Empowering digital pathology applications through explainable knowledge extraction tools

Marchesin, Stefano; Giachelle, Fabio; Marini, Niccolò; Atzori, Manfredo; Boytcheva, Svetla; Buttafuoco, Genziana; Ciompi, Francesco; Nunzio, Giorgio Maria Di; Fraggetta, Filippo; Irrera, Ornella; Müller, Henning; Primov, Todor; Vatrano, Simona; Silvello, Gianmaria

doi:10.1016/j.jpi.2022.100139

Cited by 11 publications

(9 citation statements)

References 55 publications

(68 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…9 Many current NLP projects exist to convert free text from pathology reports into structured electronic health record (EHR) information for synoptic case reporting and experiment planning (i.e., a structured search tool for identifying cases to form a study cohort). 10 , 11 , 12 , 13 , 14 , 15 …”

Section: Introductionmentioning

confidence: 99%

Development of an interactive web dashboard to facilitate the reexamination of pathology reports for instances of underbilling of CPT codes

Greenburg¹,

Lu²,

Lu³

et al. 2023

Journal of Pathology Informatics

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Development of an interactive web dashboard to facilitate the reexamination of pathology reports for instances of underbilling of CPT codes

Greenburg¹,

Lu²,

Lu³

et al. 2023

Journal of Pathology Informatics

View full text Add to dashboard Cite

“…Marchesin et al investigated the use of NLP to strengthen digital pathology applications [ 38 ]. The authors introduced explainable knowledge extraction tools capable of extracting pertinent information from pathology reports.…”

Section: Resultsmentioning

confidence: 99%

“…Their study demonstrated the potential of machine learning-driven CDSS to improve allergy detection and classification, leading to enhanced patient safety in anesthesia and ICU settings. Marchesin et al [ 38 ] focused on the application of NLP in digital pathology applications, showcasing how NLP can support pathologists and improve the overall quality of pathology diagnosis and patient care. Elkin et al [ 39 ] showed the effectiveness of NLP in identifying NVAF patients, which has the potential to lead to better management of NVAF and prevent strokes and death.…”

Section: Discussionmentioning

confidence: 99%

Applications of the Natural Language Processing Tool ChatGPT in Clinical Practice: Comparative Study and Augmented Systematic Review

Schopow,

Osterhoff,

Baur

2023

JMIR Med Inform

View full text Add to dashboard Cite

Background This research integrates a comparative analysis of the performance of human researchers and OpenAI's ChatGPT in systematic review tasks and describes an assessment of the application of natural language processing (NLP) models in clinical practice through a review of 5 studies. Objective This study aimed to evaluate the reliability between ChatGPT and human researchers in extracting key information from clinical articles, and to investigate the practical use of NLP in clinical settings as evidenced by selected studies. Methods The study design comprised a systematic review of clinical articles executed independently by human researchers and ChatGPT. The level of agreement between and within raters for parameter extraction was assessed using the Fleiss and Cohen κ statistics. Results The comparative analysis revealed a high degree of concordance between ChatGPT and human researchers for most parameters, with less agreement for study design, clinical task, and clinical implementation. The review identified 5 significant studies that demonstrated the diverse applications of NLP in clinical settings. These studies’ findings highlight the potential of NLP to improve clinical efficiency and patient outcomes in various contexts, from enhancing allergy detection and classification to improving quality metrics in psychotherapy treatments for veterans with posttraumatic stress disorder. Conclusions Our findings underscore the potential of NLP models, including ChatGPT, in performing systematic reviews and other clinical tasks. Despite certain limitations, NLP models present a promising avenue for enhancing health care efficiency and accuracy. Future studies must focus on broadening the range of clinical applications and exploring the ethical considerations of implementing NLP applications in health care settings.

show abstract

“…In a nutshell, the aim of EL is to determine if a given (extracted) entity refers to a specific concept within a reference ontology. To this end, Marchesin et al 65 developed the Semantic Knowledge Extractor Tool (SKET), an unsupervised hybrid knowledge extraction system that combines a rule-based expert system with pre-trained machine learning models to extract relevant concepts from pathology reports. The system presents a modular architecture, where different components or methods can be easily plugged-in.…”

Section: Resultsmentioning

confidence: 99%

Modelling digital health data: The ExaMode ontology for computational pathology

Menotti,

Silvello,

Atzori

et al. 2023

Journal of Pathology Informatics

Self Cite

View full text Add to dashboard Cite

Empowering digital pathology applications through explainable knowledge extraction tools

Cited by 11 publications

References 55 publications

Development of an interactive web dashboard to facilitate the reexamination of pathology reports for instances of underbilling of CPT codes

Development of an interactive web dashboard to facilitate the reexamination of pathology reports for instances of underbilling of CPT codes

Applications of the Natural Language Processing Tool ChatGPT in Clinical Practice: Comparative Study and Augmented Systematic Review

Modelling digital health data: The ExaMode ontology for computational pathology

Contact Info

Product

Resources

About