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Unsupervised machine learning identifies biomarkers of disease progression in post-kala-azar dermal leishmaniasis in Sudan.

PLoS neglected tropical diseases
BACKGROUND: Post-kala-azar dermal leishmaniasis (PKDL) appears as a rash in some individuals who have recovered from visceral leishmaniasis caused by Leishmania donovani. Today, basic knowledge of this neglected disease and how to predict its progres...

Prediction of Hypertension in the Pediatric Population Using Machine Learning and Transfer Learning: A Multicentric Analysis of the SAYCARE Study.

International journal of public health
OBJECTIVE: To develop a machine learning (ML) model utilizing transfer learning (TL) techniques to predict hypertension in children and adolescents across South America.

Predicting the immunological nonresponse to antiretroviral therapy in people living with HIV: a machine learning-based multicenter large-scale study.

Frontiers in cellular and infection microbiology
BACKGROUND: Although highly active antiretroviral therapy (HAART) has greatly enhanced the prognosis for people living with HIV (PLWH), some individuals fail to achieve adequate immune reconstitution, known as immunological nonresponse (INR), which i...

Multimodal deep learning for predicting PD-L1 biomarker and clinical immunotherapy outcomes of esophageal cancer.

Frontiers in immunology
Although the immune checkpoint inhibitors (ICIs) have demonstrated remarkable anti-tumor efficacy in solid tumors, the proportion of ESCC patients who benefit from ICIs remains limited. Current biomarkers have assisted in identifying potential respon...

Self-training EEG discrimination model with weakly supervised sample construction: An age-based perspective on ASD evaluation.

Neural networks : the official journal of the International Neural Network Society
Deep learning for Electroencephalography (EEG) has become dominant in the tasks of discrimination and evaluation of brain disorders. However, despite its significant successes, this approach has long been facing challenges due to the limited availabi...

Nano biosensor unlocks tumor derived immune signals for the early detection of ovarian cancer.

Biosensors & bioelectronics
Ovarian cancer is a critical health issue for women nowadays. Its impact is significant because of its high mortality rate (324,603 worldwide), late-stage diagnosis and poor survival rate. Lack of screening tests, vague symptoms, misdiagnosis, and ag...

Artificial intelligence: Friend or foe in the assessment of dental students?

Journal of dentistry
OBJECTIVES: Incorporating artificial intelligence (AI) in assessing dental students' knowledge and skills is in its infancy, despite AI being well established as an aid to aspects of clinical diagnosis and education. This study aimed to investigate w...

Enhancing pancreatic cancer diagnostics: Ensemble-based model for automated urine biomarker classification.

Computers in biology and medicine
This research addresses the critical challenge of early detection in pancreatic ductal adenocarcinoma (PDAC) by exploring urinary biomarkers and integrating artificial intelligence (AI) models. The study emphasizes the significance of liquid biopsy, ...

Predicting pregnancy at the first year following metabolic-bariatric surgery: development and validation of machine learning models.

Surgical endoscopy
BACKGROUND: Metabolic-bariatric surgery (MBS) is the last effective way to lose weight whom around half of the patients are women of reproductive age. It is recommended an interval of 12 months between surgery and pregnancy to optimize weight loss an...

Does perceived voluntariness of others' actions induce vicarious sense of agency? Evidence from human-robot interaction.

Consciousness and cognition
Sense of Agency (SoA) is the feeling of control over one's actions and outcomes. People can experience "vicarious" SoA towards other agents, either other humans or artificial agents such as robots. A commonly used measure of implicit SoA is the Inten...