if necessary) by alternatives. This article aims to analyse the opportunities and risks associated with this group of technologies, with a particular focus on directions for further development.

关键词： machine learning algorithms emotion recognition artificial intelligence automatic recognition

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