AIMC Topic: Recurrence
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AI-driven analysis by identifying risk factors of VL relapse in HIV co-infected patients.
Scientific reports
Jul 1, 2025
Visceral Leishmaniasis (VL), also known as Kala-Azar, poses a significant global public health challenge and is a neglected disease, with relapses and treatment failures leading to increased morbidity and mortality. This study introduces an explainab...
Relapse prediction using wearable data through convolutional autoencoders and clustering for patients with psychotic disorders.
Scientific reports
May 29, 2025
Relapse of psychotic disorders occurs commonly even after appropriate treatment. Digital phenotyping becomes essential to achieve remote monitoring for mental conditions. We applied a personalized approach using neural-network-based anomaly detection...
The risk factors for relapse behavior in individuals with substance use disorders: An interpretable machine learning study.
Journal of affective disorders
May 27, 2025
BACKGROUND: Substance abuse has become a serious public health problem worldwide, and finding effective prevention and treatment strategies is undoubtedly an urgent need. This study addresses the risk factors that lead to relapse behaviors among subs...
Construction of a predictive model for relapse of primary autoimmune hemolytic anemia: a retrospective cohort study.
Annals of medicine
May 22, 2025
OBJECTIVES: To develop a machine learning-based model to predict the relapse risk of Primary Autoimmune Haemolytic Anaemia (AIHA) after the last remission.
Forecasting optimal treatments in relapsed/refractory mature T- and NK-cell lymphomas: A global PETAL Consortium study.
British journal of haematology
May 1, 2025
There is no standard of care in relapsed/refractory T-cell/natural killer-cell lymphomas. Patients often cycle through cytotoxic chemotherapy (CC), epigenetic modifiers (EM) or small molecule inhibitors (SMI) empirically. Ideal therapy at each line r...
Predicting responsiveness to a dialectical behaviour therapy skills training app for recurrent binge eating: A machine learning approach.
Behaviour research and therapy
Apr 22, 2025
OBJECTIVE: Smartphone applications (apps) show promise as an effective and scalable intervention modality for disordered eating, yet responsiveness varies considerably. The ability to predict user responses to app-based interventions is currently lim...
Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data.
Journal of psychopathology and clinical science
Mar 20, 2025
Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use an...
Personalized prediction of psoriasis relapse post-biologic discontinuation: a machine learning-driven population cohort study.
The Journal of dermatological treatment
Mar 19, 2025
BACKGROUND: Identifying the risk of psoriasis relapse after discontinuing biologics can help optimize treatment strategies, potentially reducing relapse rates and alleviating the burden of disease management.
Machine learning for the rElapse risk eValuation in acute biliary pancreatitis: The deep learning MINERVA study protocol.
World journal of emergency surgery : WJES
Mar 3, 2025
BACKGROUND: Mild acute biliary pancreatitis (MABP) presents significant clinical and economic challenges due to its potential for relapse. Current guidelines advocate for early cholecystectomy (EC) during the same hospital admission to prevent recurr...
Development and validation of a deep learning algorithm for prediction of pediatric recurrent intussusception in ultrasound images and radiographs.
BMC medical imaging
Mar 3, 2025
PURPOSES: To develop a predictive model for recurrent intussusception based on abdominal ultrasound (US) images and abdominal radiographs.
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