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Prediction of preterm birth in multiparous women using logistic regression and machine learning approaches.

Scientific reports
To predict preterm birth (PTB) in multiparous women, comparing machine learning approaches with traditional logistic regression. A population-based cohort study was conducted using data from the Ontario Better Outcomes Registry and Network (BORN). Th...

Effects of Individual Research Practices on fNIRS Signal Quality and Latent Characteristics.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Functional near-infrared spectroscopy (fNIRS) is an increasingly popular tool for cross-cultural neuroimaging studies. However, the reproducibility and comparability of fNIRS studies is still an open issue in the scientific community. The paucity of ...

Machine learning-based prediction of the risk of moderate-to-severe catheter-related bladder discomfort in general anaesthesia patients: a prospective cohort study.

BMC anesthesiology
BACKGROUND: Catheter-related bladder discomfort (CRBD) commonly occurs in patients who have indwelling urinary catheters while under general anesthesia. And moderate-to-severe CRBD can lead to significant adverse events and negatively impact patient ...

Evaluation and analysis of risk factors for adverse events of the fractured vertebra post-percutaneous kyphoplasty: a retrospective cohort study using multiple machine learning models.

Journal of orthopaedic surgery and research
BACKGROUND: Adverse events of the fractured vertebra (AEFV) post-percutaneous kyphoplasty (PKP) can lead to recurrent pain and neurological damage, which considerably affect the prognosis of patients and the quality of life. This study aimed to analy...

Deep Learning Model for Pathological Grading and Prognostic Assessment of Lung Cancer Using CT Imaging: A Study on NLST and External Validation Cohorts.

Academic radiology
RATIONALE AND OBJECTIVES: To develop and validate a deep learning model for automated pathological grading and prognostic assessment of lung cancer using CT imaging, thereby providing surgeons with a non-invasive tool to guide surgical planning.

Predicting Biochemical and Physiological Parameters: Deep Learning from IgG Glycome Composition.

International journal of molecular sciences
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
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
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.]
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
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...