International Classification of Disease (ICD) coding plays a significant role in classify-ing morbidity and mortality rates. Currently, ICD codes are assigned to patient’s medical record by hand practitioners or specialist clinical coders. This practice is prone errors, training skilled coders requires time human resources. Automatic prediction can help alleviate this burden. In paper...

The research in this paper provides generalization and new ideas for topics computer-assisted medicine. main improvement efforts deep learning-based multimodal fusion schemes, which provide alternative directions robust feature fitting performance are building complex structures, migrating knowledge or experience, processing enhancing data, targeting features semantic correction based on contex...

Three-dimensional medical imaging enables detailed understanding of osteoarthritis structural status. However, there remains a vast need for automatic, thus, reader-independent measures that provide reliable assessment subject-specific clinical outcomes. To this end, we derive consistent generalization the recently proposed B-score to Riemannian shape spaces. We further present an algorithmic t...

One of the proposed tasks of the ImageCLEF 2005 campaign has been an Automatic Annotation Task. The objective is to provide the classification of a given set of 1,000 previously unseen medical (radiological) images according to 57 predefined categories covering different medical pathologies. 9,000 classified training images are given which can be used in any way to train a classifier. The Autom...

In this notebook, we describe the automatic retrieval runs from NEC Laboratories America (NECLA) for the Text REtrieval Conference (TREC) 2012 Medical Records track. Our approach is based on a combination of UMLS medical concept detection and a set of simple retrieval models. Our best run, sennamed2, has achieved the best inferred average precision (infAP) score on 5 of the 47 test topics, and ...

In this notebook, we describe the automatic retrieval runs from NEC Laboratories America (NECLA) for the Text REtrieval Conference (TREC) 2012 Medical Records track. Our approach is based on a combination of UMLS medical concept detection and a set of simple retrieval models. Our best run, sennamed2, has achieved the best inferred average precision (infAP) score on 5 of the 47 test topics, and ...