Deep learning--based models have surpassed classical machine learning--based
approaches in various text classification tasks, including sentiment analysis, news …

Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …

Deep learning is a branch of artificial intelligence where networks of simple interconnected
units are used to extract patterns from data in order to solve complex problems. Deep …

ICD coding is a process of assigning the International Classification of Disease diagnosis
codes to clinical/medical notes documented by health professionals (eg clinicians). This …

The social media is becoming an increasing trend for sharing the thoughts, ideas, opinions,
etc. based on online reviews which generates a tremendous amount of unstructured data …

The recent literature in text classification is biased towards short text sequences (eg,
sentences or paragraphs). In real-world applications, multi-page multi-paragraph documents …

Current models for correlating electronic medical records with-omics data largely ignore
clinical text, which is an important source of phenotype information for patients with cancer …

The widespread use of electronic health records (EHR) systems in health care provides a
large amount of real-world data, leading to new areas for clinical research. Natural language …

Clinical coding is the task of transforming medical information in a patient's health records
into structured codes so that they can be used for statistical analysis. This is a cognitive and …

Background Diagnostic or procedural coding of clinical notes aims to derive a coded
summary of disease-related information about patients. Such coding is usually done …