Healthcare databases are growing exponentially where healthcare providers, pharmaceutical companies and biotechnology firms are using text analytics and natural language processing (NLP) to improve patient outcomes. In addition, the growth of wearable device technologies, have opened new floodgates of consumer health data. The information encapsulated within electronic clinical records could lead to improved healthcare quality, promotion of clinical and research initiatives, fewer medical errors and lower costs. Due to these reasons, text analytics and NLP systems have been developed to transform this data into value. However, the documents that comprise health records vary in complexity, length and use of technical vocabulary. These problems make knowledge discovery complex and thus hinder the integration of text analytics tools with existing healthcare systems. Therefore, there is an obvious need to leverage unstructured textual data to support healthcare operations in many aspects. A large proportion of the clinical data is unavoidably stockpiled into unstructured, or semi-structured, documents or notes. Therefore, text analytics should play a key role in transforming textual data into actionable insights.
Understanding the challenges in developing and implementing data analytics expert systems to support healthcare services is essential. Over the past five years, there have been pronounced innovations in the NLP research, including novel approaches and technologies, which have resonated in the healthcare domain. Most remarkably, Deep Learning has been increasingly applied for developing large-scale language models. Deep architectures of CNNs have introduced a potent mechanism for automatically learning feature representations from raw data. Furthermore, scalable analytics platforms have been utilised for real-time data processing. Text Analytics was implemented for considerable problems, including extracting evidence-based care interventions and patient outcomes or identifying the population at risk. To this end, NLP pipelines have been intensively developed for a variety of text-processing tasks such as: i) Named entity recognition, ii) Topic modelling, iii) Semantic labelling, iv) Relationship extraction, v) Question answering, vi) Text summarisation, vii) Sentiment analysis, and others.
Researchers working in healthcare analytics and informatics are welcome to submit their research t to this Research Topic. We hope to bring together original research and review articles focusing on text analytics and/or NLP and to cover recent advances in the feasibility of novel applications of the machine and deep learning approaches of text analytics in healthcare.
Potential topics include but are not limited to the following:
• Electronic Medical Records (EMR)/Electronic Health Records (EHR) data analytics;
• Unstructured natural language processing;
• NLP based healthcare modelling;
• Speech analytics for healthcare diagnostic;
• NLP of healthcare text;
• Transfer learning in data analytics for healthcare diagnosis;
• Unstructured data processing using sentiment analysis;
• Assessment of text analytics from EMR;
• Implementation of healthcare text analytics in hospital readmission risks;
• Real-time processing of healthcare text analytics;
• NLP & data analytics for healthcare operation;
• Data analytics in managing health regulatory compliance.
Healthcare databases are growing exponentially where healthcare providers, pharmaceutical companies and biotechnology firms are using text analytics and natural language processing (NLP) to improve patient outcomes. In addition, the growth of wearable device technologies, have opened new floodgates of consumer health data. The information encapsulated within electronic clinical records could lead to improved healthcare quality, promotion of clinical and research initiatives, fewer medical errors and lower costs. Due to these reasons, text analytics and NLP systems have been developed to transform this data into value. However, the documents that comprise health records vary in complexity, length and use of technical vocabulary. These problems make knowledge discovery complex and thus hinder the integration of text analytics tools with existing healthcare systems. Therefore, there is an obvious need to leverage unstructured textual data to support healthcare operations in many aspects. A large proportion of the clinical data is unavoidably stockpiled into unstructured, or semi-structured, documents or notes. Therefore, text analytics should play a key role in transforming textual data into actionable insights.
Understanding the challenges in developing and implementing data analytics expert systems to support healthcare services is essential. Over the past five years, there have been pronounced innovations in the NLP research, including novel approaches and technologies, which have resonated in the healthcare domain. Most remarkably, Deep Learning has been increasingly applied for developing large-scale language models. Deep architectures of CNNs have introduced a potent mechanism for automatically learning feature representations from raw data. Furthermore, scalable analytics platforms have been utilised for real-time data processing. Text Analytics was implemented for considerable problems, including extracting evidence-based care interventions and patient outcomes or identifying the population at risk. To this end, NLP pipelines have been intensively developed for a variety of text-processing tasks such as: i) Named entity recognition, ii) Topic modelling, iii) Semantic labelling, iv) Relationship extraction, v) Question answering, vi) Text summarisation, vii) Sentiment analysis, and others.
Researchers working in healthcare analytics and informatics are welcome to submit their research t to this Research Topic. We hope to bring together original research and review articles focusing on text analytics and/or NLP and to cover recent advances in the feasibility of novel applications of the machine and deep learning approaches of text analytics in healthcare.
Potential topics include but are not limited to the following:
• Electronic Medical Records (EMR)/Electronic Health Records (EHR) data analytics;
• Unstructured natural language processing;
• NLP based healthcare modelling;
• Speech analytics for healthcare diagnostic;
• NLP of healthcare text;
• Transfer learning in data analytics for healthcare diagnosis;
• Unstructured data processing using sentiment analysis;
• Assessment of text analytics from EMR;
• Implementation of healthcare text analytics in hospital readmission risks;
• Real-time processing of healthcare text analytics;
• NLP & data analytics for healthcare operation;
• Data analytics in managing health regulatory compliance.