AIMC Topic: Cohort Studies
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Predicting Biochemical and Physiological Parameters: Deep Learning from IgG Glycome Composition.
International journal of molecular sciences
Sep 16, 2024
In immunoglobulin G (IgG), -glycosylation plays a pivotal role in structure and function. It is often altered in different diseases, suggesting that it could be a promising health biomarker. Studies indicate that IgG glycosylation not only associates...
Machine Learning in Clinical Diagnosis of Head and Neck Cancer.
Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery
Sep 14, 2024
OBJECTIVE: Machine learning has been effective in other areas of medicine, this study aims to investigate this with regards to HNC and identify which algorithm works best to classify malignant patients.
Development and Validation of a Deep Learning Model for Prediction of Adult Physiological Deterioration.
Critical care explorations
Sep 11, 2024
BACKGROUND: Prediction-based strategies for physiologic deterioration offer the potential for earlier clinical interventions that improve patient outcomes. Current strategies are limited because they operate on inconsistent definitions of deteriorati...
Preoperative treatment response prediction for pancreatic cancer by multiple microRNAs in plasma exosomes: Optimization using machine learning and network analysis.
Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]
Sep 10, 2024
BACKGROUND/OBJECTIVES: MicroRNAs (miRNAs) are involved in chemosensitivity through their biological activities in various malignancies, including pancreatic cancer (PC). However, single-miRNA models offer limited predictability of treatment response....
Implementing machine learning to predict survival outcomes in patients with resected pulmonary large cell neuroendocrine carcinoma.
Expert review of anticancer therapy
Sep 9, 2024
BACKGROUND: The post-surgical prognosis for Pulmonary Large Cell Neuroendocrine Carcinoma (PLCNEC) patients remains largely unexplored. Developing a precise prognostic model is vital to assist clinicians in patient counseling and creating effective t...
A time-dependent explainable radiomic analysis from the multi-omic cohort of CPTAC-Pancreatic Ductal Adenocarcinoma.
Computer methods and programs in biomedicine
Sep 7, 2024
BACKGROUND AND OBJECTIVE: In Pancreatic Ductal Adenocarcinoma (PDA), multi-omic models are emerging to answer unmet clinical needs to derive novel quantitative prognostic factors. We realized a pipeline that relies on survival machine-learning (SML) ...
Application of Machine Learning Algorithms for Risk Stratification and Efficacy Evaluation in Cervical Cancer Screening among the ASCUS/LSIL Population: Evidence from the Korean HPV Cohort Study.
Cancer research and treatment
Sep 6, 2024
PURPOSE: We assessed human papillomavirus (HPV) genotype-based risk stratification and the efficacy of cytology testing for cervical cancer screening in patients with atypical squamous cells of undetermined significance (ASCUS)/low-grade squamous int...
Handling missing data and measurement error for early-onset myopia risk prediction models.
BMC medical research methodology
Sep 6, 2024
BACKGROUND: Early identification of children at high risk of developing myopia is essential to prevent myopia progression by introducing timely interventions. However, missing data and measurement error (ME) are common challenges in risk prediction m...
Machine learning to identify precachexia and cachexia: a multicenter, retrospective cohort study.
Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Sep 3, 2024
BACKGROUND: Detection of precachexia is important for the prevention and treatment of cachexia. However, how to identify precachexia is still a challenge.
Discrepancies in ASPECTS obtained by artificial intelligence and experts: Associated factors and prognostic implications.
European journal of radiology
Aug 28, 2024
PURPOSE: The differences between the Alberta Stroke Program Early CT Score (ASPECTS) obtained by experts and artificial intelligence (AI) software require elucidation. We aimed to characterize the discrepancies between the ASPECTS obtained by AI and ...
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