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. 2022 Sep 14:13:946387.
doi: 10.3389/fpsyt.2022.946387. eCollection 2022.

Natural language processing in clinical neuroscience and psychiatry: A review

Claudio Crema  1 , Giuseppe Attardi  2 , Daniele Sartiano  3 , Alberto Redolfi  1
Affiliations

Natural language processing in clinical neuroscience and psychiatry: A review

Claudio Crema et al. Front Psychiatry. .

Abstract

Natural language processing (NLP) is rapidly becoming an important topic in the medical community. The ability to automatically analyze any type of medical document could be the key factor to fully exploit the data it contains. Cutting-edge artificial intelligence (AI) architectures, particularly machine learning and deep learning, have begun to be applied to this topic and have yielded promising results. We conducted a literature search for 1,024 papers that used NLP technology in neuroscience and psychiatry from 2010 to early 2022. After a selection process, 115 papers were evaluated. Each publication was classified into one of three categories: information extraction, classification, and data inference. Automated understanding of clinical reports in electronic health records has the potential to improve healthcare delivery. Overall, the performance of NLP applications is high, with an average F1-score and AUC above 85%. We also derived a composite measure in the form of Z-scores to better compare the performance of NLP models and their different classes as a whole. No statistical differences were found in the unbiased comparison. Strong asymmetry between English and non-English models, difficulty in obtaining high-quality annotated data, and train biases causing low generalizability are the main limitations. This review suggests that NLP could be an effective tool to help clinicians gain insights from medical reports, clinical research forms, and more, making NLP an effective tool to improve the quality of healthcare services.

Keywords: deep learning; electronic health record; information extraction; natural language processing; neuroscience; psychiatry.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Number of scientific papers with keyword “Natural Language Processing” published on PubMed from 1990 to 2021, normalized on the number of total papers published on PubMed.
FIGURE 2
FIGURE 2
PRISMA chart.
FIGURE 3
FIGURE 3
Performance box plot of the NLP methods representing the medians, interquartile ranges, outliers. IE, information extraction; ML, traditional machine learning; DL, deep learning; Patients, patients texts; Clinical, clinical notes; Disp., patients’ disposition; Cand., best candidates; Pathol., specific pathologies.
See this image and copyright information in PMC

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