Constructing a disease database and using natural language processing to capture and standardize free text clinical information

Brian Schwartz Shaina Raza

26 Januari 2023

Background Extracting relevant information about infectious diseases is an essential task. However, a significant obstacle in supporting public health research is the lack of methods for effectively mining large amounts of health data. Objective This study aims to use natural language processing (NLP) to extract the key information (clinical factors, social determinants of health) from published cases in the literature. Methods The proposed framework integrates a data layer for preparing a data cohort from clinical case reports; an NLP layer to find the clinical and demographic-named entities and relations in the texts; and an evaluation layer for benchmarking performance and analysis. The focus of this study is to extract valuable information from COVID-19 case reports. Results The named entity recognition implementation in the NLP layer achieves a performance gain of about 1–3% compared to benchmark methods. Furthermore, even without extensive data labeling, the relation extraction method outperforms benchmark methods in terms of accuracy (by 1–8% better). A thorough examination reveals the disease’s presence and symptoms prevalence in patients. Conclusions A similar approach can be generalized to other infectious diseases. It is worthwhile to use prior knowledge acquired through transfer learning when researching other infectious diseases.

E. Klang D. Brin M. Omar + 1 lainnya

17 Januari 2024

Background: Natural Language Processing (NLP) and Large Language Models (LLMs) hold largely untapped potential in infectious disease management. This review explores their current use and uncovers areas needing more attention. Methods: This analysis followed systematic review procedures, registered with PROSPERO. We conducted a search across major databases including PubMed, Embase, Web of Science, and Scopus, up to December 2023, using keywords related to NLP, LLM, and infectious diseases. We also employed the QUADAS-2 tool for evaluating the quality and robustness of the included studies. Results: Our review identified 15 studies with diverse applications of NLP in infectious disease management. Notable examples include GPT-4's application in detecting urinary tract infections and BERTweet's use in Lyme Disease surveillance through social media analysis. These models demonstrated effective disease monitoring and public health tracking capabilities. However, the effectiveness varied across studies. For instance, while some NLP tools showed high accuracy in pneumonia detection and high sensitivity in identifying invasive mold diseases from medical reports, others fell short in areas like bloodstream infection management. Conclusion: This review highlights the yet-to-be-fully-realized promise of NLP and LLMs in infectious disease management. It calls for more exploration to fully harness AI's capabilities, particularly in the areas of diagnosis, surveillance, predicting disease courses, and tracking epidemiological trends.

B. Anderson May D. Wang Theodore M. Johnson + 9 lainnya

1 Juli 2023

Key Points Question Can a natural language processing (NLP) model accurately classify patient-initiated electronic health record (EHR) messages and triage positive COVID-19 cases? Findings In this cohort study of 10 172 patients, 3048 messages reported COVID-19–positive test results, and the mean (SD) message response time for patients who received treatment (364.10 [784.47] minutes) was faster than for those who did not (490.38 [1132.14] minutes). This novel NLP model classified patient messages with 94% accuracy and a sensitivity of 85% for messages that mentioned confirmed COVID-19 infection, discussed COVID-19 without mentioning a positive test result, or were unrelated to COVID-19. Meaning These findings suggest that NLP-EHR integration can effectively triage patients reporting positive at-home COVID-19 test results via the EHR, reducing the time to first message response and increasing the likelihood of receiving an antiviral prescription within the 5-day treatment window.

Jinge Wu C. Sudlow Beatrice Alex + 20 lainnya

21 Desember 2022

Much of the knowledge and information needed for enabling high-quality clinical research is stored in free-text format. Natural language processing (NLP) has been used to extract information from these sources at scale for several decades. This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse methodologies and applications, and identify the main barriers. We collect a dataset of clinical NLP projects ( n  = 94; £  = 41.97 m) funded by UK funders or the European Union’s funding programmes. Additionally, we extract details on 9 funders, 137 organisations, 139 persons and 431 research papers. Networks are created from timestamped data interlinking all entities, and network analysis is subsequently applied to generate insights. 431 publications are identified as part of a literature review, of which 107 are eligible for final analysis. Results show, not surprisingly, clinical NLP in the UK has increased substantially in the last 15 years: the total budget in the period of 2019–2022 was 80 times that of 2007–2010. However, the effort is required to deepen areas such as disease (sub-)phenotyping and broaden application domains. There is also a need to improve links between academia and industry and enable deployments in real-world settings for the realisation of clinical NLP’s great potential in care delivery. The major barriers include research and development access to hospital data, lack of capable computational resources in the right places, the scarcity of labelled data and barriers to sharing of pretrained models.

Chih-Hsuan Wei Alexis Allot Qingyu Chen + 4 lainnya

9 Oktober 2020

The COVID-19 (coronavirus disease 2019) pandemic has had a significant impact on society, both because of the serious health effects of COVID-19 and because of public health measures implemented to slow its spread. Many of these difficulties are fundamentally information needs; attempts to address these needs have caused an information overload for both researchers and the public. Natural language processing (NLP)-the branch of artificial intelligence that interprets human language-can be applied to address many of the information needs made urgent by the COVID-19 pandemic. This review surveys approximately 150 NLP studies and more than 50 systems and datasets addressing the COVID-19 pandemic. We detail work on four core NLP tasks: information retrieval, named entity recognition, literature-based discovery, and question answering. We also describe work that directly addresses aspects of the pandemic through four additional tasks: topic modeling, sentiment and emotion analysis, caseload forecasting, and misinformation detection. We conclude by discussing observable trends and remaining challenges.