Combining text mining with clinical decision support in clinical practice: a scoping review

Burgt, Britt van de; Wasylewicz, Arthur T M; Dullemond, Bjorn; Grouls, R. J. E.; Egberts, Toine C.G.; Bouwman, Arthur; Korsten, Erik

doi:10.1093/jamia/ocac240

Cited by 4 publications

(2 citation statements)

References 72 publications

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“…63 • Medical literature mining to develop clinical decision support systems and patient communication platforms. 64…”

Section: Natural Language Processing (Nlp)mentioning

confidence: 99%

Artificial Intelligence in Newborn Medicine

Huisman,

Huisman

2024

Newborn

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“…63 • Medical literature mining to develop clinical decision support systems and patient communication platforms. 64…”

Section: Natural Language Processing (Nlp)mentioning

confidence: 99%

Artificial Intelligence in Newborn Medicine

Huisman,

Huisman

2024

Newborn

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“…One of the most popular forms of AI involves using a natural language processing (NLP) system. In medicine, researchers have used NLP to extract unstructured data from medical records, followed by organization of the output [6][7][8][9]. Some have advocated for the use of an NLP as a prognostic or diagnostic tool [10][11][12]; however, further investigation is warranted.…”

Section: Introductionmentioning

confidence: 99%

The Potential of ChatGPT as a Self-Diagnostic Tool in Common Orthopedic Diseases: Exploratory Study

Kuroiwa,

Sarcon,

Ibara

et al. 2023

J Med Internet Res

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Background Artificial intelligence (AI) has gained tremendous popularity recently, especially the use of natural language processing (NLP). ChatGPT is a state-of-the-art chatbot capable of creating natural conversations using NLP. The use of AI in medicine can have a tremendous impact on health care delivery. Although some studies have evaluated ChatGPT’s accuracy in self-diagnosis, there is no research regarding its precision and the degree to which it recommends medical consultations. Objective The aim of this study was to evaluate ChatGPT’s ability to accurately and precisely self-diagnose common orthopedic diseases, as well as the degree of recommendation it provides for medical consultations. Methods Over a 5-day course, each of the study authors submitted the same questions to ChatGPT. The conditions evaluated were carpal tunnel syndrome (CTS), cervical myelopathy (CM), lumbar spinal stenosis (LSS), knee osteoarthritis (KOA), and hip osteoarthritis (HOA). Answers were categorized as either correct, partially correct, incorrect, or a differential diagnosis. The percentage of correct answers and reproducibility were calculated. The reproducibility between days and raters were calculated using the Fleiss κ coefficient. Answers that recommended that the patient seek medical attention were recategorized according to the strength of the recommendation as defined by the study. Results The ratios of correct answers were 25/25, 1/25, 24/25, 16/25, and 17/25 for CTS, CM, LSS, KOA, and HOA, respectively. The ratios of incorrect answers were 23/25 for CM and 0/25 for all other conditions. The reproducibility between days was 1.0, 0.15, 0.7, 0.6, and 0.6 for CTS, CM, LSS, KOA, and HOA, respectively. The reproducibility between raters was 1.0, 0.1, 0.64, –0.12, and 0.04 for CTS, CM, LSS, KOA, and HOA, respectively. Among the answers recommending medical attention, the phrases “essential,” “recommended,” “best,” and “important” were used. Specifically, “essential” occurred in 4 out of 125, “recommended” in 12 out of 125, “best” in 6 out of 125, and “important” in 94 out of 125 answers. Additionally, 7 out of the 125 answers did not include a recommendation to seek medical attention. Conclusions The accuracy and reproducibility of ChatGPT to self-diagnose five common orthopedic conditions were inconsistent. The accuracy could potentially be improved by adding symptoms that could easily identify a specific location. Only a few answers were accompanied by a strong recommendation to seek medical attention according to our study standards. Although ChatGPT could serve as a potential first step in accessing care, we found variability in accurate self-diagnosis. Given the risk of harm with self-diagnosis without medical follow-up, it would be prudent for an NLP to include clear language alerting patients to seek expert medical opinions. We hope to shed further light on the use of AI in a future clinical study.

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Development of a text mining algorithm for identifying adverse drug reactions in electronic health records

van de Burgt,

Wasylewicz,

Dullemond

et al. 2024

JAMIA Open

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Objective Adverse drug reactions (ADRs) are a significant healthcare concern. They are often documented as free text in electronic health records (EHRs), making them challenging to use in clinical decision support systems (CDSS). The study aimed to develop a text mining algorithm to identify ADRs in free text of Dutch EHRs. Materials and Methods In Phase I, our previously developed CDSS algorithm was recoded and improved upon with the same relatively large dataset of 35 000 notes (Step A), using R to identify possible ADRs with Medical Dictionary for Regulatory Activities (MedDRA) terms and the related Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) (Step B). In Phase II, 6 existing text-mining R-scripts were used to detect and present unique ADRs, and positive predictive value (PPV) and sensitivity were observed. Results In Phase IA, the recoded algorithm performed better than the previously developed CDSS algorithm, resulting in a PPV of 13% and a sensitivity of 93%. For The sensitivity for serious ADRs was 95%. The algorithm identified 58 additional possible ADRs. In Phase IB, the algorithm achieved a PPV of 10%, a sensitivity of 86%, and an F-measure of 0.18. In Phase II, four R-scripts enhanced the sensitivity and PPV of the algorithm, resulting in a PPV of 70%, a sensitivity of 73%, an F-measure of 0.71, and a 63% sensitivity for serious ADRs. Discussion and Conclusion The recoded Dutch algorithm effectively identifies ADRs from free-text Dutch EHRs using R-scripts and MedDRA/SNOMED-CT. The study details its limitations, highlighting the algorithm's potential and significant improvements.

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Combining text mining with clinical decision support in clinical practice: a scoping review

Cited by 4 publications

References 72 publications

Artificial Intelligence in Newborn Medicine

Artificial Intelligence in Newborn Medicine

The Potential of ChatGPT as a Self-Diagnostic Tool in Common Orthopedic Diseases: Exploratory Study

Development of a text mining algorithm for identifying adverse drug reactions in electronic health records

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