AIMC Topic: Humans

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TARSL: Triple-Attention Cross-Network Representation Learning to Predict Synthetic Lethality for Anti-Cancer Drug Discovery.

IEEE journal of biomedical and health informatics
Cancer is a multifaceted disease that results from co-mutations of multi biological molecules. A promising strategy for cancer therapy involves in exploiting the phenomenon of Synthetic Lethality (SL) by targeting the SL partner of cancer gene. Since...

An optimized machine learning framework for predicting and interpreting corporate ESG greenwashing behavior.

PloS one
The accurate prediction and interpretation of corporate Environmental, Social, and Governance (ESG) greenwashing behavior is crucial for enhancing information transparency and improving regulatory effectiveness. This paper addresses the limitations i...

Online Unsupervised Adaptation of Latent Representation for Myoelectric Control During User-Decoder Co-Adaptation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Myoelectric control interfaces, which map electromyographic (EMG) signals into control commands for external devices, have applications in active prosthesis control. However, the statistical characteristics of EMG signals change over time (e.g., beca...

Effect of Wearable Robot-Assisted Gait Training on Balance and Walking Ability in Subacute Stroke Patients.

American journal of physical medicine & rehabilitation
OBJECTIVE: This study investigated the effectiveness of wearable robot-assisted gait training compared to treadmill gait training for improving balance and walking ability in stroke patients.

High-Adhesive Hydrogel-Based Strain Sensor in the Clinical Diagnosis of Anterior Talofibular Ligament Sprain.

ACS sensors
Anterior talofibular ligament (ATFL) sprain is one of the most prevalent sports-related injuries, so proper evaluation of ligament sprains is critical for treatment options. However, existing tests suffer from a lack of standardized quantitative eval...

Assessing the performance of large language models (GPT-3.5 and GPT-4) and accurate clinical information for pediatric nephrology.

Pediatric nephrology (Berlin, Germany)
BACKGROUND: Artificial intelligence (AI) has emerged as a transformative tool in healthcare, offering significant advancements in providing accurate clinical information. However, the performance and applicability of AI models in specialized fields s...

Artificial intelligence for the detection of airway nodules in chest CT scans.

European radiology
OBJECTIVES: Incidental airway tumors are rare and can easily be overlooked on chest CT, especially at an early stage. Therefore, we developed and assessed a deep learning-based artificial intelligence (AI) system for detecting and localizing airway n...

Controlling nutritional status score predicts posthepatectomy liver failure: an online interpretable machine learning prediction model.

European journal of gastroenterology & hepatology
BACKGROUND AND AIMS: Posthepatectomy liver failure (PHLF) remains a severe complication after hepatectomy for hepatocellular carcinoma (HCC) and accurate preoperative evaluation and predictive measures are urgently needed. We investigated the impact ...

Development of Machine Learning Models for Predicting Radiation Dermatitis in Breast Cancer Patients Using Clinical Risk Factors, Patient-Reported Outcomes, and Serum Cytokine Biomarkers.

Clinical breast cancer
BACKGROUND: Radiation dermatitis (RD) is a significant side effect of radiotherapy experienced by breast cancer patients. Severe symptoms include desquamation or ulceration of irradiated skin, which impacts quality of life and increases healthcare co...