AIMC Topic: Treatment Outcome

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Generative AI-Enabled Therapy Support Tool for Improved Clinical Outcomes and Patient Engagement in Group Therapy: Real-World Observational Study.

Journal of medical Internet research
BACKGROUND: Cognitive behavioral therapy (CBT) is a highly effective treatment for depression and anxiety disorders. Nonetheless, a substantial proportion of patients do not respond to treatment. The lack of engagement with therapeutic materials and ...

Outcomes of lateral femoral cutaneous nerve decompression surgery in meralgia paraesthetica: assessment of pain, sensory deficits, and quality of life.

International orthopaedics
PURPOSE: Meralgia paraesthetica (MP) is a rare neuropathy of the lateral femoral cutaneous nerve (LFCN), characterized by thigh pain, paraesthesia, or sensory loss. When conservative treatments fail, surgical interventions such as neurolysis or neure...

Combined effects and timing of robotic training and botulinum toxin on upper limb spasticity and motor function: a single‑blinded randomized controlled pilot study.

Journal of neuroengineering and rehabilitation
BACKGROUND: This study aimed to evaluate the combined effects of robotic training (RT) and botulinum toxin (BTX) injections on motor function and spasticity in individuals with post-stroke upper limb spasticity (ULS). We also sought to investigate th...

Effect of Wearable Robot-Assisted Gait Training on Balance and Walking Ability in Subacute Stroke Patients.

American journal of physical medicine & rehabilitation
OBJECTIVE: This study investigated the effectiveness of wearable robot-assisted gait training compared to treadmill gait training for improving balance and walking ability in stroke patients.

A machine learning approach to predict treatment efficacy and adverse effects in major depression using CYP2C19 and clinical-environmental predictors.

Psychiatric genetics
BACKGROUND: Major depressive disorder (MDD) is among the leading causes of disability worldwide and treatment efficacy is variable across patients. Polymorphisms in cytochrome P450 2C19 (CYP2C19) play a role in response and side effects to medication...

Predicting the complexity of minimally invasive liver resection for hepatocellular carcinoma using machine learning.

HPB : the official journal of the International Hepato Pancreato Biliary Association
BACKGROUND: Despite technical advancements, minimally invasive liver surgery (MILS) for hepatocellular carcinoma (HCC) remains challenging. Nonetheless, effective tools to assess MILS complexity are still lacking. Machine learning (ML) models could i...

Using Machine Learning to Predict Outcomes Following Thoracic and Complex Endovascular Aortic Aneurysm Repair.

Journal of the American Heart Association
BACKGROUND: Thoracic endovascular aortic repair (TEVAR) and complex endovascular aneurysm repair (EVAR) are complex procedures that carry a significant risk of complications. While risk prediction tools can aid in clinical decision making, they remai...

Pretrained transformers applied to clinical studies improve predictions of treatment efficacy and associated biomarkers.

Nature communications
Cancer treatment has made significant advancements in recent decades, however many patients still experience treatment failure or resistance. Attempts to identify determinants of response have been hampered by a lack of tools that simultaneously acco...

Predicting responsiveness to fixed-dose methylene blue in adult patients with septic shock using interpretable machine learning: a retrospective study.

Scientific reports
This study aimed to develop an interpretable machine learning model to predict methylene blue (MB) responsiveness in adult patients with refractory septic shock and to identify key factors influencing MB responsiveness using the SHapley Additive exPl...