Assessment of diastolic dysfunction by tissue Doppler echocardiography in patients with heart failure

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Assessment of regional systolic and diastolic impairment of left ventricular function: a study with tissue doppler imaging on right ventricular pacing

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Suppression of myocardial fibrosis by valsartan and monopril in animals after acute myocardial infarction

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Assessment of regional systolic and diastolic impairment on left ventricular function: a study with tissue Doppler imaging on right ventricular pacing

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Expression of Cyclooxygenase-2 Protein in Acute Myocardial Infarction

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Elevation of marcophage migration inhibitory factor level acute myocardial infarction but not in acute myocardial ischaemia

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Reduction in stroke by statin therapy markedly increases its cost-effectiveness

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Plasma adrenomedullin level in patients with heart failure is related to systolic but not diastolic dysfunction

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Supervised learning for suicidal ideation detection in online user content

Copyright © 2018 Shaoxiong Ji et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Early detection and treatment are regarded as the most effective ways to prevent suicidal ideation and potential suicide attempts-two critical risk factors resulting in successful suicides. Online communication channels are becoming a new way for people to express their suicidal tendencies. This paper presents an approach to understand suicidal ideation through online user-generated content with the goal of early detection via supervised learning. Analysing users' language preferences and topic descriptions reveals rich knowledge that can be used as an early warning system for detecting suicidal tendencies. Suicidal individuals express strong negative feelings, anxiety, and hopelessness. Suicidal thoughts may involve family and friends. And topics they discuss cover both personal and social issues. To detect suicidal ideation, we extract several informative sets of features, including statistical, syntactic, linguistic, word embedding, and topic features, and we compare six classifiers, including four traditional supervised classifiers and two neural network models. An experimental study demonstrates the feasibility and practicability of the approach and provides benchmarks for the suicidal ideation detection on the active online platforms: Reddit SuicideWatch and Twitter

An analysis on reasons of non-compliant to cardiac rehabilitation programme

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