A systematic method for diagnosis of hepatitis disease using machine learning

Diagnostic challenge of cutis Verticis Gyrata (CVG) in a patient presenting clinical features of Noonan or turner syndrome

Cutis Verticis Gyrata (CVG) is an uncommon condition, often classified as primary (idiopathic) or secondary to other diseases or syndromes. Its pathogenesis remains poorly understood, and its association with genetic syndromes is particularly rare. Noonan and Turner syndromes are distinct genetic disorders with characteristic phenotypes and multiple systemic involvements. This report aims to highlight the diagnostic complexities when CVG presents in the backdrop of these syndromes. A 38 years old patient was presented with chief complaints of receding hairline, dropping eyelids, cerebral deformations with deep furrows and thickened dermis. On the basis of patient's complaints, Noonan or turner syndrome was considered as possible diagnosis. This particular report presents a case of patient suffering from CVG having history of noonan and turner syndrome. With the detailed MRI, histology etc. CVG was finally confirmed. The novelty of this case lies in its rarity, diagnostic complexity, and the need for a multidisciplinary approach to unravel and manage the intersecting conditions. It contributes valuable insights to the existing medical literature, enhancing our understanding of the interplay between dermatological and genetic conditions. Patients with Noonan and turner syndrome exhibit clinical signs and symptoms that are strikingly similar to those of CVG, suggesting that this presents a significant diagnostic problem. An unfavorable outcome could arise from delayed or incorrect diagnosis. Because of this, it is recommended that healthcare fraternities should include uncommon illnesses like CVG as differential diagnosis. Considering CVG in differential diagnosis is crucial for early identification, accurate diagnosis, and comprehensive management. It ensures that associated systemic and genetic conditions are not overlooked and that patients receive holistic and personalized care.

Novel graph-based machine-learning technique for viral infectious diseases: application to influenza and hepatitis diseases

BACKGROUND

Most infectious diseases are caused by viruses, fungi, bacteria and parasites. Their ability to easily infect humans and trigger large-scale epidemics makes them a public health concern. Methods for early detection of these diseases have been developed; however, they are hindered by the absence of a unified, interoperable and reusable model. This study seeks to create a holistic and real-time model for swift, preliminary detection of infectious diseases using symptoms and additional clinical data.

MATERIALS AND METHODS

In this study, we present a medical knowledge graph (MKG) that leverages multiple data sources to analyse connections between different nodes. Medical ontologies were used to enhance the MKG. We applied various graph algorithms to extract key features. The performance of multiple machine-learning (ML) techniques for influenza and hepatitis detection was assessed, selecting multi-layer perceptron (MLP) and random forest (RF) models due to their superior outcomes. The hyperparameters of both graph-based ML models were automatically fine-tuned.

RESULTS

Both the graph-based MLP and RF models showcased the least loss and error rates, along with the most specific, accurate recall, precision and F1 scores. Their Matthews correlation coefficients were also optimal. When compared with existing ML techniques and findings from the literature, these graph-based ML models manifested superior detection accuracy.

CONCLUSIONS

The graph-based MLP and RF models effectively diagnosed influenza and hepatitis, respectively. This underlines the potential of graph data science in enhancing ML model performance and uncovering concealed relationships in the MKG.