Inferred joint multigram models for medical term normalization according to ICD

Pérez, Alicia; Atutxa, Aitziber; Casillas, Arantza; Gojenola, Koldo; Sellart, Álvaro

doi:10.1016/j.ijmedinf.2017.12.007

Cited by 13 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While the data included some standard codes, developing automated assistance in assigning codes for other non-English data sets is still a challenge. Two papers by the same research team, one of which was selected as a Best Paper 10 , address this issue for clinical documentation in Spanish 10 , 11 . Continued research on both automated methods for assigning standard codes for non-English clinical text as well as methods to link different code sets to each other and to analytic approaches will facilitate exchange of information for both operational and research uses.…”

Section: Resultsmentioning

confidence: 99%

Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management

Bloomrosen¹,

Berner

2019

Yearb Med Inform

View full text Add to dashboard Cite

Objectives: To summarize the recent literature and research and present a selection of the best papers published in 2018 in the field of Health Information Management (HIM) and Health Informatics. Methods: A systematic review of the literature was performed, with the help of a medical librarian, by the two editors of the HIM section of the International Medical Informatics Association (IMIA) Yearbook. In order to include papers that would address the special theme of the 2019 Yearbook on artificial intelligence (AI) as well as HIM, we searched bibliographic databases for HIM-related papers with an AI focus using both Medical Subject Headings (MeSH) descriptors and keywords in titles and abstracts. A shortlist of 15 candidate best papers was first selected by section editors before being peer-reviewed by independent external reviewers. Results: While there were a significant number of manuscripts that addressed issues relevant to HIM, there were virtually none with MeSH headings indicating an HIM focus. Manuscripts that were considered related to the HIM field in terms of the practice of health information management as well as the profession included those that examined using machine learning and other AI approaches to identify protected health information in clinical text to aid with de-identification, automated coding approaches to translate free-text into standardized codes, and natural language processing approaches to extract clinical data to assist with populating cancer and other registries. Conclusions: The papers discussed in the HIM section reflect the special theme of the use of AI in healthcare on issues particularly relevant to the field of HIM. This synopsis discusses these papers and recommends that HIM practitioners be more involved in research and that researchers in AI and related areas recognize the applicability and relevance of their work to the field of HIM.

show abstract

Section: Resultsmentioning

confidence: 99%

Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management

Bloomrosen¹,

Berner

2019

Yearb Med Inform

View full text Add to dashboard Cite

show abstract

“…Different conventional classifiers like Naïve Bayes (Chute et al, 2006), C4.5 decision tree (Farkas and Szarvas, 2008), SVM (Perotte et al, 2013), and random forest (Atutxa et al, 2018) have been confirmed to be capable of assigning a category code to text input for different medical encoding systems. Pérez et al (2018) proposed an encoding mechanism based on a transformer architecture. CNN and RNN encoding and their combinations have been evaluated.…”

Section: Related Studiesmentioning

confidence: 99%

Classification of user queries according to a hierarchical medical procedure encoding system using an ensemble classifier

Deng

Denecke

2022

Front. Artif. Intell.

View full text Add to dashboard Cite

The Swiss classification of surgical interventions (CHOP) has to be used in daily practice by physicians to classify clinical procedures. Its purpose is to encode the delivered healthcare services for the sake of quality assurance and billing. For encoding a procedure, a code of a maximal of 6-digits has to be selected from the classification system, which is currently realized by a rule-based system composed of encoding experts and a manual search in the CHOP catalog. In this paper, we will investigate the possibility of automatic CHOP code generation based on a short query to enable automatic support of manual classification. The wide and deep hierarchy of CHOP and the differences between text used in queries and catalog descriptions are two apparent obstacles for training and deploying a learning-based algorithm. Because of these challenges, there is a need for an appropriate classification approach. We evaluate different strategies (multi-class non-terminal and per-node classifications) with different configurations so that a flexible modular solution with high accuracy and efficiency can be provided. The results clearly show that the per-node binary classification outperforms the non-terminal multi-class classification with an F1-micro measure between 92.6 and 94%. The hierarchical prediction based on per-node binary classifiers achieved a high exact match by the single code assignment on the 5-fold cross-validation. In conclusion, the hierarchical context from the CHOP encoding can be employed by both classifier training and representation learning. The hierarchical features have all shown improvement in the classification performances under different configurations, respectively: the stacked autoencoder and training examples aggregation using true path rules as well as the unified vocabulary space have largely increased the utility of hierarchical features. Additionally, the threshold adaption through Bayesian aggregation has largely increased the vertical reachability of the per node classification. All the trainable nodes can be triggered after the threshold adaption, while the F1 measures at code levels 3–6 have been increased from 6 to 89% after the threshold adaption.

show abstract

“…Use of development set 16 (21%) [12,29,31,34,37,49,55,60,63,69,74,80,87,90,94,95] Not listed 4 (5.2%) [30,82,83,101] publications did not meet our criteria, of which 3 publications in which the algorithm was not evaluated, resulting in 77 included articles describing 77 studies. Reference checking did not provide any additional publications.…”

Section: Development Of Algorithmmentioning

confidence: 99%

Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies

et al. 2020

View full text Add to dashboard Cite

Background Free-text descriptions in electronic health records (EHRs) can be of interest for clinical research and care optimization. However, free text cannot be readily interpreted by a computer and, therefore, has limited value. Natural Language Processing (NLP) algorithms can make free text machine-interpretable by attaching ontology concepts to it. However, implementations of NLP algorithms are not evaluated consistently. Therefore, the objective of this study was to review the current methods used for developing and evaluating NLP algorithms that map clinical text fragments onto ontology concepts. To standardize the evaluation of algorithms and reduce heterogeneity between studies, we propose a list of recommendations. Methods Two reviewers examined publications indexed by Scopus, IEEE, MEDLINE, EMBASE, the ACM Digital Library, and the ACL Anthology. Publications reporting on NLP for mapping clinical text from EHRs to ontology concepts were included. Year, country, setting, objective, evaluation and validation methods, NLP algorithms, terminology systems, dataset size and language, performance measures, reference standard, generalizability, operational use, and source code availability were extracted. The studies’ objectives were categorized by way of induction. These results were used to define recommendations. Results Two thousand three hundred fifty five unique studies were identified. Two hundred fifty six studies reported on the development of NLP algorithms for mapping free text to ontology concepts. Seventy-seven described development and evaluation. Twenty-two studies did not perform a validation on unseen data and 68 studies did not perform external validation. Of 23 studies that claimed that their algorithm was generalizable, 5 tested this by external validation. A list of sixteen recommendations regarding the usage of NLP systems and algorithms, usage of data, evaluation and validation, presentation of results, and generalizability of results was developed. Conclusion We found many heterogeneous approaches to the reporting on the development and evaluation of NLP algorithms that map clinical text to ontology concepts. Over one-fourth of the identified publications did not perform an evaluation. In addition, over one-fourth of the included studies did not perform a validation, and 88% did not perform external validation. We believe that our recommendations, alongside an existing reporting standard, will increase the reproducibility and reusability of future studies and NLP algorithms in medicine.

show abstract

Inferred joint multigram models for medical term normalization according to ICD

Cited by 13 publications

References 6 publications

Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management

Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management

Classification of user queries according to a hierarchical medical procedure encoding system using an ensemble classifier

Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies

Contact Info

Product

Resources

About