Currently, the practice of recording clinical information in “free text” offers flexibility, but hinders automatic data extraction, limiting the application of analytical models. Most records are unstructured, and the use of non-standard abbreviations increases ambiguity, making interpretation difficult. Manual review is slow and prone to errors, while clinical decision support systems, which rely on structured data, are ineffective for “free text.” The use of text mining and natural language processing in healthcare is still at an early stage, with no widespread application in clinical contexts.
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Home / Publications / Publication
FROM “FREE TEXT” TO STRUCTURED CLINICAL DATA: THE FOUNDATION FOR CLINICAL DECISION SUPPORT SYSTEMS
Publication type: Article Summary
Original title: Validação de algoritmos de text mining em contexto de oncologia
Article publication date: October 2020
Source: Repositório Aberto da Universidade do Porto
Author: Renato Ferreira Magalhães
Supervisors: Mário Amorim Lopes & Lúcio Lara Santos
What is the goal, target audience, and areas of digital health it addresses?
This study aims to provide structured clinical information to support decision-making, facilitating access to clinical data to assist healthcare professionals in their daily activities. The main area of digital health covered is Artificial Intelligence (AI), with an emphasis on machine learning.
What is the context?
This study was conducted in a reference oncology hospital that generates large volumes of clinical data from medical procedures, diagnoses, treatments, and test results. The process of consulting and properly recording each patient’s data is time-consuming, which prolongs consultations and reduces the time of health professionals, consequently impacting the quality of clinical decisions. The COVID-19 pandemic, the period in which the study was carried out, further aggravated these difficulties, complicating the allocation of professionals for data annotation tasks.
What are the current approaches?
Currently, the practice of recording clinical information in “free text” offers flexibility, but hinders automatic data extraction, limiting the application of analytical models. Most records are unstructured, and the use of non-standard abbreviations increases ambiguity, making interpretation difficult. Manual review is slow and prone to errors, while clinical decision support systems, which rely on structured data, are ineffective for “free text.” The use of text mining and natural language processing in healthcare is still at an early stage, with no widespread application in clinical contexts.
What does innovation consist of? How is the impact of this study assessed?
The innovation of this study consists in the creation of a structured data repository that feeds the clinical decision support systems. This repository is built by transforming patient records (clinical diaries) written in “free text” into structured data through text mining and natural language processing.
The process starts with extracting text from PDF documents, followed by normalization, which includes correcting sentences and replacing abbreviations with their full terms. With the text already normalized, the next step is to identify the three categories or entities to which the data refer: ‘Procedures,’ ‘Disorders,’ or ‘Drugs’ using text mining algorithms. The natural language processing is used for the analysis and understanding of the texts.
In this study, 2 clinical processes were used, and 2 algorithm training models were applied. In the semi-supervised model, the training was performed by automatically associating the biomedical vocabulary of the UMLS (Unified Medical Language System) with the 3 categories while in the supervised model, the training was performed through the manual identification of the categories by 2 human annotators (A and B). The study also included the clinical validation of the text mining algorithms in the oncology context, ensuring the practical relevance and applicability of the developed models. The impact of the models was assessed through performance metrics such as precision, as well as a qualitative analysis performed by a senior physician.
What are the main results? What is the impact of these results? What is the future of this technology?
The semi-supervised model presented a precision result of 86.6%, while the supervised model obtained 63.8% precision with annotator A and 65.7% with annotator B. The qualitative analysis revealed that both models left some categories/entities unidentified in certain sentences. Although the results are promising, there is a need to reinforce the training of annotators and train the algorithms with more validated data.
The impact of these results is significant, as it demonstrates that it is possible to create tools to support clinical decision-making. The use of structured data will allow the creation of intuitive dashboards that present critical information, such as diagnoses, drugs, and even a detailed timeline with the patient’s clinical history. This organization will provide a clear and quick view of the patient’s condition, assisting healthcare professionals make more informed decisions and reducing the time spent analyzing large volumes of “free text”.
In the future, this tool can be expanded to integrate AI, helping to compare treatments and predict outcomes based on data from similar patients, crossing information with international guidelines and scientific evidence. With this, the tool will provide greater personalization of treatments and optimization of resources. By reducing redundancies, such as repeat tests, and improving the efficiency of clinical processes, technology can directly impact the quality of health care and reduce costs.
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Home / Publications / Publication
FROM “FREE TEXT” TO STRUCTURED CLINICAL DATA: THE FOUNDATION FOR CLINICAL DECISION SUPPORT SYSTEMS
Publication type: Article Summary
Original title: Validação de algoritmos de text mining em contexto de oncologia
Article publication date: October 2020
Source: Repositório Aberto da Universidade do Porto
Author: Renato Ferreira Magalhães
Supervisors: Mário Amorim Lopes & Lúcio Lara Santos
What is the goal, target audience, and areas of digital health it addresses?
This study aims to provide structured clinical information to support decision-making, facilitating access to clinical data to assist healthcare professionals in their daily activities. The main area of digital health covered is Artificial Intelligence (AI), with an emphasis on machine learning.
What is the context?
This study was conducted in a reference oncology hospital that generates large volumes of clinical data from medical procedures, diagnoses, treatments, and test results. The process of consulting and properly recording each patient’s data is time-consuming, which prolongs consultations and reduces the time of health professionals, consequently impacting the quality of clinical decisions. The COVID-19 pandemic, the period in which the study was carried out, further aggravated these difficulties, complicating the allocation of professionals for data annotation tasks.
What are the current approaches?
Currently, the practice of recording clinical information in “free text” offers flexibility, but hinders automatic data extraction, limiting the application of analytical models. Most records are unstructured, and the use of non-standard abbreviations increases ambiguity, making interpretation difficult. Manual review is slow and prone to errors, while clinical decision support systems, which rely on structured data, are ineffective for “free text.” The use of text mining and natural language processing in healthcare is still at an early stage, with no widespread application in clinical contexts.
What does innovation consist of? How is the impact of this study assessed?
The innovation of this study consists in the creation of a structured data repository that feeds the clinical decision support systems. This repository is built by transforming patient records (clinical diaries) written in “free text” into structured data through text mining and natural language processing.
The process starts with extracting text from PDF documents, followed by normalization, which includes correcting sentences and replacing abbreviations with their full terms. With the text already normalized, the next step is to identify the three categories or entities to which the data refer: ‘Procedures,’ ‘Disorders,’ or ‘Drugs’ using text mining algorithms. The natural language processing is used for the analysis and understanding of the texts.
In this study, 2 clinical processes were used, and 2 algorithm training models were applied. In the semi-supervised model, the training was performed by automatically associating the biomedical vocabulary of the UMLS (Unified Medical Language System) with the 3 categories while in the supervised model, the training was performed through the manual identification of the categories by 2 human annotators (A and B). The study also included the clinical validation of the text mining algorithms in the oncology context, ensuring the practical relevance and applicability of the developed models. The impact of the models was assessed through performance metrics such as precision, as well as a qualitative analysis performed by a senior physician.
What are the main results? What is the impact of these results? What is the future of this technology?
The semi-supervised model presented a precision result of 86.6%, while the supervised model obtained 63.8% precision with annotator A and 65.7% with annotator B. The qualitative analysis revealed that both models left some categories/entities unidentified in certain sentences. Although the results are promising, there is a need to reinforce the training of annotators and train the algorithms with more validated data.
The impact of these results is significant, as it demonstrates that it is possible to create tools to support clinical decision-making. The use of structured data will allow the creation of intuitive dashboards that present critical information, such as diagnoses, drugs, and even a detailed timeline with the patient’s clinical history. This organization will provide a clear and quick view of the patient’s condition, assisting healthcare professionals make more informed decisions and reducing the time spent analyzing large volumes of “free text”.
In the future, this tool can be expanded to integrate AI, helping to compare treatments and predict outcomes based on data from similar patients, crossing information with international guidelines and scientific evidence. With this, the tool will provide greater personalization of treatments and optimization of resources. By reducing redundancies, such as repeat tests, and improving the efficiency of clinical processes, technology can directly impact the quality of health care and reduce costs.
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