Enhancing post-traumatic stress disorder patient assessment: leveraging natural language processing for research of domain criteria identification using electronic medical records.

Background: Extracting research of domain criteria (RDoC) from high-risk populations like those with post-traumatic stress disorder (PTSD) is crucial for positive mental health improvements and policy enhancements. The intricacies of collecting, integrating, and effectively leveraging clinical notes for this purpose introduce complexities.

Methods: In our study, we created a natural language processing (NLP) workflow to analyze electronic medical record (EMR) data and identify and extract research of domain criteria using a pre-trained transformer-based natural language model, all-mpnet-base-v2.

Enhancing post-traumatic stress disorder patient assessment: Leveraging Natural Language Processing for Research of Domain Criteria Identification using electronic medical records.

Background: Extracting research of domain criteria (RDoC) from high-risk populations like those with post-traumatic stress disorder (PTSD) is crucial for positive mental health improvements and policy enhancements. The intricacies of collecting, integrating, and effectively leveraging clinical notes for this purpose introduce complexities.

Methods: In our study, we created an NLP workflow to analyze electronic medical record (EMR) data, and identify and extract research of domain criteria using a pre-trained transformer-based natural language model, allmpnet-base-v2.

DeepBiomarker2: Prediction of Alcohol and Substance Use Disorder Risk in Post-Traumatic Stress Disorder Patients Using Electronic Medical Records and Multiple Social Determinants of Health.

Prediction of high-risk events amongst patients with mental disorders is critical for personalized interventions. We developed DeepBiomarker2 by leveraging deep learning and natural language processing to analyze lab tests, medication use, diagnosis, social determinants of health (SDoH) parameters, and psychotherapy for outcome prediction. To increase the model's interpretability, we further refined our contribution analysis to identify key features by scaling with a factor from a reference feature.

Unveiling the Enigma: Exploring Risk Factors and Mechanisms for Psychotic Symptoms in Alzheimer's Disease through Electronic Medical Records with Deep Learning Models.

Around 50% of patients with Alzheimer's disease (AD) may experience psychotic symptoms after onset, resulting in a subtype of AD known as psychosis in AD (AD + P). This subtype is characterized by more rapid cognitive decline compared to AD patients without psychosis. Therefore, there is a great need to identify risk factors for the development of AD + P and explore potential treatment options.

Three dimensional engineered models to study hypoxia biology in breast cancer.

Breast cancer is the second leading cause of mortality among women worldwide. Despite the available therapeutic regimes, variable treatment response is reported among different breast cancer subtypes. Recently, the effects of the tumor microenvironment on tumor progression as well as treatment responses have been widely recognized.

Status of Agents Targeting the HGF/c-Met Axis in Lung Cancer.

Hepatocyte growth factor (HGF) is the ligand for the tyrosine kinase receptor c-Met (Mesenchymal Epithelial Transition Factor also known as Hepatocyte Growth Factor Receptor, HGFR), a receptor with expression throughout epithelial and endothelial cell types. Activation of c-Met enhances cell proliferation, invasion, survival, angiogenesis, and motility. The c-Met pathway also stimulates tissue repair in normal cells.

Recent Advancemnts in Biodegradable Ocular Implants.

The perpetuation of healthy vision is paramount in an individual. It has been observed that various drug delivery systems have been fabricated to develop vision quality in individuals. Systemic ocular drug therapies have limited efficacy due to poor bioavailability, systemic and toxic side effects and low patient compliance.