AIMC Topic: Insurance Claim Review
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Identifying drugs with disease-modifying potential in Parkinson's disease using artificial intelligence and pharmacoepidemiology.
Pharmacoepidemiology and drug safety
May 14, 2020
PURPOSE: The aim of the study was to assess the feasibility of an approach combining computational methods and pharmacoepidemiology to identify potentially disease-modifying drugs in Parkinson's disease (PD).
Evaluating the performance of a predictive modeling approach to identifying members at high-risk of hospitalization.
Journal of medical economics
Sep 24, 2019
To evaluate the risk-of-hospitalization (ROH) models developed at Blue Cross Blue Shield of Louisiana (BCBSLA) and compare this approach to the DxCG risk-score algorithms utilized by many health plans. Time zero for this study was December 31, 2016....
Humans
Male
Aged
Infant
Machine Learning
Middle Aged
Female
Aged, 80 and over
Adolescent
Algorithms
Risk Assessment
Insurance Carriers
Infant, Newborn
Young Adult
Logistic Models
Child, Preschool
Insurance, Health
Hospitalization
Adult
Residence Characteristics
Child
Insurance Claim Review
Models, Statistical
Using Generative AI to Translate Administrative Claims Data into Narrative Summaries for Palliative Care Needs Assessment: A Case Study.
Studies in health technology and informatics
Jul 24, 2024
Community-based palliative care is a useful, but underutilized service to support seriously ill older adults to remain safely at home and improve quality of life. Clinical decision support tools can assist palliative care need assessments if presente...
Towards Interpretable End-Stage Renal Disease (ESRD) Prediction: Utilizing Administrative Claims Data with Explainable AI Techniques.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 1, 2024
This study explores the potential of utilizing administrative claims data, combined with advanced machine learning and deep learning techniques, to predict the progression of Chronic Kidney Disease (CKD) to End-Stage Renal Disease (ESRD). We analyze ...
Machine Learning Can Unlock Insights Into Mortality.
American journal of public health
Jul 1, 2021
Patient Coded Severity and Payment Penalties Under the Hospital Readmissions Reduction Program: A Machine Learning Approach.
Medical care
Nov 1, 2020
OBJECTIVE: The objective of this study was to examine variation in hospital responses to the Centers for Medicare and Medicaid's expansion of allowable secondary diagnoses in January 2011 and its association with financial penalties under the Hospita...
Using Machine Learning to Predict Primary Care and Advance Workforce Research.
Annals of family medicine
Jul 1, 2020
PURPOSE: To develop and test a machine-learning-based model to predict primary care and other specialties using Medicare claims data.
Characterizing Subgroups of High-Need, High-Cost Patients Based on Their Clinical Conditions: a Machine Learning-Based Analysis of Medicaid Claims Data.
Journal of general internal medicine
Aug 1, 2019
Combining the Power of Artificial Intelligence with the Richness of Healthcare Claims Data: Opportunities and Challenges.
PharmacoEconomics
Jun 1, 2019
Combinations of healthcare claims data with additional datasets provide large and rich sources of information. The dimensionality and complexity of these combined datasets can be challenging to handle with standard statistical analyses. However, rece...
Using Machine Learning Applied to Real-World Healthcare Data for Predictive Analytics: An Applied Example in Bariatric Surgery.
Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
May 1, 2019
OBJECTIVES: Laparoscopic metabolic surgery (MxS) can lead to remission of type 2 diabetes (T2D); however, treatment response to MxS can be heterogeneous. Here, we demonstrate an open-source predictive analytics platform that applies machine-learning ...
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