Abstract. We propose a pipelined system for the automatic classification of medical documents according to their language (English, Spanish and German) and their target user group (medical experts vs. health care consumers). We use a simple n-gram based categorization model and present experimental results fo
This paper addresses a real world problem: the classification of text documents in the medical domai...
Medical texts (reports, articles, etc.) are usually written by professionals (physicians, medical re...
Medical texts (reports, articles, etc.) are usually written by professionals (physicians, medical re...
Consumer health information written by health care professionals is often inaccessible to the consum...
Consumer health information written by health care professionals is often inaccessible to the consum...
Automatic extraction of knowledge from large corpus of texts is an essential step toward linguistic ...
AbstractThis research proposes a novel lexical approach to text categorization in the bio-medical do...
Healthcare domain is characterized by a huge amount of data, contained in medical records, reports, ...
Healthcare domain is characterized by a huge amount of data, contained in medical records, reports, ...
Healthcare domain is characterized by a huge amount of data, contained in medical records, reports, ...
We introduce a multi-label text classifier with per-label attention for the classification of Electr...
We introduce a multi-label text classifier with per-label attention for the classification of Electr...
Abstract. In this paper we address some aspects of the hard problem of information extraction, model...
Many medical narratives are read by care professionals in their preferred language. These documents ...
This paper addresses a real world problem: the classification of text documents in the medical domai...
This paper addresses a real world problem: the classification of text documents in the medical domai...
Medical texts (reports, articles, etc.) are usually written by professionals (physicians, medical re...
Medical texts (reports, articles, etc.) are usually written by professionals (physicians, medical re...
Consumer health information written by health care professionals is often inaccessible to the consum...
Consumer health information written by health care professionals is often inaccessible to the consum...
Automatic extraction of knowledge from large corpus of texts is an essential step toward linguistic ...
AbstractThis research proposes a novel lexical approach to text categorization in the bio-medical do...
Healthcare domain is characterized by a huge amount of data, contained in medical records, reports, ...
Healthcare domain is characterized by a huge amount of data, contained in medical records, reports, ...
Healthcare domain is characterized by a huge amount of data, contained in medical records, reports, ...
We introduce a multi-label text classifier with per-label attention for the classification of Electr...
We introduce a multi-label text classifier with per-label attention for the classification of Electr...
Abstract. In this paper we address some aspects of the hard problem of information extraction, model...
Many medical narratives are read by care professionals in their preferred language. These documents ...
This paper addresses a real world problem: the classification of text documents in the medical domai...
This paper addresses a real world problem: the classification of text documents in the medical domai...
Medical texts (reports, articles, etc.) are usually written by professionals (physicians, medical re...
Medical texts (reports, articles, etc.) are usually written by professionals (physicians, medical re...