AIMC Topic: Cohort Studies

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Association Between Early Intravenous Fluids Provided by Paramedics and Subsequent In-Hospital Mortality Among Patients With Sepsis.

JAMA network open
IMPORTANCE: Early administration of intravenous fluids is recommended for all patients with sepsis, but the association of this treatment with mortality may depend on the patient's initial blood pressure.

Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy.

Nature communications
Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 normal and abn...

Identifying Cases of Metastatic Prostate Cancer Using Machine Learning on Electronic Health Records.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Cancer stage is rarely captured in structured form in the electronic health record (EHR). We evaluate the performance of a classifier, trained on structured EHR data, in identifying prostate cancer patients with metastatic disease. Using EHR data for...

Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Parkinson's Disease (PD) is one of the most prevalent neurodegenerative diseases that affects tens of millions of Americans. PD is highly progressive and heterogeneous. Quite a few studies have been conducted in recent years on predictive or disease ...

The Role of a Deep-Learning Method for Negation Detection in Patient Cohort Identification from Electroencephalography Reports.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Detecting negation in biomedical texts entails the automatic identification of negation cues (e.g. "never", "not", "no longer") as well as the scope of these cues. When medical concepts or terms are identified within the scope of a negation cue, thei...

Application of Machine Learning Methods to Predict Non-Alcoholic Steatohepatitis (NASH) in Non-Alcoholic Fatty Liver (NAFL) Patients.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Non-alcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease worldwide. NAFLD patients have excessive liver fat (steatosis), without other liver diseases and without excessive alcohol consumption. NAFLD consists of a spectr...

A Computable Phenotype for Acute Respiratory Distress Syndrome Using Natural Language Processing and Machine Learning.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Acute Respiratory Distress Syndrome (ARDS) is a syndrome of respiratory failure that may be identified using text from radiology reports. The objective of this study was to determine whether natural language processing (NLP) with machine learning per...

Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography.

Echocardiography (Mount Kisco, N.Y.)
BACKGROUND: Heart disease (HD) is the leading cause of global death; there are several mortality prediction models of HD for identifying critically-ill patients and for guiding decision making. The existing models, however, cannot be used during init...

Biomarkers of erosive arthritis in systemic lupus erythematosus: Application of machine learning models.

PloS one
OBJECTIVE: Limited evidences are available on biomarkers to recognize Systemic Lupus erythematosus (SLE) patients at risk to develop erosive arthritis. Anti-citrullinated peptide antibodies (ACPA) have been widely investigated and identified in up to...