Use of unstructured text in prognostic clinical prediction models: a systematic review

Abstract

Objective: This systematic review aims to assess how information from unstructured clinical text is used to develop and validate prognostic risk prediction models. We summarize the prediction problems and methodological landscape and assess whether using unstructured clinical text data in addition to more commonly used structured data improves the prediction performance. Materials and Methods: We searched Embase, MEDLINE, Web of Science, and Google Scholar to identify studies that developed prognostic risk prediction models using unstructured clinical text data published in the period from January 2005 to March 2021. Data items were extracted, analyzed, and a meta-analysis of the model performance was carried out to assess the added value of text to structured-data models. Results: We identified 126 studies that described 145 clinical prediction problems. Combining text and structured data improved model performance, compared to using only text or only structured data. In these studies, a wide variety of dense and sparse numeric text representations were combined with both deep learning and more traditional machine learning methods. External validation, public availability, and explainability of the developed models was limited. Conclusion: Overall, the use of unstructured clinical text data in the development of prognostic prediction models has been found beneficial in addition to structured data in most studies. The EHR text data is a source of valuable information for prediction model development and should not be neglected. We suggest a future focus on explainability and external validation of the developed models, promoting robust and trustworthy prediction models in clinical practice.

System for digital character recognition, extraction and structuring of medical data with the formation of a cloud-based electronic health records

“…МИС можно рассматривать как шаг к созданию нового типа облачной ЭМК с динамическим накоплением и постобработкой данных [7]. Извлечение ретроспективных данных из неструктурированных документов может использоваться для построения прогностических моделей и разработок программ профилактики [8].…”

Künstliche Intelligenz in der Nephrologie

“…Im klinischen Alltag werden Informationen vielfach auch als unstrukturierte Textdaten wie klinische Notizen, Arztbriefe oder (Radiologie-)Berichte gespeichert. Diese haben das Potenzial, die Performanz von ML-basierten Risikovorhersagesystemen zu verbessern, und zwar durch die Integration der zusätzlichen Informationen [19]. Erste Arbeiten konnten bereits im Bereich AKI zeigen, dass semistrukturierte Daten zum Training vom ML-System erfolgreich eingesetzt werden können [20].…”