AIMC Topic: Treatment Outcome

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Application of an AI-Based Model for Non-Invasive Sonographic Assessment for Injection Laryngoplasty.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: Hyaluronic acid (HA) can be degraded over time. However, the persistence of the effects after injection laryngoplasty (IL) for unilateral vocal fold paralysis (UVFP) has been observed. The relation between HA residue and clinical voice out...

Application of machine learning models to identify predictors of good outcome after laparoscopic fundoplication.

Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract
BACKGROUND: Laparoscopic fundoplication remains the gold standard treatment for gastroesophageal reflux disease. However, 10% to 20% of patients experience new, persistent, or recurrent symptoms warranting further treatment. Potential predictors for ...

A randomized controlled trial of the effects of dog-assisted versus robot dog-assisted therapy for children with autism or Down syndrome.

PloS one
Research with controlled or crossover designs in animal-assisted therapy have largely used control groups receiving no treatment or treatment as usual, which can potentially inflate the effects of these interventions. It is therefore not always clear...

Machine learning predicts spinal cord stimulation surgery outcomes and reveals novel neural markers for chronic pain.

Scientific reports
Spinal cord stimulation (SCS) is a well-accepted therapy for refractory chronic pain. However, predicting responders remain a challenge due to a lack of objective pain biomarkers. The present study applies machine learning to predict which patients w...

Predicting treatment response to cognitive behavior therapy in social anxiety disorder on the basis of demographics, psychiatric history, and scales: A machine learning approach.

PloS one
Only about half of patients with social anxiety disorder (SAD) respond substantially to cognitive behavioral therapy (CBT). However, there has been little evidence available to clinicians or patients about whether any individual patient is more or le...

A clinical data-driven machine learning approach for predicting the effectiveness of piperacillin-tazobactam in treating lower respiratory tract infections.

BMC pulmonary medicine
BACKGROUND: In hospitalized patients, inadequate antibiotic dosage leading to bacterial resistance and increased antimicrobial use intensity due to overexposure to antibiotics are common problems. In the present study, we constructed a machine learni...

Histopathology based AI model predicts anti-angiogenic therapy response in renal cancer clinical trial.

Nature communications
Anti-angiogenic (AA) therapy is a cornerstone of metastatic clear cell renal cell carcinoma (ccRCC) treatment, but not everyone responds, and predictive biomarkers are lacking. CD31, a marker of vasculature, is insufficient, and the Angioscore, an RN...

State-of-the-art for automated machine learning predicts outcomes in poor-grade aneurysmal subarachnoid hemorrhage using routinely measured laboratory & radiological parameters: coagulation parameters and liver function as key prognosticators.

Neurosurgical review
The objective of this study was to develop and evaluate automated machine learning (aML) models for predicting short-term (1-month) and medium-term (3-month) functional outcomes [Modified Rankin Scale (mRS)] in patients suffering from poor-grade aneu...