AIMC Topic: Treatment Outcome
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Predicting remission after acute phase pharmacotherapy in patients with bipolar I depression: A machine learning approach with cross-trial and cross-drug replication.
Bipolar disorders
Oct 3, 2024
OBJECTIVES: Interpatient variability in bipolar I depression (BP-D) symptoms challenges the ability to predict pharmacotherapeutic outcomes. A machine learning workflow was developed to predict remission after 8 weeks of pharmacotherapy (total score ...
Comparison of Patients With and Without Predicted Surgical Indication Between Clusters of Adult Spine Deformity (ASD) Patients.
Spine
Oct 1, 2024
STUDY DESIGN: Multicentric, retrospective analysis of prospectively collected data.
Role of artificial intelligence in treatment planning and outcome prediction of jaw corrective surgeries by using 3-D imaging: a systematic review.
Oral surgery, oral medicine, oral pathology and oral radiology
Oct 1, 2024
OBJECTIVE: Artificial intelligence (AI) has been increasingly utilized in diagnosis of skeletal deformities, while its role in treatment planning and outcome prediction of jaw corrective surgeries with 3-dimensional (3D) imaging remains underexplored...
Prediction of Lesion-Based Treatment Response after Two Cycles of Lu-177 Prostate Specific Membrane Antigen Treatment in Metastatic Castration-Resistant Prostate Cancer Using Machine Learning.
Urologia internationalis
Sep 30, 2024
INTRODUCTION: Lutetium-177 (Lu-177) prostate-specific membrane antigen (PSMA) therapy is a radionuclide treatment that prolongs overall survival in metastatic castration-resistant prostate cancer (MCRPC). We aimed to predict lesion-based treatment re...
Positron Emission Tomography Computed Tomography
Radiopharmaceuticals
Neoplasm Metastasis
Prostatic Neoplasms, Castration-Resistant
Radioisotopes
Aged, 80 and over
Male
Middle Aged
Treatment Outcome
Humans
Antigens, Surface
Machine Learning
Gallium Radioisotopes
Retrospective Studies
Glutamate Carboxypeptidase II
Aged
Lutetium
Retrospective Analysis of Radiofrequency Ablation in Patients with Small Solitary Hepatocellular Carcinoma: Survival Outcomes and Development of a Machine Learning Prognostic Model.
Current medical science
Sep 30, 2024
BACKGROUND AND OBJECTIVE: The effectiveness of radiofrequency ablation (RFA) in improving long-term survival outcomes for patients with a solitary hepatocellular carcinoma (HCC) measuring 5 cm or less remains uncertain. This study was designed to elu...
Predicting Individual Treatment Effects to Determine Duration of Dual Antiplatelet Therapy After Stent Implantation.
Journal of the American Heart Association
Sep 30, 2024
BACKGROUND: After coronary stent implantation, prolonged dual antiplatelet therapy (DAPT) increases bleeding risk, requiring personalization of DAPT duration. The aim of this study was to develop and validate a machine learning model to predict optim...
Machine learning for post-liver transplant survival: Bridging the gap for long-term outcomes through temporal variation features.
Computer methods and programs in biomedicine
Sep 29, 2024
BACKGROUND: The long-term survival of liver transplant (LT) recipients is essential for optimizing organ allocation and estimating mortality outcomes. While models like the Model-for-End-Stage-Liver-Disease (MELD) predict 90-day mortality on the wait...
Development and Internal Validation of Machine Learning to Predict Postoperative Worse Functional Status after Surgical Treatment for Thoracic Spinal Stenosis.
Medical science monitor : international medical journal of experimental and clinical research
Sep 26, 2024
BACKGROUND The objective of this study was to develop and validate machine learning (ML) algorithms to predict the 30-day and 6-month risk of deteriorating functional status following surgical treatment for thoracic spinal stenosis (TSS). We aimed to...
Prevention of adverse HIV treatment outcomes: machine learning to enable proactive support of people at risk of HIV care disengagement in Tanzania.
BMJ open
Sep 24, 2024
OBJECTIVES: This study aimed to develop a machine learning (ML) model to predict disengagement from HIV care, high viral load or death among people living with HIV (PLHIV) with the goal of enabling proactive support interventions in Tanzania. The alg...
Influence of panic disorder and paroxetine on brain functional hubs in drug-free patients.
Journal of psychopharmacology (Oxford, England)
Sep 23, 2024
BACKGROUND: The effects of panic disorder (PD) and pharmacotherapy on brain functional hubs in drug-free patients, and the utility of their degree centrality (DC) in diagnosing and predicting treatment response (TR) for PD, remained unclear.
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