Bipolar Disorder

A Self-Oscillated Organic Synapse for In-Memory Two-Factor Authentication.

Entering the era of AI 2.0, bio-inspired target recognition facilitates life. However, target recognition may suffer from some risks when the target is hijacked. Therefore, it is significantly important to provide an encryption process prior to neuromorphic computing. In this work, enlightened from time-varied synaptic rule, an in-memory asymmetric encryption as pre-authentication is utilized with...

Efficient Spiking Neural Networks with Biologically Similar Lithium-Ion Memristor Neurons.

Benefiting from the brain-inspired event-driven feature and asynchronous sparse coding approach, spiking neural networks (SNNs) are becoming a potentially energy-efficient replacement for conventional artificial neural networks. However, neuromorphic devices used to construct SNNs persistently result in considerable energy consumption owing to the absence of sufficient biological parallels. Drawin...

Microfracture-coagulation for the real robotic liver parenchymal transection.

The use of the robotic approach in liver surgery is exponentially increasing. Although technically the robot introduces several innovative features, t...

Robot therapy aids mental health in patients with hematological malignancy during hematopoietic stem cell transplantation in a protective isolation unit.

Patients with hematological malignancy experience physical and psychological pain, such as a sense of isolation and confinement due to intensive chemo...

Exploring the Use of Socially Assistive Robots Among Socially Isolated Korean American Older Adults.

This pilot study explored whether a socially assistive robot (SAR) would have positive effects on Korean American immigrant older adults' health behav...

Explainable multimodal prediction of treatment-resistance in patients with depression leveraging brain morphometry and natural language processing.

Although 20 % of patients with depression receiving treatment do not achieve remission, predicting treatment-resistant depression (TRD) remains challe...

Prediction of treatment response in major depressive disorder using a hybrid of convolutional recurrent deep neural networks and effective connectivity based on EEG signal.

In this study, we have developed a novel method based on deep learning and brain effective connectivity to classify responders and non-responders to s...

A bipolar intuitionistic fuzzy decision-making model for selection of effective diagnosis method of tuberculosis.

OBJECTIVES: Tuberculosis (TB) is a contagious illness caused by Mycobacterium tuberculosis. The initial symptoms of TB are similar to other respirator...

Comment on published article "A chat about bipolar disorder".

To the Editor, we follow the topic "A chat about bipolar disorder ". According to the study's findings, ChatGPT proved its ability to deliver basic an...

Effectiveness of animal-assisted therapy and pet-robot interventions in reducing depressive symptoms among older adults: A systematic review and meta-analysis.

BACKGROUND: Systematic reviews suggest that animal-assisted therapy (AAT) and pet-robot interventions (PRI) achieve a reduction in mental health varia...

Illusory generalizability of clinical prediction models.

It is widely hoped that statistical models can improve decision-making related to medical treatments. Because of the cost and scarcity of medical outc...

Machine learning for data-driven design of high-safety lithium metal anode.

Here, we present a protocol for developing an inorganic-organic hybrid interphase layer using the self-assembled monolayers technique to enhance the s...

Assessing prognosis in depression: comparing perspectives of AI models, mental health professionals and the general public.

BACKGROUND: Artificial intelligence (AI) has rapidly permeated various sectors, including healthcare, highlighting its potential to facilitate mental ...

Interpretable deep learning model for major depressive disorder assessment based on functional near-infrared spectroscopy.

BACKGROUND: Major depressive disorder (MDD) affects a substantial number of individuals worldwide. New approaches are required to improve the diagnosi...

Towards Personalised Mood Prediction and Explanation for Depression from Biophysical Data.

Digital health applications using Artificial Intelligence (AI) are a promising opportunity to address the widening gap between available resources and...

Prediction of patient admission and readmission in adults from a Colombian cohort with bipolar disorder using artificial intelligence.

INTRODUCTION: Bipolar disorder (BD) is a chronically progressive mental condition, associated with a reduced quality of life and greater disability. P...

Deconstructing depression by machine learning: the POKAL-PSY study.

Unipolar depression is a prevalent and disabling condition, often left untreated. In the outpatient setting, general practitioners fail to recognize d...

Development of depression assessment tools using humanoid robots -Can tele-operated robots talk with depressive persons like humans?

BACKGROUND: Depression is a common mental disorder and causes significant social loss. Early intervention for depression is important. Nonetheless, de...

Robotic pancreatic tumor enucleation by the double bipolar technique using the da Vinci SP system: An initial case report with a technical detail.

Pancreatic tumor enucleation is a procedure that can preserve pancreatic function and is sometimes performed using a minimally invasive approach. Rece...

Toward explainable AI (XAI) for mental health detection based on language behavior.

Advances in artificial intelligence (AI) in general and Natural Language Processing (NLP) in particular are paving the new way forward for the automat...