Introduction
Artificial intelligence has been the most beneficial technology in the healthcare industry. It has transformed how patient care is provided to the patients and has also helped in faster drug discoveries to help individuals fight various diseases.
Another way through which generative AI has helped the healthcare industry is through building patient cohorts with NLP (natural language processing) and Knowledge Graphs.
Patient cohorts are extremely important in the healthcare industry as they help to understand various kinds of disease patterns, treatment outcomes, and overall patient care management.
In this blog, we will understand how advanced technology has helped to turn the labour-intensive work of building patient cohorts into an easier and more successful task with the help of NLP and Knowledge Graphs.
Understanding Basics
Before moving towards the steps involved in building patient cohorts with NLP and Knowledge Graphs, let us understand the basic terms associated with the concept.
What are Patient Cohorts?
Patient cohorts can be defined as a group of individuals who share common experiences or characteristics. By grouping patients that have similar characteristics, healthcare professionals can identify trends, personalise treatments and as a result improve clinical outcomes.
Further, patient cohorts are also used for clinical studies, treatment plans, and outcome analysis. With the help of patient cohorts, healthcare professionals are able to identify trends and patterns that are most likely to be missed when looking at an individual patient.
Patient cohorts are especially critical for evidence-based medicine as it helps to improve treatment outcomes and patient care.
What is NLP (Natural Language Processing)?
NLP, or natural language processing, is the most important branch of artificial intelligence. This branch enables computers to understand, interpret and process human language.
NLP works by combining computational linguistics with machine learning and deep learning models to process as well as analyse huge volumes of natural language data.
In the healthcare industry, NLP is significantly important as it helps to extract relevant and meaningful information from huge volumes of unstructured data such as clinical notes, medical records, and research articles. As a result, it leads to the automatic identification of important and relevant patient information, such as diagnoses, treatments, and outcomes.
By using NLP in the healthcare industry, healthcare providers are able to gain valuable insights from huge volumes of textual data, which is extremely challenging to analyse manually.
What are Knowledge Graphs?
Knowledge Graphs can be described as a method of representing knowledge while using a structured format. Knowledge Graphs consist of nodes (entities) and edges (relationships) that help to create a network that consists of interconnected information.
As a result of the structured representation of knowledge, the complex relationships between different entities are clearly defined, helping the machines to process the data and provide insightful information.
In the healthcare industry, Knowledge Graphs are used to represent the relationship between patients, diseases, treatments, and outcomes.
With the help of the structured representation of data through Knowledge Graphs, healthcare providers can facilitate advanced data analysis, which leads to better support in the decision-making process.
Building Patient Cohorts With NLP
Let us now look at the steps that are taken for the successful building of patient cohorts with NLP and Knowledge Graphs in the healthcare industry.
Step 1: Data Collection
The first step of successfully building patient cohorts with NLP is data collection. Data collection involves collecting patient data from various structured and unstructured sources, such as electronic health records (contains information relating to patients’ medical history, lab results, and imaging studies), clinical notes (contains valuable insights into the symptoms, diagnoses and treatments of the patient), and research articles (contains valuable information with respect to treatment outcomes and disease progression).
The collected data must include meaningful and relevant information, such as patients’ demographics, symptoms, diagnoses, treatments, and outcomes.
Step 2: Data Preprocessing
The second step in the process of building patient cohorts with NLP and Knowledge Graphs is data preprocessing. Data preprocessing helps to prepare the collected data for further analysis. This includes cleaning and transforming the data into a format that is suitable for NLP and Knowledge Graph analysis.
During the preprocessing of data, certain activities are completed that include removing duplicates, handling missing values, and standardising the format of the data.
With the help of data preprocessing, organisations can ensure that the data provided for analysis is clean, accurate, and ready for analysis.
Step 3: NLP Processing
The next step for building patient cohorts with NLP and Knowledge Graphs is NLP processing. This includes applying NLP techniques to unstructured data in order to extract meaningful and relevant information.
The NLP techniques refer to the application of NLP algorithms to identify and extract certain key information from the preprocessed data, such as diagnoses, medications, and treatment outcomes.
This data can be used for further analysis which helps in the successful building of patient cohorts with NLP and Knowledge Graphs.
Step 4: Creating Knowledge Graphs
Once all the relevant information is extracted from all the various sources using NLP, the next step in building patient cohorts with NLP and Knowledge Graphs include the creation of Knowledge Graphs.
The creation of Knowledge Graphs involves structuring the relevant extracted information into a network of entities and relationships that are interconnected.
With the help of Knowledge Graphs, healthcare providers are able to garner a comprehensive view into the status of the patient’s health, which further leads to advanced analysis and reasoning.
Step 5: Identifying Cohort Criteria
For building patient cohorts with NLP and Knowledge Graphs, it is important to identify and define the relevant criteria for creating patient cohorts.
This includes identifying the various characteristics that will be used to group various patients into a single cohort. The criteria can include various factors, such as demographics, diagnoses, treatments, and outcomes.
It is very important to define the cohort criteria as it helps in the creation of meaningful and relevant patient groups.
Step 6: Cohort Analysis
After the successful defining of patient cohorts, the next step in building patient cohorts with NLP and Knowledge Graphs is to conduct cohort analysis to identify various patterns and trends.
This step involves the usage of statistical and machine learning techniques to conduct data analysis and gain valuable insights into the health status, treatment outcomes, and disease progression of the patient.
With the help of cohort analysis, healthcare providers are able to identify various valuable insights that leads to informed clinical decision-making.
Step 7: Visualisation
Visualisation in building patient cohorts with NLP and Knowledge Graphs helps to effectively present the results of cohort analysis.
This step involves the creation of various visual representations of the data, such as charts, graphs, and dashboards, which further helps to effectively communicate all the insights.
With the help of visualisation, the data becomes more accessible and understandable for all the healthcare providers.
Further, visualisation also helps to improve the communication of the result of the analysis, leading to more informed decision-making.
Step 8: Validation
In order to ensure the reliability and accuracy of the patient cohorts, validation is very crucial. Validation includes the verification of the data as well as the results of the analysis to ensure that they are consistent and accurate.
To conduct validation, organisations can use various techniques, such as cross-referencing the data with external sources and conducting sensitivity analysis.
Step 9: Implementation
The final step in the successful building of patient cohorts with NLP and Knowledge Graphs is implementing the patient cohorts in clinical practice.
This step includes integrating the patient cohorts with the existing workflows and using the valuable insights gained to inform patient care and treatment plans.
Various tasks included in the implementation process include providing training to healthcare providers on the usage of cohorts and integrating the cohorts into the electronic health record (EHR) systems.
Through implementation, the organisation can ensure that the patient cohorts are effectively used in clinical practice, improving patient care and outcomes.
Benefits Of Building Patient Cohorts With NLP
The various benefits of building patient cohorts with NLP and Knowledge Graphs include:
Improved Patient Care
By building patient cohorts with NLP and Knowledge Graphs, healthcare providers are able to provide persoanlised treatment plans to specific patient groups, leading to an improvement in patient care.
As patient cohorts provide the identification of the characteristics and needs of different cohorts, healthcare providers are able to develop treatment strategies that are more targeted and effective.
Enhanced Research
With the help of patient cohorts and the valuable data it provides for clinical research, researchers are able to effectively study disease patterns, treatment outcomes, and other significant aspects of healthcare.
Further, with the help of patient cohorts analysis, researchers are able to identify certain patterns and trends that would otherwise be difficult to identify when examining individual patients.
Evidence-Based Medicine
Building patient cohorts with NLP and Knowledge Graphs provides data-driven insights into patient care and treatment outcomes, supporting evidence-based medicine.
With patient cohort analysis, healthcare providers are able to identify the most effective treatments for specific patient groups, leading to more informed and evidence-based decision-making.
Improved Resource Allocation
By building patient cohorts with NLP and Knowledge Graphs, healthcare providers are able to improve the effectiveness of resource allocation as they are able to identify the needs of different patient groups.
By understanding the characteristics and needs of different patient cohorts, organisations are able to identify and allocate resources to areas where they are most needed.
Conclusion
Through the building of patient cohorts with NLP and Knowledge Graphs, the healthcare industry has seen a transformation in the way it manages and analyses huge volumes of complex and diverse data sources.
By using the advanced technology of generative AI, healthcare professionals are able to create accurate and meaningful patient cohorts that help them in more informed and evidence-based decision-making.
As the healthcare industry continues to revolutionise, it promises a world with better healthcare facilities and treatments, leading to improved survival rates and patient outcomes.
We at CrossML, with our extraordinary team of AI experts, help healthcare institutions build patient cohorts with NLP and Knowledge Graphs and integrate them with their existing clinical workflows. This ensures that healthcare institutions are able to create more accurate and reliable patient cohorts that help in the positive progression of the medical industry.
FAQs
Best practices for building patient cohorts with NLP and Knowledge Graphs include ensuring data quality and consistency, using standardised medical terminologies, integrating various and diverse data sources, employing strong NLP algorithms, validating cohort criteria, and continuously updating the Knowledge Graph.
NLP extracts meaningful and relevant data from various data sources (both structured and undtructured) while Knowledge Graphs structures this data into entities and relationships that are interconnected. As a result, it enables the identification of various patterns and trends that lead to the creation of accurate, reliable and meaningful patient cohorts.
The various challenges of using NLP and Knowledge Graph for patient cohorts include handling the variability and complexity of medical terminology, ensuring data quality and consistency, integrating diverse data sources, maintaining data integrity, privacy and security, and managing the computational complexity of building and updating Knowledge Graphs.
Key components of patient cohorts built with NLP and Knowledge Graph include high-quality and diverse data sources, strong NLP algorithms that help to extract meaningful and relevant data, a well-structured Knowledge Graph that effectively represents the data, and a clear and well-defined cohort criteria. Further, key components also include effective validation processes to ensure accuracy, visualisation tools for data analysis, and strong mechanisms that help in maintaining data integrity, privacy, and security.
