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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
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...

Artificial Intelligence-Guided Bronchoscopy is Superior to Human Expert Instruction for the Performance of Critical-Care Physicians: A Randomized Controlled Trial.

Critical care medicine
OBJECTIVES: Bronchoscopy in the mechanically ventilated patient is an important skill for critical-care physicians. However, training opportunity is heterogenous and limited by infrequent caseload or inadequate instructor feedback for satisfactory co...

A CT-based deep learning-driven tool for automatic liver tumor detection and delineation in patients with cancer.

Cell reports. Medicine
Liver tumors, whether primary or metastatic, significantly impact the outcomes of patients with cancer. Accurate identification and quantification are crucial for effective patient management, including precise diagnosis, prognosis, and therapy evalu...

Generating accurate sex estimation from hand X-ray images using AI deep-learning techniques: A study of limited bone regions.

Legal medicine (Tokyo, Japan)
Hand bone structure provides valuable features for sex estimation. This research introduces a novel approach using Artificial Intelligence (AI), specifically Convolutional Neural Networks (CNNs), to classify sex from hand X-ray images, focusing on th...

Artificial intelligence learns to reason.

Science (New York, N.Y.)
Julia has two sisters and one brother. How many sisters does her brother Martin have? Solving this tiny puzzle requires a bit of thinking. You might mentally picture the family of three girls and one boy and then realize that the boy has three sister...

PyGlaucoMetrics: A Stacked Weight-Based Machine Learning Approach for Glaucoma Detection Using Visual Field Data.

Medicina (Kaunas, Lithuania)
: Glaucoma (GL) classification is crucial for early diagnosis and treatment, yet relying solely on stand-alone models or International Classification of Diseases (ICD) codes is insufficient due to limited predictive power and inconsistencies in clini...

Development of PDAC diagnosis and prognosis evaluation models based on machine learning.

BMC cancer
BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is difficult to detect early and highly aggressive, often leading to poor patient prognosis. Existing serum biomarkers like CA19-9 are limited in early diagnosis, failing to meet clinical needs. Mac...

Predicting postoperative nausea and vomiting using machine learning: a model development and validation study.

BMC anesthesiology
BACKGROUND: Postoperative nausea and vomiting (PONV) is a frequently observed complication in patients undergoing surgery under general anesthesia. Moreover, it is a frequent cause of distress and dissatisfaction in the early postoperative period. Cu...

Comparison of MRI and CT based deep learning radiomics analyses and their combination for diagnosing intrahepatic cholangiocarcinoma.

Scientific reports
Intrahepatic cholangiocarcinoma (iCCA) and other subtypes of primary liver cancer (PLC) have overlapping clinical manifestations and radiological characteristics. The objective of this study was to evaluate the efficacy of deep learning (DL) radiomic...

Multidisciplinary characterization of embarrassment through behavioral and acoustic modeling.

Scientific reports
Embarrassment is a social emotion that shares many characteristics with social anxiety (SA). Most people experience embarrassment in their daily lives, but it is quite overlooked in research. We characterized embarrassment through an interdisciplinar...