Natural language processing (NLP) is an artificial intelligent technology that enables structured information extraction from unstructured objects, applicable to precision medicine in the field of psychiatry. As an interdisciplinary research area, psychiatry has accumulated abundant research materials and data. Analyzing free texts in clinical records and identifying key variables will certainly advance the precision medicine in this discipline.
Due to the challenges of extracting key variables from heterogeneous clinical materials, NLP still is yet to be developed in medical field in spite of its rapid development in terms of methodologies and applications. A current focus of medical NLP research is identifying relationships between phenotype, genotypes, diseases, drugs, and pathways from scientific papers and patients’ clinical data. For researchers, high false positive rates had been hindering the utility and wider spread of AI-aided diagnosis. In order to improve the automated diagnosis, a suite of state-of-art NLP methodologies and user-friendly computational tools specialized with a profound understanding of neurological disorders need to be developed to assist clinicians to make better decision.
In light of the above, this Research Topic is proposed to publish rigorously peer-reviewed articles from researchers whose skills could be applied across the full spectrum of neuroscience and psychiatry, and who are seeking applications of innovative applications to increase the efficiency, effectiveness and quality of data processing in the area of neurological disorders, especially in the diagnosis and treatments of mental disorders.
This research topic hopes to promote the fundamentals and technologies of medical NLP and its application, and topics of interest includes but not limited to:
• Intelligent diagnosis and treatment
• Information extraction
• Information retrieval and text mining
• Interpretability and analysis of models for NLP
• Machine learning for medical NLP
• Hospital information management system
• NLP applications in neuroscience and smart health management
• Disease prediction and forecast system
• Medical data security and sentiment analysis, stylistic analysis, and argument mining
• Linguistic theories, cognitive modeling and psycholinguistics
Natural language processing (NLP) is an artificial intelligent technology that enables structured information extraction from unstructured objects, applicable to precision medicine in the field of psychiatry. As an interdisciplinary research area, psychiatry has accumulated abundant research materials and data. Analyzing free texts in clinical records and identifying key variables will certainly advance the precision medicine in this discipline.
Due to the challenges of extracting key variables from heterogeneous clinical materials, NLP still is yet to be developed in medical field in spite of its rapid development in terms of methodologies and applications. A current focus of medical NLP research is identifying relationships between phenotype, genotypes, diseases, drugs, and pathways from scientific papers and patients’ clinical data. For researchers, high false positive rates had been hindering the utility and wider spread of AI-aided diagnosis. In order to improve the automated diagnosis, a suite of state-of-art NLP methodologies and user-friendly computational tools specialized with a profound understanding of neurological disorders need to be developed to assist clinicians to make better decision.
In light of the above, this Research Topic is proposed to publish rigorously peer-reviewed articles from researchers whose skills could be applied across the full spectrum of neuroscience and psychiatry, and who are seeking applications of innovative applications to increase the efficiency, effectiveness and quality of data processing in the area of neurological disorders, especially in the diagnosis and treatments of mental disorders.
This research topic hopes to promote the fundamentals and technologies of medical NLP and its application, and topics of interest includes but not limited to:
• Intelligent diagnosis and treatment
• Information extraction
• Information retrieval and text mining
• Interpretability and analysis of models for NLP
• Machine learning for medical NLP
• Hospital information management system
• NLP applications in neuroscience and smart health management
• Disease prediction and forecast system
• Medical data security and sentiment analysis, stylistic analysis, and argument mining
• Linguistic theories, cognitive modeling and psycholinguistics