Detecting evidence of invasive fungal infections in cytology and histopathology reports enriched with concept-level annotations

Rozova, Vlada; Khanina, Anna; Teng, Jasmine C.; Teh, Joanne S.K.; Worth, Leon J.; Slavin, Monica; Thursky, Karin; Verspoor, Karin

doi:10.1016/j.jbi.2023.104293

“…After the removal of 143 duplicates, the screening process found 13 studies that met our inclusion and exclusion criteria (19,(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32). We identified two additional studies via reference screening (33,34).…”

Section: Search Results and Study Selectionmentioning

confidence: 99%

“…Six studies demonstrated a low risk of bias across all domains (21,(31)(32)(33)(34)(35). Overall, ten studies were evaluated as having low risk (21)(22)(23)26,28,(31)(32)(33)(34)(35), four showed high risk (19,24,27,29), and one lacked sufficient data for a conclusive risk assessment (30).…”

Section: Risk Of Biasmentioning

confidence: 99%

Utilizing Natural Language Processing and Large Language Models in the Diagnosis and Prediction of Infectious Diseases: A Systematic Review

Omar,

Brin,

Glicksberg

et al. 2024

Preprint

1

0

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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.

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“…After the removal of 143 duplicates, the screening process found 13 studies that met our inclusion and exclusion criteria (19,(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32). We identified two additional studies via reference screening (33,34).…”

Section: Search Results and Study Selectionmentioning

confidence: 99%

“…Six studies demonstrated a low risk of bias across all domains (21,(31)(32)(33)(34)(35). Overall, ten studies were evaluated as having low risk (21)(22)(23)26,28,(31)(32)(33)(34)(35), four showed high risk (19,24,27,29), and one lacked sufficient data for a conclusive risk assessment (30).…”

Section: Risk Of Biasmentioning

confidence: 99%

See 1 more Smart Citation

Utilizing natural language processing and large language models in the diagnosis and prediction of infectious diseases: A systematic review

Omar,

Brin,

Glicksberg

et al. 2024

American Journal of Infection Control

5

0

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Advancing delirium classification: A clinical notes-based natural language processing-supported machine learning model

Amjad,

Holmes,

Kishore

et al. 2024

Intelligence-Based Medicine

0

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Detecting evidence of invasive fungal infections in cytology and histopathology reports enriched with concept-level annotations

Cited by 4 publications

References 14 publications

Utilizing Natural Language Processing and Large Language Models in the Diagnosis and Prediction of Infectious Diseases: A Systematic Review

Utilizing Natural Language Processing and Large Language Models in the Diagnosis and Prediction of Infectious Diseases: A Systematic Review

Utilizing natural language processing and large language models in the diagnosis and prediction of infectious diseases: A systematic review

Advancing delirium classification: A clinical notes-based natural language processing-supported machine learning model

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