- Predicting Patient Disease Risk and Measuring Quality🔍
- Patient Disease Risk Prediction with Lakehouse🔍
- Predictive Risk Models to Identify Patients at High|Risk for Severe ...🔍
- The use and misuse of risk prediction tools for clinical decision|making🔍
- Artificial Intelligence for Clinical Prediction🔍
- Machine learning for patient risk stratification🔍
- Introduction to Application of Predictive Modeling in Healthcare🔍
- Developing prediction models for clinical use using logistic regression🔍
Predicting Patient Disease Risk and Measuring Quality
Predicting Patient Disease Risk and Measuring Quality - Databricks
A blueprint to assess a patient's risk for a condition based on encounter history and demographic information.
Patient Disease Risk Prediction with Lakehouse | Databricks Blog
Building on risk prediction, we next incorporate quality measures. Risk and quality are closely interconnected, and both payers and providers ...
Predictive Risk Models to Identify Patients at High-Risk for Severe ...
Predictive risk models identifying patients at high risk for specific outcomes may provide valuable insights to providers and payers regarding points of ...
The use and misuse of risk prediction tools for clinical decision-making
Risk prediction tools are of great value in supporting clinical decision-making: they can identify, for example, the potential benefit of a treatment on ...
Artificial Intelligence for Clinical Prediction: Exploring Key Domains ...
In the third domain, Risk Assessment, the focus shifts to predicting the likelihood of a patient developing a disease or condition in the future. This ...
Machine learning for patient risk stratification - Nature
Machine learning can help clinicians to make individualized patient predictions only if researchers demonstrate models that contribute novel insights.
Introduction to Application of Predictive Modeling in Healthcare
Posted June 18, 2024 & filed under Quality Improvement & Patient Safety Hot Topics. ... Predictive modeling is an analytical technique that uses statistical ...
Developing prediction models for clinical use using logistic regression
The goal of an accurate prediction model is to provide patient risk stratification to support tailored clinical decision-making with the hope of improving ...
A machine learning approach for diagnostic and prognostic ...
Crucially, the ability to predict adverse events following surgery based on patients' presurgical clinical data, such as electronic health ...
Predicting disease onset from electronic health records for ...
Key to Population Health Management is the use of data; in particular, the ability to identify those patients at risk of future adverse outcomes such as a ...
Medical risk prediction models estimate the likelihood of health-related events occurring in ... coronary heart disease risk assessment in ambulatory care ...
Factors influencing clinician and patient interaction with machine ...
Article quality varied with qualitative studies performing strongest. Overall, perceptions of ML risk prediction models were positive. HCPs and patients ...
Pathways to chronic disease detection and prediction: Mapping the ...
Machine learning (ML) shows promise for early detection and prediction of chronic diseases. Complex “omics” data from genomics, proteomics, and ...
Predicting disease risks from highly imbalanced data using random ...
We present a method utilizing Healthcare Cost and Utilization Project (HCUP) dataset for predicting disease risk of individuals based on ...
A proposed technique for predicting heart disease using machine ...
One of the critical issues in medical data analysis is accurately predicting a patient's risk of heart disease, which is vital for early ...
Prediction of disease comorbidity using explainable artificial ...
Disease comorbidity is a major challenge in healthcare affecting the patient's quality of life and costs. AI-based prediction of comorbidities can overcome ...
Artificial intelligence for cardiovascular disease risk assessment in ...
Prognostic models use AI to predict disease progression and patient outcomes, guiding personalised insights into a patient's health status. Finally, ...
Social Determinants of Health Improve Predictive Accuracy of ...
As a result, health systems often rely on algorithms and risk prediction models to identify high-risk patients who may benefit from ...
Prediction models for cardiovascular disease risk in the general ...
The prediction horizon was not specified for 49 models (13%), and for 92 (25%) crucial information was missing to enable the model to be used ...
Using measures of race to make clinical predictions - PNAS
Much of clinical decision-making involves the quantitative prediction of disease risk, treatment effectiveness, or other outcomes based on various sources of ...