AIMC Topic: Treatment Outcome

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Identifying azithromycin responders with an individual treatment effect model in COPD.

Thorax
OBJECTIVE: Long-term azithromycin treatment effectively prevents acute exacerbations of chronic obstructive pulmonary disease (COPD). However, patients would benefit from better identification of responders and non-responders to minimise unnecessary ...

Machine learning using serial changes in proteinuria during initial steroid therapy to predict treatment response and immunosuppressant use in pediatric idiopathic nephrotic syndrome.

Clinical and experimental nephrology
BACKGROUND: Epidemiological studies on idiopathic nephrotic syndrome (INS) in children have identified no definitive factors predicting steroid-resistant nephrotic syndrome (SRNS) or frequent relapsing nephrotic syndrome. Research using machine learn...

Predicting rTMS treatment response in depression: use of machine learning models to identify the roles of metabolic and clinical factors.

Journal of affective disorders
BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depression in patients with major depressive disorder (MDD) and bipolar disorder (BD), but accurate prediction of treatment response remains a challenge. Th...

Multiband EEG signatures decoded using machine learning for predicting rTMS treatment response in MDD.

Journal of affective disorders
BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is a promising treatment for major depression disorder (MDD), particularly for treatment-resistant cases. However, identifying translatable biomarkers predictive of treatment outcomes re...

Depression is associated with treatment response trajectories in adults with Prolonged Grief Disorder: A machine learning analysis.

Journal of affective disorders
BACKGROUND: Although evidence-based Prolonged Grief Disorder treatments (PGDT) exist, pretreatment characteristics associated with differential improvement remain unidentified. To identify clinical factors relevant to optimizing PGDT outcomes, we use...

Predicting treatment response to systemic therapy in advanced gallbladder cancer using multiphase enhanced CT images.

European radiology
BACKGROUND: Accurate estimation of treatment response can help clinicians identify patients who would potentially benefit from systemic therapy. This study aimed to develop and externally validate a model for predicting treatment response to systemic...

Inflammatory biomarkers as predictors for unlocking antidepressant efficacy: Assessing predictive value and risk stratification in major depressive disorder in a prospective longitudinal study.

Journal of affective disorders
BACKGROUND: Major depressive disorder (MDD) is characterized by significant heterogeneity in treatment response, with inflammation hypothesized to play a role in its pathophysiology. Peripheral inflammatory biomarkers, such as the neutrophil-to-lymph...

Artificial intelligence in predicting efficacy and toxicity of Immunotherapy: Applications, challenges, and future directions.

Cancer letters
Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, becoming a standard approach for various tumor types. Consequently, accurately predicting their efficacy has become crucial in clinical practice. Artificial intelligence (AI) h...

`Probabilistic ensemble learning for prediction of stroke thrombectomy outcomes from the NeuroVascular Quality Initiative-Quality Outcomes Database (NVQI-QOD) Acute Ischemic Stroke Registry.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
INTRODUCTION: Mechanical Thrombectomy (MT) is the standard of care in the interventional management of Acute Ischemic Stroke (AIS). The NVQI-QOD registry records detailed patient characteristics, pre-operative imaging, procedure metrics, and post-ope...