Natural Language Processing in Healthcare

Natural Language Processing in Healthcare

The importance of incorporating Natural Language Processing (NLP) methods into clinical informatics research has been increasingly recognized in recent years and has led to transformative advances. The development of health information systems using artificial intelligence algorithms can increase health care efficiency by organizing health records, decreasing health costs and increasing the quality of care.

Clinical files contain multiple types of patient information, such as patient status, history, reports, applied treatments, medications used, diagnoses or discharge summary. Doctors record this information in free texts. Information such as observation notes and physician opinions contained in these free texts can provide many benefits to clinical research against diseases. However, since this information is in free text format, it is not easily processed by computers. At this point, Natural Language Processing (NLP) aims to classify and organize natural language using algorithms that can analyze and process the use of natural language.

What is Natural Language Processing?

Natural Language Processing (NLP) is the field of cognitive linguistics that enables artificial intelligence to recognize the use of natural language. It enables spoken languages to be processed and used by machines. It aims to understand, decipher and transform the daily use of human language into data. NLP makes use of machine learning technology.

Machine Learning in Healthcare

NLP in Healthcare

Collecting patient and disease data from all over the world and making them a structure can provide many advantages for the healthcare sector. While technology always reveals more than expected, some of these advantages are as follows;

Cohort Analysis

The “old-fashioned” methods of defining a cohort, in clinical trials, is a difficult and lengthy process for many researchers to obtain large-scale data. Using NLP can significantly reduce the financial and intangible costs of cohort creation. Structured clinical data provides researchers an easy, scaled, and filtered form that can be very convenient in terms of cohort formation.

Decision Making and Fast Action

Patient profiles contain important data from their physicians. This information is important for researchers to increase the quality of clinical reports, offering suggestions and roadmaps for advanced treatments. The integrity of the patient profile is ensured to achieve better medical monitoring.

Highly Qualified Clinical Trials

In studies conducted for patients with specific medical conditions, obtaining frequent regular data, helps in determining the patient’s condition and comparing physician observations, therefore; predicting the patient’s future quality of life.

Pharmaceutical Research

The use of NLP is expected to greatly influence drug research. Access to big data, regarding the effect of a drug in development, will speed up the research considerably. In addition, after the drug is put into use, its effectiveness, treatments or side effects can be monitored.

Clinical Trial Pre-screening with Artificial Intelligence

Building Cohort with NLP

Many new steps are being taken to make electronic health records (EHRs) widely available. It is not sufficient to use only diagnostic codes and procedure codes for the progress of clinical trials and cohort identification. Detailed patient information, doctor’s comments and observations are of great importance. Transferring this information to big data using the NLP technique is an important development for cohort formation. Contact Massive Bio for more information on how NLP is used to more effectively treat cancer patients.

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