Towards unstructured mortality prediction with free-text clinical notes
نویسندگان
چکیده
منابع مشابه
Parsing Free Text Nursing Notes
Parsing nursing notes requires tokenization, recognition of special forms, abbreviation expansion, and classification in the context of identified sections
متن کاملCategorizing Medications from Unstructured Clinical Notes
One of the important pieces of information in a patient's clinical record is the information about their medications. Besides administering information, it also consists of the category of the medication i.e. whether the patient was taking these medications at Home, were administered in the Emergency Department, during course of stay or on discharge etc. Unfortunately, much of this information ...
متن کاملIdentification of Clinical Characteristics of Large Patient Cohorts through Analysis of Free Text Physician Notes
Background A number of important applications in medicine and biomedical research, including quality of care surveillance and identification of prospective study subjects, require identification of large cohorts of patients with specific clinical characteristics. Currently used conventional techniques are either labor-intensive or imprecise, while natural language processing-based applications ...
متن کاملSurvey of Clinical Knowledge Management and Analysis of Unstructured Text
[0001] Today a large portion of biomedical information can be accessed electronically. However, research articles, technical reports, clinical notes, and many other sources of information are stored as free-form text not suited for quantitative analysis. As the amount of data grow everyday, it becomes increasingly difficult to make use of the wealth of valuable knowledge that is hidden in these...
متن کاملA Comparison of Dimensionality Reduction Techniques for Unstructured Clinical Text
Much of clinical data is free text, which is challenging to use together with machine learning, visualization tools, and clinical decision rules. In this paper, we compare supervised and unsupervised dimensionality reduction techniques, including the recently proposed sLDA and MedLDA algorithms, on clinical texts. We evaluate each dimensionality reduction method by using them as features for tw...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Biomedical Informatics
سال: 2020
ISSN: 1532-0464
DOI: 10.1016/j.jbi.2020.103489