AIMC Topic: Prognosis

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In-Hospital Prognostic Value of Electrocardiographic Parameters Other Than ST-Segment Changes in Acute Myocardial Infarction: Literature Review and Future Perspectives.

Heart, lung & circulation
Electrocardiography (ECG) remains an irreplaceable tool in the management of the patients with myocardial infarction, with evaluation of the QRS and ST segment being the present major focus. Several ECG parameters have already been proposed to have p...

The use of machine learning in rare diseases: a scoping review.

Orphanet journal of rare diseases
BACKGROUND: Emerging machine learning technologies are beginning to transform medicine and healthcare and could also improve the diagnosis and treatment of rare diseases. Currently, there are no systematic reviews that investigate, from a general per...

A novel model for predicting the outcome of intracerebral hemorrhage: Based on 1186 Patients.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVE: To establish a model for predicting the outcome according to the clinical and computed tomography(CT) image data of patients with intracerebral hemorrhage(ICH).

Radiomics in neuro-oncology: Basics, workflow, and applications.

Methods (San Diego, Calif.)
Over the last years, the amount, variety, and complexity of neuroimaging data acquired in patients with brain tumors for routine clinical purposes and the resulting number of imaging parameters have substantially increased. Consequently, a timely and...

Defining heterogeneity of epicardial functional stenosis with low coronary flow reserve by unsupervised machine learning.

Heart and vessels
Low CFR is associated with poor prognosis, whereas it is a heterogeneous condition according to the actual coronary flow, such as high resting or low hyperemic coronary flow, which should have different physiological traits and clinical implications....

Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors.

Human brain mapping
In a machine learning setting, this study aims to compare the prognostic utility of connectomic, brain structural, and clinical/demographic predictors of individual change in symptom severity in individuals with schizophrenia. Symptom severity at bas...