AIMC Topic: Decision Support Techniques

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Evaluation of antiarrhythmia drug through QSPR modeling and multi criteria decision analysis.

Scientific reports
This study explores how topological indices (TIs), which are mathematical descriptors of a drug's molecular structure, can support to predict vital properties and biological activities. This understanding is a key for more effective drug design. We f...

ChatGPT as a decision-support tool for better self-monitoring of hearing.

American journal of otolaryngology
BACKGROUND: The rapid development of large language model chatbots, such as ChatGPT, has created new possibilities for healthcare support. This study investigates the feasibility of integrating self-monitoring of hearing (via a mobile app) with ChatG...

Predicting Morbidity and Mortality After Transjugular Intrahepatic Portosystemic Shunt Placement: A Review of Existing Models and Future Directions.

Techniques in vascular and interventional radiology
Transjugular intrahepatic portosystemic shunt (TIPS) is a key therapeutic intervention in the management of portal hypertension and its complications, such as variceal bleeding, hepatic hydrothorax, and refractory ascites. TIPS has historically been ...

The Impact of Machine Learning Mortality Risk Prediction on Clinician Prognostic Accuracy and Decision Support: A Randomized Vignette Study.

Medical decision making : an international journal of the Society for Medical Decision Making
BackgroundMachine learning (ML) algorithms may improve the prognosis for serious illnesses such as cancer, identifying patients who may benefit from earlier palliative care (PC) or advance care planning (ACP). We evaluated the impact of various prese...

Prediction of three-year all-cause mortality in patients with heart failure and atrial fibrillation using the CatBoost model.

BMC cardiovascular disorders
BACKGROUND: Heart failure and atrial fibrillation (HF-AF) frequently coexist, resulting in complex interactions that substantially elevate mortality risk. This study aimed to develop and validate a machine learning (ML) model predicting the 3-year al...

Predicting carotid atherosclerosis in latent autoimmune diabetes in adult patients using machine learning models: a retrospective study.

BMC cardiovascular disorders
BACKGROUND: Latent autoimmune diabetes in adults (LADA) is a slowly progressing form of diabetes with autoimmune origins. Patients with LADA are at an elevated risk of developing cardiovascular diseases, including carotid atherosclerosis. While machi...

Predicting mortality risk following major lower extremity amputation using machine learning.

Journal of vascular surgery
OBJECTIVE: Major lower extremity amputation for advanced vascular disease involves significant perioperative risks. Although outcome prediction tools could aid in clinical decision-making, they remain limited. To address this, we developed machine le...

Decision support using machine learning for predicting adequate bladder filling in prostate radiotherapy: a feasibility study.

Radiological physics and technology
This study aimed to develop a model for predicting the bladder volume ratio between daily CBCT and CT to determine adequate bladder filling in patients undergoing treatment for prostate cancer with external beam radiation therapy (EBRT). The model wa...

Machine-learning model for predicting left atrial thrombus in patients with paroxysmal atrial fibrillation.

BMC cardiovascular disorders
OBJECTIVE: Left atrial thrombus (LAT) poses a significant risk for stroke and other thromboembolic complication in patients with atrial fibrillation (AF). This study aimed to evaluate the incidence and predictors of LAT in patients with paroxysmal AF...