Use of an artificial neural network for the diagnosis of myocardial infarction.
about
Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical dataPrediction models in the design of neural network based ECG classifiers: a neural network and genetic programming approachBridging scales in cancer progression: mapping genotype to phenotype using neural networksModeling Paradigms for Medical Diagnostic Decision Support: A Survey and Future DirectionsThe dynamic range of neonatal heart rate variability.Artificial neural networks: a prospective tool for the analysis of psychiatric disorders.Comparison of artificial neural networks with other statistical approaches: results from medical data sets.Identification of patients with evolving coronary syndromes by using statistical models with data from the time of presentation.Evaluating uses of data mining techniques in propensity score estimation: a simulation studyPrincipal component analysis and artificial neural network analysis of oral tissue fluorescence spectra: classification of normal premalignant and malignant pathological conditions.A study to derive a clinical decision rule for triage of emergency department patients with chest pain: design and methodology.Detecting central fixation by means of artificial neural networks in a pediatric vision screener using retinal birefringence scanning.Medical diagnostic decision support systems--past, present, and future: a threaded bibliography and brief commentary.Prediction of effect of pegylated interferon alpha-2b plus ribavirin combination therapy in patients with chronic hepatitis C infection.Cost minimization using an artificial neural network sleep apnea prediction tool for sleep studies.Implementation of intelligent decision support systems in health care.Artificial neural network models for prediction of cardiovascular autonomic dysfunction in general Chinese populationComparison of prediction model for cardiovascular autonomic dysfunction using artificial neural network and logistic regression analysisDiscrepancy between clinician and research assistant in TIMI score calculation (TRIAGED CPU)Computer-Aided Prediction of Long-Term Prognosis of Patients with Ulcerative Colitis after Cytoapheresis Therapy.Enhancing the fever workup utilizing a multi-technique modeling approach to diagnose infections more accurately.Chemotherapy-induced neutropenia during adjuvant treatment for cervical cancer patients: development and validation of a prediction model.Identification of low frequency patterns in backpropagation neural networks.Assessing the effect of quantitative and qualitative predictors on gastric cancer individuals survival using hierarchical artificial neural network models.An evolutionary hybrid cellular automaton model of solid tumour growth.Modelling evolutionary cell behaviour using neural networks: application to tumour growthAdaptation of Predictive Models to PDA Hand-Held Devices.Preventive Healthcare: A Neural Network Analysis of Behavioral Habits and Chronic Diseases.The use of computer-assisted diagnosis in cardiac perfusion nuclear medicine studies: a review (Part 3)Prospective validation of a modified thrombolysis in myocardial infarction risk score in emergency department patients with chest pain and possible acute coronary syndrome.Neural networks and psychiatry: candidate applications in clinical decision making.Backpropagation and adaptive resonance theory in predicting suicidal risk.Neural networks in clinical medicine.Kinase inhibitor screening using artificial neural networks and engineered cardiac biowiresDiagnosing acute cardiac ischemia.Use of a neural network as a predictive instrument for length of stay in the intensive care unit following cardiac surgery.Predicting mortality after coronary artery bypass surgery: what do artificial neural networks learn? The Steering Committee of the Cardiac Care Network of Ontario.Modeling mortality in the intensive care unit: comparing the performance of a back-propagation, associative-learning neural network with multivariate logistic regression.Application of an artificial neural network to predict postinduction hypotension during general anesthesia.Clinical validation of an artificial neural network trained to identify acute allograft rejection in liver transplant recipients.
P2860
Q24797301-52A5054B-2CBA-4FD2-A98D-9DEC374D1E84Q24801117-47A2D0D9-25A7-445A-B717-0248022770A2Q27005804-1FC725A7-1A0A-41D6-949C-DCC1AABC8A2EQ29038919-4AFC549A-3046-4402-8C17-B14A0C47A002Q30466575-D39289F4-4D26-4396-BA15-96B1AC21F066Q30498991-33768F58-72E4-47A2-8AEC-3FB8AEABB59AQ30648833-BD76B417-E741-44D6-BB4A-4D8328CCB333Q30991599-F6311971-69D4-4549-B4C9-64D289F2EDE1Q31148198-F0869A44-562E-4F5D-A125-4F3FEFD6F1CDQ33233625-4770346E-457C-4540-BC6B-2F62F14E29A8Q33318584-C258AEE9-31F8-4153-B42A-B326C198F10FQ33612869-2DEEAFEC-CD9F-4E0B-BFF7-2A9C21121146Q33871177-D75BFF62-5314-429B-8239-FC93C482EC01Q34098886-A978CEC7-4FBE-4277-867A-D8D862DECD1FQ34425625-8A988B6D-E8C0-4E35-9640-FC45DAADC762Q34812589-B99B0711-1CFC-4A2C-87A5-9416034DEBA4Q34884883-C0C5A92A-6DE9-41F2-A6DE-C4A08F67D79CQ34935362-590A32DA-DA32-4030-9D25-B070495707F6Q35017934-2C84E207-903C-4D7E-BC72-756FA4022988Q35674635-99434BA2-6540-4871-AEC7-5142CA972230Q35868624-F8DE9DD8-0A02-4F26-A86F-C4C65143942EQ36046284-49826807-7B0C-43D8-8219-B58498886C1EQ36469550-30018AF9-BC3C-4351-8D8D-6A95205BC53BQ36660934-21F21909-48AE-4145-8913-D2993D4EC2A4Q37119366-5E812579-F221-4F3D-8A09-04FE5DCDCC47Q37156318-CDD78C53-CF36-4C76-AE93-A2CB7B4C18D0Q37534180-A9C51FB1-A0C2-483D-8A4B-26638472F1E4Q37728024-4035E3BC-29CE-4C9B-8E8D-92C976345A8EQ39408005-2A33CD4A-8E3C-4D77-B483-DDA6052EBD9BQ39927205-4A5D46FF-C27C-4F76-AF42-910FD067C7ADQ40551996-A21EC1CE-4345-48D3-B6B0-1F970CD7619EQ40826917-9E2F2F51-644D-458D-921A-50C13C2C2E0EQ41205626-6665979A-2947-4D0A-BAA8-C1755224F155Q41718114-D02B7B87-6C4B-45C8-A823-F6E52D44E129Q41928699-2C033C01-0962-49AA-A350-7D74B1BC4742Q41934501-FBF4CFB1-6F4D-47F2-99C7-0205330EDAFBQ42054378-26588B1F-5DA9-4C0E-81E2-813DAFCA0D21Q43053176-30C7582C-3695-4EC6-8A25-0DE846F63323Q43633167-AAFE8158-9D51-4AAB-B99D-F6313C7CA9EEQ43666864-70DE3321-28FB-4D9F-9184-D714B4C1A6A9
P2860
Use of an artificial neural network for the diagnosis of myocardial infarction.
description
1991 nî lūn-bûn
@nan
1991年の論文
@ja
1991年学术文章
@wuu
1991年学术文章
@zh
1991年学术文章
@zh-cn
1991年学术文章
@zh-hans
1991年学术文章
@zh-my
1991年学术文章
@zh-sg
1991年學術文章
@yue
1991年學術文章
@zh-hant
name
Use of an artificial neural network for the diagnosis of myocardial infarction.
@en
Use of an artificial neural network for the diagnosis of myocardial infarction.
@nl
type
label
Use of an artificial neural network for the diagnosis of myocardial infarction.
@en
Use of an artificial neural network for the diagnosis of myocardial infarction.
@nl
prefLabel
Use of an artificial neural network for the diagnosis of myocardial infarction.
@en
Use of an artificial neural network for the diagnosis of myocardial infarction.
@nl
P1476
Use of an artificial neural network for the diagnosis of myocardial infarction.
@en
P2093
P304
P356
10.7326/0003-4819-115-11-843
P407
P577
1991-12-01T00:00:00Z