AIMC Topic: Cohort Studies

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PREDICTIVE MODELING OF HOSPITAL READMISSION RATES USING ELECTRONIC MEDICAL RECORD-WIDE MACHINE LEARNING: A CASE-STUDY USING MOUNT SINAI HEART FAILURE COHORT.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Reduction of preventable hospital readmissions that result from chronic or acute conditions like stroke, heart failure, myocardial infarction and pneumonia remains a significant challenge for improving the outcomes and decreasing the cost of healthca...

[Spontaneous bacterial peritonitis].

Klinicka mikrobiologie a infekcni lekarstvi
AIM OF STUDY: Spontaneous bacterial peritonitis (SBP) is the most frequent infectious complication of liver cirrhosis with serious consequences. Initially, SBP is always treated with empirical, not targeted, antibiotic therapy. Since a retrospective ...

Determining Multiple Sclerosis Phenotype from Electronic Medical Records.

Journal of managed care & specialty pharmacy
BACKGROUND: Multiple sclerosis (MS), a central nervous system disease in which nerve signals are disrupted by scarring and demyelination, is classified into phenotypes depending on the patterns of cognitive or physical impairment progression: relapsi...

Comparison of Re-irradiation Outcomes for Charged Particle Radiotherapy and Robotic Stereotactic Radiotherapy Using CyberKnife for Recurrent Head and Neck Cancers: A Multi-institutional Matched-cohort Analysis.

Anticancer research
AIM: To compare survival outcomes for charged particle radiotherapy (CP) and stereotactic body radiotherapy using CyberKnife (CK) in patients who had undergone re-irradiation for head and neck cancers.

Improving Prediction of Suicide and Accidental Death After Discharge From General Hospitals With Natural Language Processing.

JAMA psychiatry
IMPORTANCE: Suicide represents the 10th leading cause of death across age groups in the United States (12.6 cases per 100 000) and remains challenging to predict. While many individuals who die by suicide are seen by physicians before their attempt, ...

Risk prediction for cardiovascular disease using ECG data in the China kadoorie biobank.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
We set out to use machine learning techniques to analyse ECG data to improve risk evaluation of cardiovascular disease in a very large cohort study of the Chinese population. We performed this investigation by (i) detecting "abnormality" using 3 one-...

Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data.

Critical care medicine
OBJECTIVE: The use of machine-learning algorithms to classify alerts as real or artifacts in online noninvasive vital sign data streams to reduce alarm fatigue and missed true instability.

Unsupervised learning technique identifies bronchiectasis phenotypes with distinct clinical characteristics.

The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease
BACKGROUND: Unsupervised learning technique allows researchers to identify different phenotypes of diseases with complex manifestations.

Improving the knowledge base in older people's care.

Nursing older people
The UK has a rapidly ageing population, and the number of people aged over 75 is projected to double in the next 30 years. In November 2014, King's College London introduced the Older Person's Nurse Fellowship, a pioneering programme designed to give...

Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards.

Critical care medicine
OBJECTIVE: Machine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. We compared the accuracy of different techniques for detecting clinical deterioration on the wards in a large, multicenter ...