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Decision Support Systems, Clinical

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Bayesian averaging over Decision Tree models for trauma severity scoring.

Artificial intelligence in medicine
Health care practitioners analyse possible risks of misleading decisions and need to estimate and quantify uncertainty in predictions. We have examined the "gold" standard of screening a patient's conditions for predicting survival probability, based...

A novel method for predicting kidney stone type using ensemble learning.

Artificial intelligence in medicine
The high morbidity rate associated with kidney stone disease, which is a silent killer, is one of the main concerns in healthcare systems all over the world. Advanced data mining techniques such as classification can help in the early prediction of t...

Supervised learning for infection risk inference using pathology data.

BMC medical informatics and decision making
BACKGROUND: Antimicrobial Resistance is threatening our ability to treat common infectious diseases and overuse of antimicrobials to treat human infections in hospitals is accelerating this process. Clinical Decision Support Systems (CDSSs) have been...

Development of an intelligent surgical training system for Thoracentesis.

Artificial intelligence in medicine
Surgical training improves patient care, helps to reduce surgical risks, increases surgeon's confidence, and thus enhances overall patient safety. Current surgical training systems are more focused on developing technical skills, e.g. dexterity, of t...

Clinical decision support of radiotherapy treatment planning: A data-driven machine learning strategy for patient-specific dosimetric decision making.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Clinical decision support systems are a growing class of tools with the potential to impact healthcare. This study investigates the construction of a decision support system through which clinicians can efficiently identify wh...

A novel bagging C4.5 algorithm based on wrapper feature selection for supporting wise clinical decision making.

Journal of biomedical informatics
From the perspective of clinical decision-making in a Medical IoT-based healthcare system, achieving effective and efficient analysis of long-term health data for supporting wise clinical decision-making is an extremely important objective, but deter...

Learning ensemble classifiers for diabetic retinopathy assessment.

Artificial intelligence in medicine
Diabetic retinopathy is one of the most common comorbidities of diabetes. Unfortunately, the recommended annual screening of the eye fundus of diabetic patients is too resource-consuming. Therefore, it is necessary to develop tools that may help doct...

Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction.

Artificial intelligence in medicine
OBJECTIVE: The use of artificial intelligence techniques to find out which Single Nucleotide Polymorphisms (SNPs) promote the development of a disease is one of the features of medical research, as such techniques may potentially aid early diagnosis ...