AIMC Topic: Prognosis

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Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To conduct a systematic review of deep learning models for electronic health record (EHR) data, and illustrate various deep learning architectures for analyzing different data sources and their target applications. We also highlight ongoin...

Proton pump inhibitors receiving and prognosis of patients after scheduled percutaneous coronary interventions.

Terapevticheskii arkhiv
AIM: The urgency of the study is determined by the lack of data necessary in order to assess the safety of prolonged use of proton pump inhibitors (PPI) in patients with IHD combined with anti-aggregant therapy. The aim of the study was to study the ...

The Association Between Arthralgia and Vedolizumab Using Natural Language Processing.

Inflammatory bowel diseases
BACKGROUND: The gut-selective nature of vedolizumab has raised questions regarding increased joint pain or arthralgia with its use in inflammatory bowel disease (IBD) patients. As arthralgias are seldom coded and thus difficult to study, few studies ...

Use of Machine Learning to Determine Deviance in Neuroanatomical Maturity Associated With Future Psychosis in Youths at Clinically High Risk.

JAMA psychiatry
IMPORTANCE: Altered neurodevelopmental trajectories are thought to reflect heterogeneity in the pathophysiologic characteristics of schizophrenia, but whether neural indicators of these trajectories are associated with future psychosis is unclear.

Predicting Response to Repetitive Transcranial Magnetic Stimulation in Patients With Schizophrenia Using Structural Magnetic Resonance Imaging: A Multisite Machine Learning Analysis.

Schizophrenia bulletin
BACKGROUND: The variability of responses to plasticity-inducing repetitive transcranial magnetic stimulation (rTMS) challenges its successful application in psychiatric care. No objective means currently exists to individually predict the patients' r...

Neural Networks for Prognostication of Patients With Heart Failure.

Circulation. Heart failure
Background Prognostication of heart failure patients from cardiopulmonary exercise test (CPET) currently involves simplification of complex time-series data into summary indices. We hypothesized that prognostication could be improved by considering t...

Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To develop a conceptual prediction model framework containing standardized steps and describe the corresponding open-source software developed to consistently implement the framework across computational environments and observational heal...