AIMC Topic: Female

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Named Entity Recognition for Chinese Cancer Electronic Health Records-Development and Evaluation of a Domain-Specific BERT Model: Quantitative Study.

JMIR medical informatics
BACKGROUND: The unstructured data of Chinese cancer electronic health records (EHRs) contains valuable medical expertise. Accurate medical entity recognition is crucial for building a medical-assisted decision system. Named entity recognition (NER) i...

Evaluation of Cancer Survivors' Experience of Using AI-Based Conversational Tools: Qualitative Study.

JMIR cancer
BACKGROUND: Cancer survivorship is a complicated, chronic, and long-lasting experience, causing uncertainty and a wide range of physical and emotional health concerns. Due to the complexity of cancer, patients often seek out multiple sources of healt...

Mapping neurophysiological and molecular profiles of heterogeneity and homogeneity in schizophrenia-bipolar disorder.

Science advances
The heterogeneity of psychotic disorders leads to instability in subjectively defined diagnoses. This study used a machine learning framework termed common orthogonal basis extraction (COBE) to decompose electroencephalography-based functional connec...

Relationship between cognitive abilities and mental health as represented by cognitive abilities at the neural and genetic levels of analysis.

eLife
Cognitive abilities are closely tied to mental health from early childhood. This study explores how neurobiological units of analysis of cognitive abilities-multimodal neuroimaging and polygenic scores (PGS)-represent this connection. Using data from...

Multimodal prediction of psychotic-like experiences using elastic net modeling: external validation in a clinical sample.

Psychological medicine
BACKGROUND: Psychotic-like experiences (PLEs) are considered a subclinical component of psychosis continuum. Studies indicate that PLEs arise from multimodal factors, yet research comprehensively examining these factors together remains scarce. Using...

Applied machine learning for nociceptive pain detection using EEG spectral features.

Biomedical physics & engineering express
. This study explores a more reliable method for measuring nociceptive pain induced by laser stimuli from electroencephalography (EEG) signals, addressing the limitations of fixed pain scales by incorporating inter-individual variability in subjectiv...

Predicting 30-day and 1-year mortality in heart failure with preserved ejection fraction (HFpEF).

PloS one
OBJECTIVES: To develop and compare prediction models for 30-day and 1-year mortality in Heart failure with preserved ejection fraction (HFpEF) using EHR data, utilizing both traditional and machine learning (ML) techniques.

MRI-based 2.5D deep learning and radiomics effectively predicted microvascular invasion and Ki-67 expression in hepatocellular carcinoma.

PloS one
OBJECTIVE: To develop and validate an integrated 2.5D deep learning (DL) and Radiomics model using gadoxetic acid-enhanced MRI hepatobiliary phase (HBP) images combined with clinical features for preoperative prediction of microvascular invasion (MVI...

Gender-specific effectiveness of dialectic-behavioral therapy for patients with complex post-traumatic stress disorder (DBT-PTSD) - results of an observational single center study.

European journal of psychotraumatology
Complex post-traumatic stress disorder (cPTSD) was recently included in the ICD-11, extending the PTSD symptom profile to encompass disturbances in self-organization (DSO). Trauma-focused Dialectical Behavior Therapy (DBT-PTSD) is an effective psych...

DART Predictor: A Multi-Label Attention Model for High-Throughput Screening of Chemicals with Developmental and Reproductive Toxicity (DART).

Environmental science & technology
Chemicals with developmental and reproductive toxicity (DART) pose significant risks to human health, particularly exposure during critical windows of embryonic and fetal development. Therefore, rapid and accurate identification of DART chemicals is ...