AIMC Topic: Treatment Outcome

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Development and validation of multivariable prediction models of remission, recovery, and quality of life outcomes in people with first episode psychosis: a machine learning approach.

The Lancet. Digital health
BACKGROUND: Outcomes for people with first-episode psychosis are highly heterogeneous. Few reliable validated methods are available to predict the outcome for individual patients in the first clinical contact. In this study, we aimed to build multiva...

CT Texture Analysis and Machine Learning Improve Post-ablation Prognostication in Patients with Adrenal Metastases: A Proof of Concept.

Cardiovascular and interventional radiology
INTRODUCTION: To assess the performance of pre-ablation computed tomography texture features of adrenal metastases to predict post-treatment local progression and survival in patients who underwent ablation using machine learning as a prediction tool...

Initial report of safety and procedure duration of robotic-assisted chronic total occlusion coronary intervention.

Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions
BACKGROUND: No previous reports have examined the impact of robotic-assisted (RA) chronic total occlusion (CTO) PCI on procedural duration or safety compared to totally manual CTO PCI.

Cath Lab Robotics: Paradigm Change in Interventional Cardiology?

Current cardiology reports
PURPOSE OF REVIEW: To review the contemporary evidence for robotic-assisted percutaneous coronary and vascular interventions, discussing its current capabilities, limitations, and potential future applications.

Machine learning based prediction of perioperative blood loss in orthognathic surgery.

Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery
The aim of this study was to evaluate, if and with what accuracy perioperative blood loss can be calculated by a machine learning algorithm prior to orthognathic surgery. The investigators implemented a random forest algorithm to predict perioperativ...

Multicategory individualized treatment regime using outcome weighted learning.

Biometrics
Individualized treatment regimes (ITRs) aim to recommend treatments based on patient-specific characteristics in order to maximize the expected clinical outcome. Outcome weighted learning approaches have been proposed for this optimization problem wi...

Connectomic Profiling Identifies Responders to Vagus Nerve Stimulation.

Annals of neurology
OBJECTIVE: Vagus nerve stimulation (VNS) is a common treatment for medically intractable epilepsy, but response rates are highly variable, with no preoperative means of identifying good candidates. This study aimed to predict VNS response using struc...

Predicting Clinical Outcomes of Large Vessel Occlusion Before Mechanical Thrombectomy Using Machine Learning.

Stroke
Background and Purpose- The clinical course of acute ischemic stroke with large vessel occlusion (LVO) is a multifactorial process with various prognostic factors. We aimed to model this process with machine learning and predict the long-term clinica...