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Hemophilia A

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Machine learning method using position-specific mutation based classification outperforms one hot coding for disease severity prediction in haemophilia 'A'.

Genomics
Haemophilia is an X-linked genetic disorder in which A and B types are the most common that occur due to absence or lack of protein factors VIII and IX, respectively. Severity of the disease depends on mutation. Available Machine Learning (ML) method...

Deep compartment models: A deep learning approach for the reliable prediction of time-series data in pharmacokinetic modeling.

CPT: pharmacometrics & systems pharmacology
Nonlinear mixed effect (NLME) models are the gold standard for the analysis of patient response following drug exposure. However, these types of models are complex and time-consuming to develop. There is great interest in the adoption of machine-lear...

The current role of artificial intelligence in hemophilia.

Expert review of hematology
INTRODUCTION: The utilization of artificial intelligence (AI) in hemophilia is still in its early phases.

Design, development and usability of an educational AI chatbot for People with Haemophilia in Senegal.

Haemophilia : the official journal of the World Federation of Hemophilia
INTRODUCTION: Gaps in the disease knowledge of People with Haemophilia (PWH) in Senegal are important barriers to the effective management of haemophilia. Digital health systems for chronic diseases in low- and middle-income countries are suggested t...

Application of machine learning approaches for predicting hemophilia A severity.

Journal of thrombosis and haemostasis : JTH
BACKGROUND: Hemophilia A (HA) is an X-linked congenital bleeding disorder, which leads to deficiency of clotting factor (F) VIII. It mostly affects males, and females are considered carriers. However, it is now recognized that variants of F8 in femal...

Leveraging domain knowledge for synthetic ultrasound image generation: a novel approach to rare disease AI detection.

International journal of computer assisted radiology and surgery
PURPOSE: This study explores the use of deep generative models to create synthetic ultrasound images for the detection of hemarthrosis in hemophilia patients. Addressing the challenge of sparse datasets in rare disease diagnostics, the study aims to ...

Applying artificial intelligence to uncover the genetic landscape of coagulation factors.

Journal of thrombosis and haemostasis : JTH
Artificial intelligence (AI) is rapidly advancing our ability to identify and interpret genetic variants associated with coagulation factor deficiencies. This review introduces AI, with a specific focus on machine learning (ML) methods, and examines ...