AIMC Topic: Health Expenditures

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Robust Machine Learning Variable Importance Analyses of Medical Conditions for Health Care Spending.

Health services research Mar 11, 2018
OBJECTIVE: To propose nonparametric double robust machine learning in variable importance analyses of medical conditions for health spending.
Demography Health Expenditures Risk Adjustment Machine Learning Databases, Factual Female Male United States Chronic Disease Humans
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The Economic Burden of ACPA-Positive Status Among Patients with Rheumatoid Arthritis.

Journal of managed care & specialty pharmacy Jul 17, 2017
BACKGROUND: Anticitrullinated protein antibodies (ACPAs) are serological biomarkers associated with early, rapidly progressing rheumatoid arthritis (RA), including more severe disease and joint damage. ACPA testing has become a routine tool for RA di...
Biological Products Aged Health Expenditures Young Adult Cost of Illness Male Retrospective Studies Arthritis, Rheumatoid Adult Antirheumatic Agents Middle Aged Treatment Outcome Humans Electronic Health Records Disease Progression Anti-Citrullinated Protein Antibodies Female Biomarkers
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Machine-Learning Algorithms to Code Public Health Spending Accounts.

Public health reports (Washington, D.C. : 1974) Mar 31, 2017
OBJECTIVES: Government public health expenditure data sets require time- and labor-intensive manipulation to summarize results that public health policy makers can use. Our objective was to compare the performances of machine-learning algorithms with...
Algorithms Public Health Health Expenditures Humans Machine Learning
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Determining health care cost drivers in older Hodgkin lymphoma survivors using interpretable machine learning methods.

Journal of managed care & specialty pharmacy Apr 1, 2025
BACKGROUND: The cost of health care for patients with Hodgkin lymphoma (HL) is projected to rise, making it essential to understand expenditure drivers across different demographics, including the older adult population. Although older HL patients co...
Aged, 80 and over Male United States Retrospective Studies Female Aged Humans Health Care Costs Machine Learning Medicare Cancer Survivors Health Expenditures Hodgkin Disease SEER Program
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Novel Machine Learning Approach to Identify Preoperative Risk Factors Associated With Super-Utilization of Medicare Expenditure Following Surgery.

JAMA surgery Nov 1, 2019
IMPORTANCE: Typically defined as the top 5% of health care users, super-utilizers are responsible for an estimated 40% to 55% of all health care costs. Little is known about which factors may be associated with increased risk of long-term postoperati...
Retrospective Studies Female Risk Factors Humans Machine Learning Medicare Health Expenditures United States Preoperative Care Postoperative Care Aged Patient Acceptance of Health Care Elective Surgical Procedures Male Risk Assessment
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Bundled Care for Hip Fractures: A Machine-Learning Approach to an Untenable Patient-Specific Payment Model.

Journal of orthopaedic trauma Jul 1, 2019
OBJECTIVES: With the transition to a value-based model of care delivery, bundled payment models have been implemented with demonstrated success in elective lower extremity joint arthroplasty. Yet, hip fracture outcomes are dependent on patient-level ...
Health Expenditures Hip Fractures Arthroplasty, Replacement, Hip Bayes Theorem Medicare Patient Care Bundles Health Care Costs Female Aged Male Middle Aged Length of Stay United States Humans Machine Learning
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Identifying Specific Combinations of Multimorbidity that Contribute to Health Care Resource Utilization: An Analytic Approach.

Medical care Mar 1, 2017
BACKGROUND: Multimorbidity affects the majority of elderly adults and is associated with higher health costs and utilization, but how specific patterns of morbidity influence resource use is less understood.
Male Comorbidity United States Socioeconomic Factors Health Status Age Factors Female Aged Aged, 80 and over Humans Machine Learning Retrospective Studies Activities of Daily Living Medicare Health Behavior Self Report Health Expenditures
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