LITERATURE REVIEW - Development of natural language processing (NLP) techniques for extracting information from unstructured clinical notes.35 PAGES - WORD document15 SLIDES - PPT

Development of NLP Techniques      2
DEVELOPMENT OF NLP TECHNIQUES
Table of Contents
Literature Review    3
Introduction    3
Natural Language Processing    3
National Language Processing Techniques    7
Techniques for Extracting Information    9
Current State of Research in Terms of The Techniques    12
Pattern in the Development of NLP    13
Rule Based Approach    14
Machine Learning Based Approaches    16
Hybrid Approaches    20
Development and Use of NLP    22
Applications of NLP Techniques for Extracting Information from Unstructured Clinical Notes    25
Challenges and Limitations of NLP Techniques for Extracting Information from Unstructured Clinical Notes    28
Future Directions and Opportunities for NLP Techniques for Extracting Information from Unstructured Clinical Notes    32
Conclusion    35
References    36
Literature Review
Introduction
The goal of this literature review is to provide an understanding of the present state of the developments in NLP techniques for extracting information from unstructured clinical notes. The review will examine the different NLP techniques that have been used for this purpose, including Named Entity Recognition (NER), Information Extraction (IE), and Text Classification. In addition, the review will explore the challenges associated with developing and implementing these NLP techniques in clinical settings, such as variability in clinical language and the need for large amounts of annotated data. Furthermore, the review will discuss the potential benefits of using NLP techniques to extract information from unstructured clinical notes, including improved clinical decision-making, enhanced patient outcomes, and more efficient use of healthcare resources. Overall, this literature review will provide valuable insights into the current state of the art in NLP techniques for extracting information from unstructured clinical notes, as well as the challenges and opportunities associated with their implementation in healthcare settings.
Natural Language Processing
Unstructured clinical notes are a rich source of patient data, but they are typically written in free text format, making it challenging to extract useful information from them. NLP techniques can be used to automatically analyse and extract information from unstructured clinical notes. There are several NLP techniques that can be used for this purpose, including Named Entity Recognition (NER), Information Extraction (IE), and Text Classification. Named Entity Recognition (NER) is a technique used to identify and classify specific entities in text, such as diseases, symptoms, drugs, and procedures. This technique involves training a machine learning model on annotated data to recognize and label entities in new text (Zeng et al., 2020).
Information Extraction (IE) is a technique used to extract specific pieces of information from text, such as laboratory results or medication orders. This technique involves using rules or machine learning algorithms to identify relevant information in text and extract it into a structured format (Wyles et al., 2020).
Text Classification is a technique used to classify text into predefined categories, such as diagnosis or procedure codes. This technique involves training a machine learning model on annotated data to recognize patterns in text and classify new text into predefined categories.
These NLP techniques can be applied to various clinical settings, such as electronic health records (EHRs), clinical trials, and biomedical literature. They can help healthcare providers to identify important patient information, support clinical decision-making, and improve patient outcomes (Suh et al., 2020). The use of electronic health records (EHRs) has become increasingly widespread in healthcare settings. However, much of the information in these records is in unstructured format, making it difficult to extract and analyse. This has led to a growing interest in the development of NLP techniques to automatically extract information from unstructured clinical notes.
NLP is a field of study that is focused on enabling the machines to understand the human language and communicate with the people in their native language. In the health care Natural Language Processing has become an increasingly important element due to the first amount of clinical data that are generated by the Healthcare providers and which are primary stored in the unstructured format purchase clinical notes or pathology report. This particular data can valuable information about the patient care and the population health but extracting the meaningful insights from the constructor data can be challenging.
The natural language processing analyses and interpret information from the unstructured clinical data which can help care providers make more informed decisions and improved patient outcomes (Rahman et al., 2020). By automating the process of analysing and structured clinical data Natural Language Processing can help the help your providers to save time and reduce errors and improve the accuracy of diagnosis and treatment plans. Over the years researchers have been more concerned about the growing levels of the clinical data and this has given the aspect of understanding the relevance of natural language processing in the Healthcare which is expected to increase in the near future.
Natural Language Processing has the potential to transform the way Healthcare is delivered and improve the patient outcome which makes it a critical area of research and development in the field of healthcare. To be more specific as the literature has presented the context of Natural Language Processing is the fact that it is a software of the artificial intelligence that is concerned with the interactions between computer and human language which is more particular how computers processes and analyses a large amount of natural language data. Literature has often contemplated the importance of a natural language processing which has an effectively focus on structuring and the rules of natural language that has focus on creating the intelligent systems that are capable of a deriving the proper contacts of the data that has been naturally inserted in a particular document (Hatef, 2020).
It is obvious that a most of the resources has contemplated the need for the natural language processing techniques in order to analyse the recorded the unstructured data and get those data in a very structured format. It is also relevant to understand that in the field of Medical Science there are lot number of terms that are repeated more than once. In this aspect it is effective to under standard that to maintain their medical records for the better treatment and something that are similar the professionals need to maintain more patient a file which are unstructured (Hao et al., 2020). In this case it is effective to understand the importance of the natural language processing on the structure data and get useful information for the patient notes.
In most of the research papers researchers has concluded that clinical notes are an essential component of the patient record and are created by the Healthcare providers during the patient visits. These particular notes can contain a wealth of information about the patients’ medical history and other aspects like symptoms, diagnosis, treatment and outcomes. However clinical notes are typically on structure and written in natural language making them difficult to analyse and extract information from. It is obvious that natural language processing techniques can be used to extract valuable information from the unstructured clinical notes such as medication Orders and other aspects which can be readily available whenever necessary (Allen, 2019).
This information can be used to improve the patient outcomes and monitor the treatment effectiveness by providing support for the clinical decision making. The need for the natural language processing techniques to extract information from the unstructured clinical notes is given by the several factors that are focused by effectively concentrating on the aspects and the growing demand for the personalised medicine and the need for improving the efficiency and the quality of the health care delivery. The use of the techniques in terms of the natural language processing in healthcare is still at the very early stages but with the advances in technology and increasing and adoption by the health care providers the potential benefits of the particular process is significant. It is also relevant to understand that the researchers have pointed out that natural language processing has the potential to transform the Healthcare industry in the way it is delivered and by improving the patient outcomes making this particular field a critical area of research and development in the field of health care.
National Language Processing Techniques
A particular study has effectively pointed out that the National Language Processing techniques can be utilised to process information from the instructed medical notes with high accuracy and this particular task can be effective in identifying the medication orders and adverse drug events. Another literature has also found that the particular techniques used by the natural language processing can be used to identify the comorbidities and complications from the unstructured clinical notes which can be used to develop predictive models for the patient outcomes. Researches has also pointed out that the natural language processing techniques is focus to improve the patient care and the outcomes by identifying the patience at the high risk for adverse event such as hospital re-admission or medication errors. In a more contextual setting, the natural language processing techniques have also been used to support clinical research by effectively identifying the patient cohorts and extracting data for analysis.
It is also effective to understand that there are still challenges and the three are associated aspects with the use of the natural language processing techniques in the health care that includes the need for high quality data and the development of the accurate and reliable algorithm along with the other concerns about patient privacy and data security. The challenges highlight the importance of the ongoing research and development in the field of NLP and healthcare. In a similar not it is also effective to understand the context of the unstructured clinical notes and the variability in the language and terminology that are used by the different healthcare providers. This can make it a difficult for the NLP algorithms to accurately identify and extract relevant information. To address challenge researchers have developed methods to standardise language and terminology such as the use of medical ontologies and natural language processing pipelines. The natural language processing techniques have also been used to analyse the structured clinical notes for the patient phenotyping which involves identifying the patience with similar characteristics or the wrist factors for the particular conditions. For example, several resources have also contemplated that the aspects of using Natural Language Processing to identify the patients with the chronic obstructive pulmonary disease and classify them based on the disease severity and comorbidities.
Another area of research in terms of the natural language processing and the Healthcare is the use of the sentiment analysis to analyse the patient feedback and improve the quality of care. For example, in a particular study it has affectively pointed out that the National Language Processing techniques to analyse the patient comments on the online health forums and identify the areas for improvement in patient care. Similarly in particular research it has been pointed out that the techniques can also be used to identify and monitor the public health trend and outbreak.
The techniques can also be used to identify and monitor the public health care aspects and this was more prominent when particular research as effectively contemplated the patterns and trends in the public perceptions and awareness during the spread of the zika virus. The use of the natural language processing in healthcare also races the ethical and the legal concerns particularly around the patient previously and data security. Researchers have also emphasized the importance of developing transparent and ethical guidelines for the use of the natural language processing techniques in the health care including the obtaining of the patient consent and ensuring data security.
Techniques for Extracting Information
Literature and Research as also contemplated the context of the techniques of the natural language processing techniques for extracting information from the unstructured a critical note. It is relevant to understand the different techniques that have been demonstrated by the literature and the resources in terms of understanding the relevant aspects and the growth of this particular process. One of the most popular techniques that have been concentrated in most of the researches is the technique of Named Entity Recognition. This particular technique that involves the identification and extraction of the entities from the unstructured data such as patient names or any other prospects of medical procedures. This particular technique has been widely used in the healthcare for the prospects of medical reconciliation and adverse drug event detection. Relationship extraction is another technique that involves identification and extraction of the relationships between the entities in the unstructured text such as the relationship between the medication and a diagnosis. This particular technique has been used to develop predictive models for the patient outcomes such as hospitals admission and mortality.
Clinical concept of extraction involves identification and extraction of the clinical concepts from the unstructured a text. This particular technique has been used for the task such as a clinical decision support and a patient phenotyping. Another important aspect that a can effectively concentrate on the aspects of Natural Language Processing is the sentiment analysis that involves analysing the emotional tone of the text such as patient feedback or the social media post. This particular technique has been used to improve the patient’s satisfaction and identify the areas for the improvement in the patient care. Deep learning techniques are also important such as the neural networks and co-volitional neural networks that have been used to develop the models for the health care task such as disease classification and adverse event detection. These models have shown high accuracy and ability to learn from the large amounts of data. Medical ontologies are standardized terminologies that focuses on the prospects of improving the accuracy and consistency of the algorithms for the Healthcare task. In this aspect the clinical Natural Language Processing pipelines are pre build Natural Language Processing models and tools that are designs for the specific Health Care task such as medication reconciliation or disease diagnosis.
These pipelines have been developed to reduce the time and resource and required to build the custom natural language process models for the health care. In terms of understanding these techniques there are other literature also which has affectively focused on proposing a model that are based on the transformer architecture for extracting the drug related entities from the clinical notes.
The model achieved the trend of effective performance on the task of drug entity recognition which indicate the effectiveness of the transformer-based models for the NER in the healthcare. In some of the literature and research it has been effective that has focused on the challenges and the opportunities in developing the models for the health care applications that have highlighted the importance of the integration of medical knowledge into the models and the need for the domains specific training data. The performance of the several entity recognition models on the task of extracting the medication related entities from the clinical notes.
The study found that the deep learning models outperformed the rule based on traditional machine learning models indicating the potential of deep learning for the NER in the healthcare. A study by Rajkumar et. al (2018) develop the relationship extraction model for the prediction of the patient outcomes and the use of the clinical notes. The model was able to identify the relationship between the diagnosis and the medications and showed the promising results in the prediction of the hospital admissions on the prospects of mortality. In some of the other research papers it has affectively concentrated on the challenges and the opportunities in the development of the relationship extraction models for healthcare applications and have highlighted the need for the domain’s specific knowledge and training data that has also provided the potential of deep learning methods for this particular task.
Several literatures have also proposed the novel relationship extraction a model based on the graph neural network for predicting the drug disease interactions from the clinical notes. The model outperforms the traditional machine learning models and show the potential aspects for improving the drug safety recognition.
Another study has effectively developed the clinical concept of extraction model for identifying the clinical concepts from the clinical notes. The model was able to achieve high occur on several task including the identification of the breast cancer status and detecting the tuberculosis. Another review has effectively discussed about the challenges and opportunities in the development of the clinical concept extraction models for the healthcare applications and have highlighted the importance of incorporating domain specific knowledge and addressing the issue of data sparsity.
With the more cohesive approach, it is also found that the aspects of other literature have focused on the development of a extraction model focused on the medical ontology for identifying the awards drug events from the clinical notes. The model achieved high accuracy and showed potential for improving the pharmacovigilance. Overall, the studies have demonstrated the potential aspects of the natural language processing techniques for extracting information from the unstructured clinical notes in healthcare applications. While the challenges such as lack of domains specific training data and the need for the domain specific knowledge still exist the developed of the new model and method such as deep learning and medical on to logic show from missing aspects for improving the accuracy and effectiveness of the natural language processing in the health care.
Current State of Research in Terms of The Techniques
It is also effective to consider the current state of research in terms of the techniques available for extracting information from the unstructured clinical notes. Many recent studies have focused on the integration of the clinical knowledge into the models to improve their accuracy and effectiveness in the Healthcare applications. This includes the use of the medical ontologies and domain specific dictionary and other knowledge resources to improve the recognition and extraction of the clinical entities and relationships.
Deep learning methods had increasingly become popular in the research and mini recent studies have explode the use of deep learning methods for clinical text analysis. This includes the use of deep neural networks and convolutional neural networks and record and neural networks for the tasks such as entity recognition and relationship extraction and other aspects of the clinical concept extraction.
Multimodal data analysis has been found importance in most of the recent studies. Some of the recent studies have explode the use of multimodal data such as structure data for the clinical analysis. This particular includes the use of the image analysis on the natural language processing techniques to extract information from the radiology reports and other image base clinical data.
Evaluation and benchmarking are one of the most important aspects of the natural language processing models and become more complex and specialised for helping the applications there is a growing need for the standardised evaluation. The benchmarking frameworks needs to be compared and the evaluation of the different models must be contemplated with the effective focus on the development of search frameworks and metrics for the task such as the relationship extraction and clinical concept extraction.
The current state of research in the natural language processing techniques for the extracting information from the unstructured clinical notes is characterized by a focus on improving the accuracy and effectiveness to the integration of the clinical knowledge and the use of deep learning methods and the exploration of the multimodal data analysis. There is also a growing emphasis on standard eyes revaluation and benchmarking the frameworks to compare and evaluate the current models. 
Pattern in the Development of NLP
The early development and the use of the natural language processing techniques for the clinical text analysis can be traced back to 1960s and 70s when the researchers began to exploring the use of computer programs to analyse the medical record. Early studies have focused on the task such as the keyword extraction and the simple synthetic analysis but where limited by the complexity and the variability of the clinical language. In 1980s and 1990s the research begins developing the rule-based approaches for the clinical takes analysis which involved the use of manually corrupted rules to identify and extract clinical entities and relationship.
These methods by effective for the simple task such as the keyword extraction but were limited by their inability to handle the complexity and the variability of the clinical language. In 2000 and extended to the period of 2010 there was a shift towards the machine learning approaches from the clinical text analysis which involved the use of statistical models and to learn the patterns on relationship for the large amounts of anodized data. This includes the use of techniques such as Named Entity Recognition and relation extraction along with the other...