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Comorbidity

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Time to Move On: The Impending Need for a New Disease-specific Comorbidity Index for Bladder Cancer Patients Undergoing Robot-assisted Radical Cystectomy.

European urology focus
The Charlson comorbidity index is an outdated comorbidity assessment tool which is not disease specific and is not applicable to contemporary BCa patients.

Preoperative Prediction of Value Metrics and a Patient-Specific Payment Model for Primary Total Hip Arthroplasty: Development and Validation of a Deep Learning Model.

The Journal of arthroplasty
BACKGROUND: The primary objective was to develop and test an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient charges, and discharge disposition for total hip arthroplasty. The secondary objective was to create...

Importance of coding co-morbidities for APR-DRG assignment: Focus on cardiovascular and respiratory diseases.

Health information management : journal of the Health Information Management Association of Australia
BACKGROUND: The All Patient-Refined Diagnosis-Related Groups (APR-DRGs) system has adjusted the basic DRG structure by incorporating four severity of illness (SOI) levels, which are used for determining hospital payment. A comprehensive report of all...

Extensive phenotype data and machine learning in prediction of mortality in acute coronary syndrome - the MADDEC study.

Annals of medicine
Investigation of the clinical potential of extensive phenotype data and machine learning (ML) in the prediction of mortality in acute coronary syndrome (ACS). The value of ML and extensive clinical data was analyzed in a retrospective registry stud...

Using a machine learning approach to predict outcome after surgery for degenerative cervical myelopathy.

PloS one
Degenerative cervical myelopathy (DCM) is a spinal cord condition that results in progressive non-traumatic compression of the cervical spinal cord. Spine surgeons must consider a large quantity of information relating to disease presentation, imagin...

Predicting hospital-acquired pneumonia among schizophrenic patients: a machine learning approach.

BMC medical informatics and decision making
BACKGROUND: Medications are frequently used for treating schizophrenia, however, anti-psychotic drug use is known to lead to cases of pneumonia. The purpose of our study is to build a model for predicting hospital-acquired pneumonia among schizophren...

Exploring the use of machine learning for risk adjustment: A comparison of standard and penalized linear regression models in predicting health care costs in older adults.

PloS one
BACKGROUND: Payers and providers still primarily use ordinary least squares (OLS) to estimate expected economic and clinical outcomes for risk adjustment purposes. Penalized linear regression represents a practical and incremental step forward that p...

Novel Machine Learning Identifies Brain Patterns Distinguishing Diagnostic Membership of Human Immunodeficiency Virus, Alcoholism, and Their Comorbidity of Individuals.

Biological psychiatry. Cognitive neuroscience and neuroimaging
The incidence of alcohol use disorder (AUD) in human immunodeficiency virus (HIV) infection is twice that of the rest of the population. This study documents complex radiologically identified, neuroanatomical effects of AUD+HIV comorbidity by identif...

Neurodevelopmental heterogeneity and computational approaches for understanding autism.

Translational psychiatry
In recent years, the emerging field of computational psychiatry has impelled the use of machine learning models as a means to further understand the pathogenesis of multiple clinical disorders. In this paper, we discuss how autism spectrum disorder (...