Automated Classification of Selected Data Elements from Free-text Diagnostic Reports for Clinical Research

Löpprich, Martin; Krauss, Felix; Ganzinger, Matthias; Senghas, K.; Riezler, Stefan; Knaup, Petra

doi:10.3414/me15-02-0019

Cited by 15 publications

(7 citation statements)

References 20 publications

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“…unstructured and not standardized. Such data can only be processed by computerized application systems if natural language processing (NLP) methods are used [ 58 ], which is considerably difficult not only for documents in the Japanese language [ 59 ] but also for those in the German language [ 60 ].…”

Section: Resultsmentioning

confidence: 99%

Quality Requirements for Electronic Health Record Systems

Winter¹,

Takabayashi²,

Jahn³

et al. 2017

Methods Inf Med

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SummaryBackground: For more than 30 years, there has been close cooperation between Japanese and German scientists with regard to information systems in health care. Collaboration has been formalized by an agreement between the respective scientific associa tions. Following this agreement, two joint workshops took place to explore the similarities and differences of electronic health record systems (EHRS) against the background of the two national healthcare systems that share many commonalities. Objectives: To establish a framework and requirements for the quality of EHRS that may also serve as a basis for comparing different EHRS.Methods: Donabedian's three dimensions of quality of medical care were adapted to the outcome, process, and structural quality of EHRS and their management. These quality dimensions were proposed before the first workshop of EHRS experts and enriched during the discussions. Results: The Quality Requirements Framework of EHRS (QRF-EHRS) was defined and complemented by requirements for high quality EHRS. The framework integrates three quality dimensions (outcome, process, and structural quality), three layers of information systems (processes and data, applications, and physical tools) and three dimensions of information management (strategic, tactical, and operational information management). Conclusions: Describing and comparing the quality of EHRS is in fact a multidimensional problem as given by the QRF-EHRS framework. This framework will be utilized to compare Japanese and German EHRS, notably those that were presented at the second workshop.

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Section: Resultsmentioning

confidence: 99%

Quality Requirements for Electronic Health Record Systems

Winter¹,

Takabayashi²,

Jahn³

et al. 2017

Methods Inf Med

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“…Nevertheless, we only found one case in which NLP was utilized to retrieve patient data from unstructured free text on MM. Löpprich, Krauss et al [7] created a “multiclass classification of free-text diagnostic reports” and “a framework to enable automatic multi-class classification of relevant data elements from free-text diagnostic reports”. Their aim was to automatically document “diagnosis and state of disease of myeloma patients” from clinical reports.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover NLP finds application in oncology for case identification and for disease stages and outcome determination [3]. NLP has been used to query clinical reports on MM-specific parameters [7]. However, our ontology provides much more extensive querying than these trials [7].…”

Section: Introductionmentioning

confidence: 99%

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Exploration of Artificial Intelligence Use with ARIES in Multiple Myeloma Research

Loda

Krebs

Danhof

et al. 2019

JCM

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Background: Natural language processing (NLP) is a powerful tool supporting the generation of Real-World Evidence (RWE). There is no NLP system that enables the extensive querying of parameters specific to multiple myeloma (MM) out of unstructured medical reports. We therefore created a MM-specific ontology to accelerate the information extraction (IE) out of unstructured text. Methods: Our MM ontology consists of extensive MM-specific and hierarchically structured attributes and values. We implemented “A Rule-based Information Extraction System” (ARIES) that uses this ontology. We evaluated ARIES on 200 randomly selected medical reports of patients diagnosed with MM. Results: Our system achieved a high F1-Score of 0.92 on the evaluation dataset with a precision of 0.87 and recall of 0.98. Conclusions: Our rule-based IE system enables the comprehensive querying of medical reports. The IE accelerates the extraction of data and enables clinicians to faster generate RWE on hematological issues. RWE helps clinicians to make decisions in an evidence-based manner. Our tool easily accelerates the integration of research evidence into everyday clinical practice.

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“…Richter-Pechanski et al showed the application of NLP on German texts with the goal of de-identification. [13] Another group of researchers from the University of Heidelberg used NLP technologies to extract diagnoses from German diagnostic reports[14].…”

Section: Introductionmentioning

confidence: 99%

Knowledge-based best of breed approach for automated detection of clinical events based on German free text digital hospital discharge letters

König

Sander²,

Demuth

et al. 2019

PLoS ONE

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ObjectivesThe secondary use of medical data contained in electronic medical records, such as hospital discharge letters, is a valuable resource for the improvement of clinical care (e.g. in terms of medication safety) or for research purposes. However, the automated processing and analysis of medical free text still poses a huge challenge to available natural language processing (NLP) systems. The aim of this study was to implement a knowledge-based best of breed approach, combining a terminology server with integrated ontology, a NLP pipeline and a rules engine.MethodsWe tested the performance of this approach in a use case. The clinical event of interest was the particular drug-disease interaction “proton-pump inhibitor [PPI] use and osteoporosis”. Cases were to be identified based on free text digital discharge letters as source of information. Automated detection was validated against a gold standard.ResultsPrecision of recognition of osteoporosis was 94.19%, and recall was 97.45%. PPIs were detected with 100% precision and 97.97% recall. The F-score for the detection of the given drug-disease-interaction was 96,13%.ConclusionWe could show that our approach of combining a NLP pipeline, a terminology server, and a rules engine for the purpose of automated detection of clinical events such as drug-disease interactions from free text digital hospital discharge letters was effective. There is huge potential for the implementation in clinical and research contexts, as this approach enables analyses of very high numbers of medical free text documents within a short time period.

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Automated Classification of Selected Data Elements from Free-text Diagnostic Reports for Clinical Research

Abstract: The low average error rates and high average F1-scores of each pipeline demonstrate the suitability of the investigated NPL methods. However, it was also shown that there is no best practice for an automatic classification of data elements from free-text diagnostic reports.

Cited by 15 publications

References 20 publications

Quality Requirements for Electronic Health Record Systems

Quality Requirements for Electronic Health Record Systems

Exploration of Artificial Intelligence Use with ARIES in Multiple Myeloma Research

Knowledge-based best of breed approach for automated detection of clinical events based on German free text digital hospital discharge letters

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