WHITE

The Recurrent Neural Network Processor.

Excellent Medical Language Understanding

Handles long medical texts containing jargons, acronyms, and highly specialized terminologies that can be challenging for traditional summarization methods.

Enhanced Performance

WHITE can handle large amounts of medical text data - enabling the automatic generation of summaries, which drastically improves efficiency & reduces the need for human labor.

Clinical Version

Rapid Care introduces summarization a patient’s medical record on the fly in an adhoc manner which is valuable in clinical settings where time is of the essence.

High Quality Training data set

Proven with 25+ years of quality track record in KPO & Medical Record Reviews our Experts have hand crafted labeled dataset with medical documents paired with their summaries. For example, clinical notes paired with concise summaries or abstracts of medical research papers. WHITE RNN excels in domain specific medical text with precision handling jargon, abbreviations, and terminologies that are specific to the domain. We in the pursuit of further training of our WHITE RNN model on a customised medical corpus to improve WHITE's understanding of the medical context & business grammar.

RNN with Sequential Memory

Medical documents, such as clinical notes, discharge summaries and other patient data related to legal or claims processing tend to have complex, sequential structures. Information presented in the large medical documents will depend on earlier parts of the file, making sequential modeling essential. This is where WHITE as an RNN engine shine, as it is designed to process sequences of health care data step by step,maintaining an internal state (memory) of previous variables.

Computer Science Features for Records Review

Sequential Data

Medical texts are often interdependent; for instance, a diagnosis mentioned at the beginning of a clinical note may need to be connected with treatment steps or outcomes mentioned later.

Long-term Context

In long medical records - later sentences or paragraphs often depend on earlier ones for full understanding. WHITE have the ability to "remember" this context over multiple time steps.

Bidirectional RNN

WHITE processes the text in both directions (forward and backward), capturing dependencies in both directions of the text. This is useful in medical summarization, where understanding both the context before and after a phrase is crucial.

Concept Extraction

The primary role of WHITE as an RNN in the Medical Summarization context is to extract important local features from the given input text from Operating Platforms like Rapid Care's TEXTURE. This is especially useful for understanding context or extracting key phrases that may be critical in medical summaries.

Layer Pooling

After successful extraction WHITE RNN's pooling layers reduce the dimensionality of the feature map while retaining the important aspects of the learned features from the given MEDICAL DATA SET. This helps in focusing on more significant elements of the text, reducing noise and unnecessary details for Medical Records summarization.

Integrated Neural Network

WHITE ensures summarizing long-form of medical text (especially without losing key information) by integrating few proprietary Transformers for better understanding of text semantics and long-term dependencies in Health care records.