De-identification of clinical free text using natural language processing: A systematic review of current approaches

Kovačević, Aleksandar; Bašaragin, Bojana; Milošević, Nikola; Nenadić, Goran

doi:10.1016/j.artmed.2024.102845

Cited by 5 publications

(2 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For secondary use ( Figure 1 ), the Personal Health Record must be de-identified ( 31 ) to make anonymized or pseudonymized data versions. This process involves the removal of any information that could lead to the identification of the subject, while preserving the reliability of the data.…”

Section: Evaluation Of the Proposed Architecturementioning

confidence: 99%

“…First, as such systems are data processors according to the GDPR, they must process, protect, and secure data accordingly. Therefore, accessing data for secondary purposes is difficult due to complex content management and the need for de-identification (anonymization and pseudonymization) ( 31 ). Second, in such systems, the dilemma of data comprehensiveness has not been solved because of the international mobility of citizens.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A reference architecture for personal health data spaces using decentralized content-addressable storage networks

Klementi,

Piho,

Ross

2024

Front. Med.

View full text Add to dashboard Cite

IntroductionThis paper addresses the dilemmas of accessibility, comprehensiveness, and ownership related to health data. To resolve these dilemmas, we propose and justify a novel, globally scalable reference architecture for a Personal Health Data Space (PHDS). This architecture leverages decentralized content-addressable storage (DCAS) networks, ensuring that the data subject retains complete control and ownership of their personal health data. In today's globalized world, where people are increasingly mobile for work and leisure, healthcare is transitioning from episodic symptom-based treatment toward continuity of care. The main aims of this are patient engagement, illness prevention, and active and healthy longevity. This shift, along with the secondary use of health data for societal benefit, has intensified the challenges associated with health data accessibility, comprehensiveness, and ownership.MethodThe study is structured around four health data use case scenarios from the Estonian National Health Information System (EHIS): primary medical use, medical emergency use, secondary use, and personal use. We analyze these use cases from the perspectives of accessibility, comprehensiveness, and ownership. Additionally, we examine the security, privacy, and interoperability aspects of health data.ResultsThe proposed architectural solution allows individuals to consolidate all their health data into a unified Personal Health Record (PHR). This data can come from various healthcare institutions, mobile applications, medical devices for home use, and personal health notes.DiscussionsThe comprehensive PHR can then be shared with healthcare providers in a semantically interoperable manner, regardless of their location or the information systems they use. Furthermore, individuals maintain the autonomy to share, sell, or donate their anonymous or pseudonymous health data for secondary use with different systems worldwide. The proposed reference architecture aligns with the principles of the European Health Data Space (EHDS) initiative, enhancing health data management by providing a secure, cost-effective, and sustainable solution.

show abstract

Section: Evaluation Of the Proposed Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A reference architecture for personal health data spaces using decentralized content-addressable storage networks

Klementi,

Piho,

Ross

2024

Front. Med.

View full text Add to dashboard Cite

show abstract

From zero to hero: Harnessing transformers for biomedical named entity recognition in zero- and few-shot contexts

Košprdić,

Prodanović,

Ljajić

et al. 2024

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

Combination of machine learning algorithms with natural language processing may increase the probability of bacteremia detection in the emergency department: A retrospective, big-data analysis of 94,482 patients

Ben-Haim,

Yosef,

Rowand

et al. 2024

DIGITAL HEALTH

View full text Add to dashboard Cite

Background Prompt diagnosis of bacteremia in the emergency department (ED) is of utmost importance. Nevertheless, the average time to first clinical laboratory finding range from 1 to 3 days. Alongside a myriad of scoring systems for occult bacteremia prediction, efforts for applying artificial intelligence (AI) in this realm are still preliminary. In the current study we combined an AI algorithm with a Natural Language Processing (NLP) algorithm that would potentially increase the yield extracted from clinical ED data. Methods This study involved adult patients who visited our emergency department and at least one blood culture was taken to rule out bacteremia. Using both tabular and free text data, we built an ensemble model that leverages XGBoost for structured data, and logistic regression (LR) on a word-analysis technique called bag-of-words (BOW) Term Frequency-Inverse Document Frequency (TF-IDF), for textual data. All algorithms were designed in order to predict the risk for bacteremia with ED patients whose blood cultures were sent to the laboratory. Results The study cohort comprised 94,482 individuals, of whom 52% were males. The prevalence of bacteremia in the entire cohort was 9.7%. The model trained on the tabular data yielded an area under the curve (AUC) of 73.7% for XGBoost, while the LR that was trained on the free text achieved an AUC of 71.3%. After checking a range of weights, the best combination was for 55% weight on the XGBoost prediction and 45% weight on the LR prediction. The final model prediction yielded an AUC of 75.6%. Conclusion Harnessing artificial intelligence to the task of bacteremia surveillance in the ED settings by a combination of both free text and tabular data analysis improved predictive performance compared to using tabular data alone. We recommend that future AI applications based on our findings should be assimilated into the clinical routines of ED physicians.

show abstract

De-identification of clinical free text using natural language processing: A systematic review of current approaches

Cited by 5 publications

References 84 publications

A reference architecture for personal health data spaces using decentralized content-addressable storage networks

A reference architecture for personal health data spaces using decentralized content-addressable storage networks

From zero to hero: Harnessing transformers for biomedical named entity recognition in zero- and few-shot contexts

Combination of machine learning algorithms with natural language processing may increase the probability of bacteremia detection in the emergency department: A retrospective, big-data analysis of 94,482 patients

Contact Info

Product

Resources

About