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2.
Anesth Analg ; 130(5): 1157-1166, 2020 05.
Artigo em Inglês | MEDLINE | ID: mdl-32287123

RESUMO

BACKGROUND: Acute hypotensive episodes (AHE), defined as a drop in the mean arterial pressure (MAP) <65 mm Hg lasting at least 5 consecutive minutes, are among the most critical events in the intensive care unit (ICU). They are known to be associated with adverse outcome in critically ill patients. AHE prediction is of prime interest because it could allow for treatment adjustment to predict or shorten AHE. METHODS: The Super Learner (SL) algorithm is an ensemble machine-learning algorithm that we specifically trained to predict an AHE 10 minutes in advance. Potential predictors included age, sex, type of care unit, severity scores, and time-evolving characteristics such as mechanical ventilation, vasopressors, or sedation medication as well as features extracted from physiological signals: heart rate, pulse oximetry, and arterial blood pressure. The algorithm was trained on the Medical Information Mart for Intensive Care dataset (MIMIC II) database. Internal validation was based on the area under the receiver operating characteristic curve (AUROC) and the Brier score (BS). External validation was performed using an external dataset from Lariboisière hospital, Paris, France. RESULTS: Among 1151 patients included, 826 (72%) patients had at least 1 AHE during their ICU stay. Using 1 single random period per patient, the SL algorithm with Haar wavelets transform preprocessing was associated with an AUROC of 0.929 (95% confidence interval [CI], 0.899-0.958) and a BS of 0.08. Using all available periods for each patient, SL with Haar wavelets transform preprocessing was associated with an AUROC of 0.890 (95% CI, 0.886-0.895) and a BS of 0.11. In the external validation cohort, the AUROC reached 0.884 (95% CI, 0.775-0.993) with 1 random period per patient and 0.889 (0.768-1) with all available periods and BSs <0.1. CONCLUSIONS: The SL algorithm exhibits good performance for the prediction of an AHE 10 minutes ahead of time. It allows an efficient, robust, and rapid evaluation of the risk of hypotension that opens the way to routine use.


Assuntos
Algoritmos , Hospitalização/tendências , Hipotensão/diagnóstico , Unidades de Terapia Intensiva/tendências , Aprendizado de Máquina/tendências , Doença Aguda , Idoso , Estudos de Coortes , Feminino , Humanos , Hipotensão/fisiopatologia , Masculino , Pessoa de Meia-Idade , Valor Preditivo dos Testes
3.
Anesth Analg ; 130(5): 1201-1210, 2020 05.
Artigo em Inglês | MEDLINE | ID: mdl-32287127

RESUMO

BACKGROUND: Predictive analytics systems may improve perioperative care by enhancing preparation for, recognition of, and response to high-risk clinical events. Bradycardia is a fairly common and unpredictable clinical event with many causes; it may be benign or become associated with hypotension requiring aggressive treatment. Our aim was to build models to predict the occurrence of clinically significant intraoperative bradycardia at 3 time points during an operative course by utilizing available preoperative electronic medical record and intraoperative anesthesia information management system data. METHODS: The analyzed data include 62,182 scheduled noncardiac procedures performed at the University of Washington Medical Center between 2012 and 2017. The clinical event was defined as severe bradycardia (heart rate <50 beats per minute) followed by hypotension (mean arterial pressure <55 mm Hg) within a 10-minute window. We developed models to predict the presence of at least 1 event following 3 time points: induction of anesthesia (TP1), start of the procedure (TP2), and 30 minutes after the start of the procedure (TP3). Predictor variables were based on data available before each time point and included preoperative patient and procedure data (TP1), followed by intraoperative minute-to-minute patient monitor, ventilator, intravenous fluid, infusion, and bolus medication data (TP2 and TP3). Machine-learning and logistic regression models were developed, and their predictive abilities were evaluated using the area under the ROC curve (AUC). The contribution of the input variables to the models were evaluated. RESULTS: The number of events was 3498 (5.6%) after TP1, 2404 (3.9%) after TP2, and 1066 (1.7%) after TP3. Heart rate was the strongest predictor for events after TP1. Occurrence of a previous event, mean heart rate, and mean pulse rates before TP2 were the strongest predictor for events after TP2. Occurrence of a previous event, mean heart rate, mean pulse rates before TP2 (and their interaction), and 15-minute slopes in heart rate and blood pressure before TP2 were the strongest predictors for events after TP3. The best performing machine-learning models including all cases produced an AUC of 0.81 (TP1), 0.87 (TP2), and 0.89 (TP3) with positive predictive values of 0.30, 0.29, and 0.15 at 95% specificity, respectively. CONCLUSIONS: We developed models to predict unstable bradycardia leveraging preoperative and real-time intraoperative data. Our study demonstrates how predictive models may be utilized to predict clinical events across multiple time intervals, with a future goal of developing real-time, intraoperative, decision support.


Assuntos
Bradicardia/diagnóstico , Hipotensão/diagnóstico , Aprendizado de Máquina/tendências , Monitorização Intraoperatória/tendências , Bradicardia/fisiopatologia , Previsões , Humanos , Hipotensão/fisiopatologia , Monitorização Intraoperatória/métodos , Valor Preditivo dos Testes , Estudos Retrospectivos
4.
Plast Reconstr Surg ; 145(4): 1079-1086, 2020 04.
Artigo em Inglês | MEDLINE | ID: mdl-32221238

RESUMO

Advances in computer science and photography not only are pervasive but are also quantifiably influencing the practice of medicine. Recent progress in both software and hardware technology has translated into the design of advanced artificial neural networks: computer frameworks that can be thought of as algorithms modeled on the human brain. In practice, these networks have computational functions, including the autonomous generation of novel images and videos, frequently referred to as "deepfakes." The technological advances that have resulted in deepfakes are readily applicable to facets of plastic surgery, posing both benefits and harms to patients, providers, and future research. As a specialty, plastic surgery should recognize these concepts, appropriately discuss them, and take steps to prevent nefarious uses. The aim of this article is to highlight these emerging technologies and discuss their potential relevance to plastic surgery.


Assuntos
Aprendizado de Máquina/tendências , Fotografação/métodos , Procedimentos Cirúrgicos Reconstrutivos/métodos , Gravação em Vídeo/métodos , Previsões , Humanos , Fotografação/tendências , Procedimentos Cirúrgicos Reconstrutivos/tendências , Gravação em Vídeo/tendências
5.
Anesthesiology ; 132(2): 379-394, 2020 02.
Artigo em Inglês | MEDLINE | ID: mdl-31939856

RESUMO

Artificial intelligence has been advancing in fields including anesthesiology. This scoping review of the intersection of artificial intelligence and anesthesia research identified and summarized six themes of applications of artificial intelligence in anesthesiology: (1) depth of anesthesia monitoring, (2) control of anesthesia, (3) event and risk prediction, (4) ultrasound guidance, (5) pain management, and (6) operating room logistics. Based on papers identified in the review, several topics within artificial intelligence were described and summarized: (1) machine learning (including supervised, unsupervised, and reinforcement learning), (2) techniques in artificial intelligence (e.g., classical machine learning, neural networks and deep learning, Bayesian methods), and (3) major applied fields in artificial intelligence.The implications of artificial intelligence for the practicing anesthesiologist are discussed as are its limitations and the role of clinicians in further developing artificial intelligence for use in clinical care. Artificial intelligence has the potential to impact the practice of anesthesiology in aspects ranging from perioperative support to critical care delivery to outpatient pain management.


Assuntos
Anestesiologia/métodos , Inteligência Artificial , Monitorização Intraoperatória/métodos , Anestesiologia/tendências , Inteligência Artificial/tendências , Aprendizado Profundo/tendências , Humanos , Aprendizado de Máquina/tendências , Monitorização Intraoperatória/tendências , Redes Neurais de Computação
6.
Neural Netw ; 121: 1-9, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-31522020

RESUMO

Existing zero-shot learning (ZSL) models usually learn mappings between visual space and semantic space. However, few of them take the label information into account. Indirect Attribute Prediction (IAP) learns the posterior probability of each attribute by label space, but labels of seen and unseen classes are defined in different spaces, which is not suitable for Generalized ZSL (GZSL). We propose a Label-Activating Framework (LAF) for semantic-based classification. The purpose of the proposed framework is to activate the label space by learning mappings from vision and semantics to labels. In the training phase, the original label space made up of one-hot vectors is used as common space, on which visual features and semantic information are embedded. After the label space is activated, labels of unseen classes can be regarded as the linear combination of labels of seen classes. In this case, seen and unseen labels are defined in the same space, and the label space has specific meanings rather than only signs of each class. Doing so makes the activated label space become very discriminative, especially for GZSL, which is therefore more challenging and reasonable for real-world tasks. In addition, we develop a specific model based on the framework, which effectively mitigate the projection domain shift problem. Extensive experiments show our framework outperforms state-of-the-art methods and also its suitability for GZSL.


Assuntos
Aprendizado de Máquina , Semântica , Animais , Humanos , Aprendizado de Máquina/tendências
7.
Neural Netw ; 121: 161-168, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-31563699

RESUMO

User response prediction makes a crucial contribution to the rapid development of online advertising system and recommendation system. The importance of learning feature interactions has been emphasized by many works. Many deep models are proposed to automatically learn high-order feature interactions. Since most features in advertising systems and recommendation systems are high-dimensional sparse features, deep models usually learn a low-dimensional distributed representation for each feature in the bottom layer. Besides traditional fully-connected architectures, some new operations, such as convolutional operations and product operations, are proposed to learn feature interactions better. In these models, the representation is shared among different operations. However, the best representation for each operation may be different. In this paper, we propose a new neural model named Operation-aware Neural Networks (ONN) which learns different representations for different operations. Our experimental results on two large-scale real-world ad click/conversion datasets demonstrate that ONN consistently outperforms the state-of-the-art models in both offline-training environment and online-training environment.


Assuntos
Aprendizado Profundo , Redes Neurais de Computação , Aprendizado Profundo/tendências , Previsões , Aprendizado de Máquina/tendências
8.
J Nurs Care Qual ; 35(1): 27-33, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-31136529

RESUMO

BACKGROUND: Electronic health record-derived data and novel analytics, such as machine learning, offer promising approaches to identify high-risk patients and inform nursing practice. PURPOSE: The aim was to identify patients at risk for readmissions by applying a machine-learning technique, Classification and Regression Tree, to electronic health record data from our 300-bed hospital. METHODS: We conducted a retrospective analysis of 2165 clinical encounters from August to October 2017 using data from our health system's data store. Classification and Regression Tree was employed to determine patient profiles predicting 30-day readmission. RESULTS: The 30-day readmission rate was 11.2% (n = 242). Classification and Regression Tree analysis revealed highest risk for readmission among patients who visited the emergency department, had 9 or more comorbidities, were insured through Medicaid, and were 65 years of age and older. CONCLUSIONS: Leveraging information through the electronic health record and Classification and Regression Tree offers a useful way to identify high-risk patients. Findings from our algorithm may be used to improve the quality of nursing care delivery for patients at highest readmission risk.


Assuntos
Registros Eletrônicos de Saúde/estatística & dados numéricos , Aprendizado de Máquina/tendências , Cuidados de Enfermagem/métodos , Idoso , Análise de Dados , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Cuidados de Enfermagem/normas , Readmissão do Paciente , Estudos Retrospectivos , Fatores de Risco , Gestão de Riscos/métodos , Gestão de Riscos/tendências
9.
Khirurgiia (Mosk) ; (12): 91-99, 2019.
Artigo em Russo | MEDLINE | ID: mdl-31825348

RESUMO

Recently, more and more attention has been paid to the utility of artificial intelligence in medicine. Radiology differs from other medical specialties with its high digitalization, so most software developers operationalize this area of medicine. The primary condition for machine learning is met because medical diagnostic images have high reproducibility. Today, the most common anatomic area for computed tomography is the thorax, particularly with the widespread lung cancer screening programs using low-dose computed tomography. In this regard, the amount of information that needs to be processed by a radiologist is snowballing. Thus, automatic image analysis will allow more studies to be interpreted. This review is aimed at highlighting the possibilities of machine learning in the chest computed tomography.


Assuntos
Diagnóstico por Computador/tendências , Detecção Precoce de Câncer , Neoplasias Pulmonares/diagnóstico por imagem , Aprendizado de Máquina/tendências , Tórax/diagnóstico por imagem , Tomografia Computadorizada por Raios X/tendências , Detecção Precoce de Câncer/instrumentação , Detecção Precoce de Câncer/métodos , Previsões , Humanos , Reprodutibilidade dos Testes
10.
Bone Joint J ; 101-B(12): 1476-1478, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31786999

RESUMO

This annotation briefly reviews the history of artificial intelligence and machine learning in health care and orthopaedics, and considers the role it will have in the future, particularly with reference to statistical analyses involving large datasets. Cite this article: Bone Joint J 2019;101-B:1476-1478.


Assuntos
Inteligência Artificial/história , Regras de Decisão Clínica , Procedimentos Ortopédicos/história , Inteligência Artificial/tendências , Interpretação Estatística de Dados , Previsões , História do Século XX , Humanos , Aprendizado de Máquina/história , Aprendizado de Máquina/tendências , Procedimentos Ortopédicos/tendências , Prognóstico , Reino Unido , Estados Unidos
13.
Best Pract Res Clin Anaesthesiol ; 33(2): 189-197, 2019 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-31582098

RESUMO

Blood pressure is the main determinant of organ perfusion. Hypotension is common in patients having surgery and in critically ill patients. The severity and duration of hypotension are associated with hypoperfusion and organ dysfunction. Hypotension is mostly treated reactively after low blood pressure values have already occurred. However, prediction of hypotension before it becomes clinically apparent would allow the clinician to treat hypotension pre-emptively, thereby reducing the severity and duration of hypotension. Hypotension can now be predicted minutes before it actually occurs from the blood pressure waveform using machine-learning algorithms that can be trained to detect subtle changes in cardiovascular dynamics preceding clinically apparent hypotension. However, analyzing the complex cardiovascular system is a challenge because cardiovascular physiology is highly interdependent, works within complicated networks, and is influenced by compensatory mechanisms. Improved hemodynamic data collection and integration will be a key to improve current models and develop new hypotension prediction models.


Assuntos
Determinação da Pressão Arterial/métodos , Cuidados Críticos/métodos , Hipotensão/diagnóstico , Hipotensão/fisiopatologia , Assistência Perioperatória/métodos , Determinação da Pressão Arterial/tendências , Cuidados Críticos/tendências , Humanos , Aprendizado de Máquina/tendências , Assistência Perioperatória/tendências , Valor Preditivo dos Testes
14.
Int J Lab Hematol ; 41(6): 717-725, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31498973

RESUMO

Machine learning (ML) offers opportunities to advance pathological diagnosis, especially with increasing trends in digitalizing microscopic images. Diagnosing leukemia is time-consuming and challenging in many areas globally and there is a growing trend in utilizing ML techniques for its diagnosis. In this review, we aimed to describe the literature of ML utilization in the diagnosis of the four common types of leukemia: acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia (CLL), acute myeloid leukemia (AML), and chronic myelogenous leukemia (CML). Using a strict selection criterion, utilizing MeSH terminology and Boolean logic, an electronic search of MEDLINE and IEEE Xplore Digital Library was performed. The electronic search was complemented by handsearching of references of related studies and the top results of Google Scholar. The full texts of 58 articles were reviewed, out of which, 22 studies were included. The number of studies discussing ALL, AML, CLL, and CML was 12, 8, 3, and 1, respectively. No studies were prospectively applying algorithms in real-world scenarios. Majority of studies had small and homogenous samples and used supervised learning for classification tasks. 91% of the studies were performed after 2010, and 74% of the included studies applied ML algorithms to microscopic diagnosis of leukemia. The included studies illustrated the need to develop the field of ML research, including the transformation from solely designing algorithms to practically applying them clinically.


Assuntos
Leucemia/diagnóstico , Aprendizado de Máquina/tendências , Algoritmos , Humanos , Leucemia/patologia , Leucemia Linfocítica Crônica de Células B/diagnóstico , Leucemia Linfocítica Crônica de Células B/patologia , Leucemia Mielogênica Crônica BCR-ABL Positiva/diagnóstico , Leucemia Mielogênica Crônica BCR-ABL Positiva/patologia , Leucemia Mieloide Aguda/diagnóstico , Leucemia Mieloide Aguda/patologia , Leucemia-Linfoma Linfoblástico de Células Precursoras/diagnóstico , Leucemia-Linfoma Linfoblástico de Células Precursoras/patologia
16.
BMC Bioinformatics ; 20(1): 480, 2019 Sep 18.
Artigo em Inglês | MEDLINE | ID: mdl-31533612

RESUMO

BACKGROUND: MicroRNAs (miRNAs) are noncoding RNA molecules heavily involved in human tumors, in which few of them circulating the human body. Finding a tumor-associated signature of miRNA, that is, the minimum miRNA entities to be measured for discriminating both different types of cancer and normal tissues, is of utmost importance. Feature selection techniques applied in machine learning can help however they often provide naive or biased results. RESULTS: An ensemble feature selection strategy for miRNA signatures is proposed. miRNAs are chosen based on consensus on feature relevance from high-accuracy classifiers of different typologies. This methodology aims to identify signatures that are considerably more robust and reliable when used in clinically relevant prediction tasks. Using the proposed method, a 100-miRNA signature is identified in a dataset of 8023 samples, extracted from TCGA. When running eight-state-of-the-art classifiers along with the 100-miRNA signature against the original 1046 features, it could be detected that global accuracy differs only by 1.4%. Importantly, this 100-miRNA signature is sufficient to distinguish between tumor and normal tissues. The approach is then compared against other feature selection methods, such as UFS, RFE, EN, LASSO, Genetic Algorithms, and EFS-CLA. The proposed approach provides better accuracy when tested on a 10-fold cross-validation with different classifiers and it is applied to several GEO datasets across different platforms with some classifiers showing more than 90% classification accuracy, which proves its cross-platform applicability. CONCLUSIONS: The 100-miRNA signature is sufficiently stable to provide almost the same classification accuracy as the complete TCGA dataset, and it is further validated on several GEO datasets, across different types of cancer and platforms. Furthermore, a bibliographic analysis confirms that 77 out of the 100 miRNAs in the signature appear in lists of circulating miRNAs used in cancer studies, in stem-loop or mature-sequence form. The remaining 23 miRNAs offer potentially promising avenues for future research.


Assuntos
Aprendizado de Máquina/tendências , MicroRNAs/genética , Neoplasias/classificação , Humanos
17.
Dig Dis Sci ; 64(11): 3048-3058, 2019 11.
Artigo em Inglês | MEDLINE | ID: mdl-31471859

RESUMO

Traditionally, early esophageal cancer (i.e., cancer limited to the mucosa or superficial submucosa) was managed surgically; the gastroenterologist's role was primarily to diagnose the tumor. Over the last decade, advances in endoscopic imaging, ablation, and resection techniques have resulted in a paradigm shift-diagnosis, staging, treatment, and surveillance are within the endoscopist's domain. Yet, there are few reviews that provide a focused, evidence-based approach to early esophageal cancer, and highlight areas of controversy for practicing gastroenterologists. In this manuscript, we will discuss the following: (1) utility of novel endoscopic technologies to identify high-grade dysplasia and early esophageal cancer, (2) role of endoscopic resection and imaging to stage early esophageal cancer, (3) endoscopic therapies for early esophageal cancer, and (4) indications for surgical and multidisciplinary management.


Assuntos
Detecção Precoce de Câncer/tendências , Endoscopia Gastrointestinal/tendências , Neoplasias Esofágicas/diagnóstico por imagem , Gastroenterologistas/tendências , Aprendizado de Máquina/tendências , Detecção Precoce de Câncer/métodos , Endoscopia Gastrointestinal/métodos , Mucosa Esofágica/diagnóstico por imagem , Mucosa Esofágica/cirurgia , Neoplasias Esofágicas/cirurgia , Humanos , Estadiamento de Neoplasias/métodos , Estadiamento de Neoplasias/tendências
18.
Crit Care ; 23(1): 284, 2019 Aug 22.
Artigo em Inglês | MEDLINE | ID: mdl-31439010

RESUMO

BACKGROUND: Intensive care units (ICUs) face financial, bed management, and staffing constraints. Detailed data covering all aspects of patients' journeys into and through intensive care are now collected and stored in electronic health records: machine learning has been used to analyse such data in order to provide decision support to clinicians. METHODS: Systematic review of the applications of machine learning to routinely collected ICU data. Web of Science and MEDLINE databases were searched to identify candidate articles: those on image processing were excluded. The study aim, the type of machine learning used, the size of dataset analysed, whether and how the model was validated, and measures of predictive accuracy were extracted. RESULTS: Of 2450 papers identified, 258 fulfilled eligibility criteria. The most common study aims were predicting complications (77 papers [29.8% of studies]), predicting mortality (70 [27.1%]), improving prognostic models (43 [16.7%]), and classifying sub-populations (29 [11.2%]). Median sample size was 488 (IQR 108-4099): 41 studies analysed data on > 10,000 patients. Analyses focused on 169 (65.5%) papers that used machine learning to predict complications, mortality, length of stay, or improvement of health. Predictions were validated in 161 (95.2%) of these studies: the area under the ROC curve (AUC) was reported by 97 (60.2%) but only 10 (6.2%) validated predictions using independent data. The median AUC was 0.83 in studies of 1000-10,000 patients, rising to 0.94 in studies of > 100,000 patients. The most common machine learning methods were neural networks (72 studies [42.6%]), support vector machines (40 [23.7%]), and classification/decision trees (34 [20.1%]). Since 2015 (125 studies [48.4%]), the most common methods were support vector machines (37 studies [29.6%]) and random forests (29 [23.2%]). CONCLUSIONS: The rate of publication of studies using machine learning to analyse routinely collected ICU data is increasing rapidly. The sample sizes used in many published studies are too small to exploit the potential of these methods. Methodological and reporting guidelines are needed, particularly with regard to the choice of method and validation of predictions, to increase confidence in reported findings and aid in translating findings towards routine use in clinical practice.


Assuntos
Aprendizado de Máquina/normas , Adulto , Análise de Dados , Registros Eletrônicos de Saúde , Feminino , Humanos , Unidades de Terapia Intensiva/organização & administração , Aprendizado de Máquina/tendências , Masculino
19.
Nat Med ; 25(9): 1337-1340, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31427808

RESUMO

Interest in machine-learning applications within medicine has been growing, but few studies have progressed to deployment in patient care. We present a framework, context and ultimately guidelines for accelerating the translation of machine-learning-based interventions in health care. To be successful, translation will require a team of engaged stakeholders and a systematic process from beginning (problem formulation) to end (widespread deployment).


Assuntos
Assistência à Saúde/tendências , Aprendizado de Máquina/tendências , Tomada de Decisão Clínica/ética , Assistência à Saúde/ética , Humanos , Aprendizado de Máquina/ética
20.
Stroke ; 50(9): 2379-2388, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31409267

RESUMO

Background and Purpose- The clinical course of acute ischemic stroke with large vessel occlusion (LVO) is a multifactorial process with various prognostic factors. We aimed to model this process with machine learning and predict the long-term clinical outcome of LVO before endovascular treatment and to compare our method with previously developed pretreatment scoring methods. Methods- The derivation cohort included 387 LVO patients, and the external validation cohort included 115 LVO patients with anterior circulation who were treated with mechanical thrombectomy. The statistical model with logistic regression without regularization and machine learning algorithms, such as regularized logistic regression, linear support vector machine, and random forest, were used to predict good clinical outcome (modified Rankin Scale score of 0-2 at 90 days) with standard and multiple pretreatment clinical variables. Five previously reported pretreatment scoring methods (the Pittsburgh Response to Endovascular Therapy score, the Stroke Prognostication Using Age and National Institutes of Health Stroke Scale index, the Totaled Health Risks in Vascular Events score, the Houston Intra-Arterial Therapy score, and the Houston Intra-Arterial Therapy 2 score) were compared with these models for the area under the receiver operating characteristic curve. Results- The area under the receiver operating characteristic curve of random forest, which was the worst among the machine learning algorithms, was significantly higher than those of the standard statistical model and the best model among the previously reported pretreatment scoring methods in the derivation (the area under the receiver operating characteristic curve were 0.85±0.07 for random forest, 0.78±0.08 for logistic regression without regularization, and 0.77±0.09 for Stroke Prognostication using Age and National Institutes of Health Stroke Scale) and validation cohorts (the area under the receiver operating characteristic curve were 0.87±0.01 for random forest, 0.56±0.07 for logistic regression without regularization, and 0.83±0.00 for Pittsburgh Response to Endovascular Therapy). Conclusions- Machine learning methods with multiple pretreatment clinical variables can predict clinical outcomes of patients with anterior circulation LVO who undergo mechanical thrombectomy more accurately than previously developed pretreatment scoring methods.


Assuntos
Transtornos Cerebrovasculares/diagnóstico , Transtornos Cerebrovasculares/cirurgia , Aprendizado de Máquina , Trombectomia , Idoso , Idoso de 80 Anos ou mais , Estudos de Coortes , Feminino , Humanos , Aprendizado de Máquina/tendências , Masculino , Pessoa de Meia-Idade , Valor Preditivo dos Testes , Estudos Retrospectivos , Trombectomia/tendências , Resultado do Tratamento
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