146,043 results match your criteria artificial intelligence


[Review of Physiological Parameters Monitoring Technology in the ICU].

Zhongguo Yi Liao Qi Xie Za Zhi 2021 Nov;45(6):662-669

Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016.

Physiological parameters monitoring is essential to direct medical staff to evaluate, diagnose and treat critical patients quantitatively. ECG, blood pressure, SpO2, respiratory rate and body temperature are the basic vital signs of patients in the ICU. The measuring methods are relatively mature at present, and the trend is to be wireless and more accurate and comfortable. Read More

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November 2021

[Design and Implementation of Multifunctional Interactive Electronic Bedside Card System for Inpatients Based on Internet of Things Technology].

Zhongguo Yi Liao Qi Xie Za Zhi 2021 Nov;45(6):650-654

Department of Information Center, Fudan University Shanghai Cancer Center Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032.

This research is based on data from clinical information systems such as HIS, EMR, LIS, etc, based on the functions of the traditional paper bedside card, relying on wired network technology, using the Internet of Things technology to design and develop a multi-functional intelligent interactive electronic bedside card system for inpatients. The functional framework of the system is introduced and discussed in detail, and the design is carried out from several aspects of system architecture, network architecture, software architecture, database and software system. The results show that the system has stable performance and can ensure the real-time and accuracy of medical information. Read More

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November 2021

[Design and Implementation of Multifunctional Interactive Electronic Bedside Card System for Inpatients Based on Internet of Things Technology].

Zhongguo Yi Liao Qi Xie Za Zhi 2021 Nov;45(6):641-644

Department of Information Center, Fudan University Shanghai Cancer Center Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032.

This research is based on data from clinical information systems such as HIS, EMR, LIS, etc, based on the functions of the traditional paper bedside card, relying on wired network technology, using the Internet of Things technology to design and develop a multi-functional intelligent interactive electronic bedside card system for inpatients. The functional framework of the system is introduced and discussed in detail, and the design is carried out from several aspects of system architecture, network architecture, software architecture, database and software system. The results show that the system has stable performance and can ensure the real-time and accuracy of medical information. Read More

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November 2021

[Design and Implementation of Software Platform for AI-ECG Algorithm Research].

Zhongguo Yi Liao Qi Xie Za Zhi 2021 Nov;45(6):616-621

Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, 200433.

A software platform for AI-ECG algorithm research is designed and implemented to better serve the research of ECG artificial intelligence classification algorithm and to solve the problem of subjects data information management. Matlab R2019b and MySQL Sever 8.0 are used to design the software platform. Read More

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November 2021

XGBoost-based intelligence yield prediction and reaction factors analysis of amination reaction.

J Comput Chem 2021 Dec 4. Epub 2021 Dec 4.

College of Chemistry and Chemical Engineering, Henan University, Kaifeng, China.

Buchwald-Hartwig amination reaction catalyzed by palladium plays an important role in drug synthesis. In the last few years, machine learning-assisted strategies emerged and quickly gained attention. In this article, an importance and relevance-based integrated feature screening method is proposed to effectively filter high-dimensional feature descriptor data. Read More

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December 2021

A comprehensive review of machine learning techniques on diabetes detection.

Vis Comput Ind Biomed Art 2021 Dec 3;4(1):30. Epub 2021 Dec 3.

Department of Chemical Engineering, School of Technology, Pandit Deendayal Energy University, 382426, Gandhinagar, Gujarat, India.

Diabetes mellitus has been an increasing concern owing to its high morbidity, and the average age of individual affected by of individual affected by this disease has now decreased to mid-twenties. Given the high prevalence, it is necessary to address with this problem effectively. Many researchers and doctors have now developed detection techniques based on artificial intelligence to better approach problems that are missed due to human errors. Read More

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December 2021

Clinical Prediction Modeling in Intramedullary Spinal Tumor Surgery.

Acta Neurochir Suppl 2022 ;134:333-339

Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

Artificial intelligence is poised to influence various aspects of patient care, and neurosurgery is one of the most uprising fields where machine learning is being applied to provide surgeons with greater insight about the pathophysiology and prognosis of neurological conditions. This chapter provides a guide for clinicians on relevant aspects of machine learning and reviews selected application of these methods in intramedullary spinal cord tumors. The potential areas of application of machine learning extend far beyond the analyses of clinical data to include several areas of artificial intelligence, such as genomics and computer vision. Read More

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January 2022

Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction.

Acta Neurochir Suppl 2022 ;134:319-331

Department of Neurosurgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.

Machine learning (ML) is a rapidly rising research tool in biomedical sciences whose applications include segmentation, classification, disease detection, and outcome prediction. With respect to traditional statistical methods, ML algorithms have the potential to learn and improve their predictive performance when fed with large data sets without the need of being specifically programmed. In recent years, this technology has been increasingly applied for tackling clinical issues in intracranial aneurysm (IA) research. Read More

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January 2022

Artificial Intelligence in Adult Spinal Deformity.

Acta Neurochir Suppl 2022 ;134:313-318

Department of Orthopedic Surgery, Orthopedic Spine Center and Orthopedic Oncology Service, Massachusetts General Hospital, Boston, MA, USA.

Artificial Intelligence is gaining traction in medicine for its ease of use and advancements in technology. This study evaluates the current literature on the use of artificial intelligence in adult spinal deformity. Read More

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January 2022

At the Pulse of Time: Machine Vision in Retinal Videos.

Acta Neurochir Suppl 2022 ;134:303-311

Department of Ophthalmology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.

Spontaneous venous pulsations (SVP) are a common finding in healthy people. The absence of SVP is associated with rapid progression in glaucoma and increased intracranial pressure. Traditionally, SVP has been documented qualitatively by clinicians during biomicroscopy. Read More

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January 2022

Machine Learning in Pituitary Surgery.

Acta Neurochir Suppl 2022 ;134:291-301

Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.

Machine learning applications in neurosurgery are increasingly reported for diverse tasks such as faster and more accurate preoperative diagnosis, enhanced lesion characterization, as well as surgical outcome, complications and healthcare cost prediction. Even though the pertinent literature in pituitary surgery is less extensive with respect to other neurosurgical diseases, past research attempted to answer clinically relevant questions to better assist surgeons and clinicians. In the present chapter we review reported ML applications in pituitary surgery including differential diagnosis, preoperative lesion characterization (immunohistochemistry, cavernous sinus invasion, tumor consistency), surgical outcome and complication predictions (gross total resection, tumor recurrence, and endocrinological remission, cerebrospinal fluid leak, postoperative hyponatremia). Read More

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January 2022

Natural Language Processing Applications in the Clinical Neurosciences: A Machine Learning Augmented Systematic Review.

Acta Neurochir Suppl 2022 ;134:277-289

Neuroscience Institute, Virginia Mason Medical Center, Seattle, WA, USA.

Natural language processing (NLP), a domain of artificial intelligence (AI) that models human language, has been used in medicine to automate diagnostics, detect adverse events, support decision making and predict clinical outcomes. However, applications to the clinical neurosciences appear to be limited. NLP has matured with the implementation of deep transformer models (e. Read More

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January 2022

The Artificial Intelligence Doctor: Considerations for the Clinical Implementation of Ethical AI.

Acta Neurochir Suppl 2022 ;134:257-261

Neurosurgical Artificial Intelligence Laboratory Aachen (NAILA), RWTH Aachen University Hospital, Aachen, Germany.

The applications of artificial intelligence (AI) and machine learning (ML) in modern medicine are growing exponentially, and new developments are fast-paced. However, the lack of trust and appropriate legislation hinder its clinical implementation. Recently, there is a clear increase of directives and considerations on Ethical AI. Read More

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January 2022

Machine Learning and Ethics.

Acta Neurochir Suppl 2022 ;134:251-256

Department of Neurosurgery, Leiden University Medical Center, Leiden, Zuid-Holland, The Netherlands.

When new technology is introduced into healthcare, novel ethical dilemmas arise in the human-machine interface. As artificial intelligence (AI), machine learning (ML) and big data can exhaust human oversight and memory capacity, this will give rise to many of these new dilemmas.Technology has little if any ethical status but is inevitably interwoven with human activity and thus may serve to allow qualitative and quantitative disruption of human performance and interaction. Read More

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January 2022

Foundations of Time Series Analysis.

Acta Neurochir Suppl 2022 ;134:215-220

Department of Neurosurgery, Faculty of Medicine, RWTH Aachen University, Aachen, Germany.

For almost a century, classical statistical methods including exponential smoothing and autoregression integrated moving averages (ARIMA) have been predominant in the analysis of time series (TS) and in the pursuit of forecasting future events from historical data. TS are chronological sequences of observations, and TS data are therefore prevalent in many aspects of clinical medicine and academic neuroscience. With the rise of highly complex and nonlinear datasets, machine learning (ML) methods have become increasingly popular for prediction or pattern detection and within neurosciences, including neurosurgery. Read More

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January 2022

Foundations of Multiparametric Brain Tumour Imaging Characterisation Using Machine Learning.

Acta Neurochir Suppl 2022 ;134:183-193

Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia.

The heterogeneity of brain tumours at the molecular, metabolic and structural levels poses significant challenge for accurate tissue characterisation. Artificial intelligence and radiomics have emerged as valuable tools to analyse quantitative features extracted from medical images which capture the complex microenvironment of brain tumours. In particular, a number of computational tools including machine learning algorithms have been proposed for image preprocessing, tumour segmentation, feature extraction, classification, and prognostic stratifications as well. Read More

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January 2022

Foundations of Lesion Detection Using Machine Learning in Clinical Neuroimaging.

Acta Neurochir Suppl 2022 ;134:171-182

Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zürich, University of Zurich, Zurich, Switzerland.

This chapter describes technical considerations and current and future clinical applications of lesion detection using machine learning in the clinical setting. Lesion detection is central to neuroradiology and precedes all further processes which include but are not limited to lesion characterization, quantification, longitudinal disease assessment, prognosis, and prediction of treatment response. A number of machine learning algorithms focusing on lesion detection have been developed or are currently under development which may either support or extend the imaging process. Read More

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January 2022

Machine Learning-Based Radiomics in Neuro-Oncology.

Acta Neurochir Suppl 2022 ;134:139-151

Department of Neurosurgery, Faculty of Medicine, RWTH Aachen University, Aachen, Germany.

In the last decades, modern medicine has evolved into a data-centered discipline, generating massive amounts of granular high-dimensional data exceeding human comprehension. With improved computational methods, machine learning and artificial intelligence (AI) as tools for data processing and analysis are becoming more and more important. At the forefront of neuro-oncology and AI-research, the field of radiomics has emerged. Read More

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January 2022

Machine Learning Algorithms in Neuroimaging: An Overview.

Acta Neurochir Suppl 2022 ;134:125-138

Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.

Machine learning (ML) and artificial intelligence (AI) applications in the field of neuroimaging have been on the rise in recent years, and their clinical adoption is increasing worldwide. Deep learning (DL) is a field of ML that can be defined as a set of algorithms enabling a computer to be fed with raw data and progressively discover-through multiple layers of representation-more complex and abstract patterns in large data sets. The combination of ML and radiomics, namely the extraction of features from medical images, has proven valuable, too: Radiomic information can be used for enhanced image characterization and prognosis or outcome prediction. Read More

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January 2022

Introduction to Machine Learning in Neuroimaging.

Acta Neurochir Suppl 2022 ;134:121-124

Neurosurgical Artificial Intelligence Laboratory Aachen (NAILA), RWTH Aachen University Hospital, Aachen, Germany.

Advancements in neuroimaging and the availability of large-scale datasets enable the use of more sophisticated machine learning algorithms. In this chapter, we non-exhaustively discuss relevant analytical steps for the analysis of neuroimaging data using machine learning (ML), while the field of radiomics will be addressed separately (c.f. Read More

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January 2022

A Discussion of Machine Learning Approaches for Clinical Prediction Modeling.

Acta Neurochir Suppl 2022 ;134:65-73

Department of Neurosurgery, Stanford University, Stanford, CA, USA.

While machine learning has occupied a niche in clinical medicine for decades, continued method development and increased accessibility of medical data have led to broad diversification of approaches. These range from humble regression-based models to more complex artificial neural networks; yet, despite heterogeneity in foundational principles and architecture, the spectrum of machine learning approaches to clinical prediction modeling have invariably led to the development of algorithms advancing our ability to provide optimal care for our patients. In this chapter, we briefly review early machine learning approaches in medicine before delving into common approaches being applied for clinical prediction modeling today. Read More

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January 2022

Dimensionality Reduction: Foundations and Applications in Clinical Neuroscience.

Acta Neurochir Suppl 2022 ;134:59-63

Department of Neurosurgery, Faculty of Medicine, RWTH Aachen University, Aachen, Germany.

Advancements in population neuroscience are spurred by the availability of large scale, open datasets, such as the Human Connectome Project or recently introduced UK Biobank. With the increasing data availability, analyses of brain imaging data employ more and more sophisticated machine learning algorithms. However, all machine learning algorithms must balance generalization and complexity. Read More

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January 2022

Foundations of Feature Selection in Clinical Prediction Modeling.

Acta Neurochir Suppl 2022 ;134:51-57

Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.

Selecting a set of features to include in a clinical prediction model is not always a simple task. The goals of creating parsimonious models with low complexity while, at the same time, upholding predictive performance by explaining a large proportion of the variance within the dependent variable must be balanced. With this aim, one must consider the clinical setting and what data are readily available to clinicians at specific timepoints, as well as more obvious aspects such as the availability of computational power and size of the training dataset. Read More

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January 2022

Foundations of Machine Learning-Based Clinical Prediction Modeling: Part V-A Practical Approach to Regression Problems.

Acta Neurochir Suppl 2022 ;134:43-50

Neurosurgical Artificial Intelligence Laboratory Aachen (NAILA), Department of Neurosurgery, RWTH Aachen University Hospital, Aachen, Germany.

This chapter goes through the steps required to train and validate a simple, machine learning-based clinical prediction model for any continuous outcome. We supply fully structured code for the readers to download and execute in parallel to this section, as well as a simulated database of 10,000 glioblastoma patients who underwent microsurgery, and predict survival from diagnosis in months. We walk the reader through each step, including import, checking, splitting of data. Read More

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January 2022

Foundations of Machine Learning-Based Clinical Prediction Modeling: Part IV-A Practical Approach to Binary Classification Problems.

Acta Neurochir Suppl 2022 ;134:33-41

Neurosurgical Artificial Intelligence Laboratory Aachen (NAILA), Department of Neurosurgery, RWTH Aachen University Hospital, Aachen, Germany.

We illustrate the steps required to train and validate a simple, machine learning-based clinical prediction model for any binary outcome, such as, for example, the occurrence of a complication, in the statistical programming language R. To illustrate the methods applied, we supply a simulated database of 10,000 glioblastoma patients who underwent microsurgery, and predict the occurrence of 12-month survival. We walk the reader through each step, including import, checking, and splitting of datasets. Read More

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January 2022

Foundations of Machine Learning-Based Clinical Prediction Modeling: Part III-Model Evaluation and Other Points of Significance.

Acta Neurochir Suppl 2022 ;134:23-31

Neurosurgical Artificial Intelligence Laboratory Aachen (NAILA), Department of Neurosurgery, RWTH Aachen University Hospital, Aachen, Germany.

Various available metrics to describe model performance in terms of discrimination (area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 Score) and calibration (slope, intercept, Brier score, expected/observed ratio, Estimated Calibration Index, Hosmer-Lemeshow goodness-of-fit) are presented. Recalibration is introduced, with Platt scaling and Isotonic regression as proposed methods. We also discuss considerations regarding the sample size required for optimal training of clinical prediction models-explaining why low sample sizes lead to unstable models, and offering the common rule of thumb of at least ten patients per class per input feature, as well as some more nuanced approaches. Read More

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January 2022

Foundations of Machine Learning-Based Clinical Prediction Modeling: Part II-Generalization and Overfitting.

Acta Neurochir Suppl 2022 ;134:15-21

Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.

We review the concept of overfitting, which is a well-known concern within the machine learning community, but less established in the clinical community. Overfitted models may lead to inadequate conclusions that may wrongly or even harmfully shape clinical decision-making. Overfitting can be defined as the difference among discriminatory training and testing performance, while it is normal that out-of-sample performance is equal to or ever so slightly worse than training performance for any adequately fitted model, a massively worse out-of-sample performance suggests relevant overfitting. Read More

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January 2022

Foundations of Machine Learning-Based Clinical Prediction Modeling: Part I-Introduction and General Principles.

Acta Neurochir Suppl 2022 ;134:7-13

Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.

We provide explanations on the general principles of machine learning, as well as analytical steps required for successful machine learning-based predictive modeling, which is the focus of this series. In particular, we define the terms machine learning, artificial intelligence, as well as supervised and unsupervised learning, continuing by introducing optimization, thus, the minimization of an objective error function as the central dogma of machine learning. In addition, we discuss why it is important to separate predictive and explanatory modeling, and most importantly state that a prediction model should not be used to make inferences. Read More

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January 2022

Machine Intelligence in Clinical Neuroscience: Taming the Unchained Prometheus.

Acta Neurochir Suppl 2022 ;134:1-4

Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.

The democratization of machine learning (ML) through availability of open-source learning libraries, the availability of datasets in the "big data" era, increasing computing power even on mobile devices, and online training resources have both led to an explosion in applications and publications of ML in the clinical neurosciences, but has also enabled a dangerous amount of flawed analyses and cardinal methodological errors committed by benevolent authors. While powerful ML methods are nowadays available to almost anyone and can be applied after just few minutes of familiarizing oneself with these methods, that does not imply that one has mastered these techniques. This textbook for clinicians aims to demystify ML by illustrating its methodological foundations, as well as some specific applications throughout clinical neuroscience, and its limitations. Read More

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January 2022

The superior parietal lobule of macaque monkey: relative influence of gaze and static arm position during reaching.

eNeuro 2021 Dec 3. Epub 2021 Dec 3.

Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy.

The superior parietal lobule (SPL) integrates somatosensory, motor, and visual signals to dynamically control arm movements. During reaching, visual and gaze signals are used to guide the hand to the desired target location, while proprioceptive signals allow to correct arm trajectory, and keep the limb in the final position at the end of the movement. Three SPL areas are particularly involved in this process: V6A, PEc, PE. Read More

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December 2021