Assessment of Follow-Up for Pulmonary Nodules from Radiology Reports with Natural Language Processing.

Radiology reports are an essential communication method for ensuring smooth workflow in healthcare. However, many of these reports are described in free text, and findings documented by radiologists may not be adequately addressed. In this study, focusing on pulmonary nodules, we evaluated whether cases in which radiologists described follow-up as recommended were receiving appropriate treatment.

Implementation of a Multilingual Electronic Medical Questionnaire: For Use Inside or Outside the Hospital.

We implemented a multilingual medical questionnaire system, which allows patients to answer questionnaires both in and out of the hospital. The response data are sent to and stored as structured data on the server in hospital information system, and could be converted to Japanese and quoted as part of progress notes in the electronic medical record.

Classification of Diagnostic Certainty in Radiology Reports with Deep Learning.

A radiology report is prepared for communicating clinical information about observed abnormal structures and clinically important findings with referring clinicians. However, such observations and findings are often accompanied by ambiguous expressions, which can prevent clinicians from accurately interpreting the content of reports. To systematically assess the degree of diagnostic certainty for each observation and finding in a report, we defined an ordinal scale comprising five classes: definite, likely, may represent, unlikely, and denial.

Development of the Clinical Specimen Information Management System for Multicenter Clinical Studies.

Some multicenter clinical studies require the acquisition of clinical specimens from patients, and the centralized management and analysis of clinical specimens at a research institution. In such cases, it is necessary to manage clinical specimens with anonymized patient information. In addition, clinical specimens need to be managed in connection with clinical information in clinical studies.

Extracting Clinical Information From Japanese Radiology Reports Using a 2-Stage Deep Learning Approach: Algorithm Development and Validation.

Background: Radiology reports are usually written in a free-text format, which makes it challenging to reuse the reports.

Objective: For secondary use, we developed a 2-stage deep learning system for extracting clinical information and converting it into a structured format.

Methods: Our system mainly consists of 2 deep learning modules: entity extraction and relation extraction.

Impact of an Electronic Medical Record-Connected Questionnaire on Efficient Nursing Documentation: Usability and Efficacy Study.

Background: Documentation tasks comprise a large percentage of nurses' workloads. Nursing records were partially based on a report from the patient. However, it is not a verbatim transcription of the patient's complaints but a type of medical record.

Automatic creation of annotations for chest radiographs based on the positional information extracted from radiographic image reports.

Background And Objective: In this study, we tried to create a machine-learning method that detects disease lesions from chest X-ray (CXR) images using a data set annotated with extracted CXR reports information. We set the nodule as the target disease lesion. Manually annotating nodules is costly in terms of time.

Extracting clinical terms from radiology reports with deep learning.

Extracting clinical terms from free-text format radiology reports is a first important step toward their secondary use. However, there is no general consensus on the kind of terms to be extracted. In this paper, we propose an information model comprising three types of clinical entities: observations, clinical findings, and modifiers.

End-to-End Approach for Structuring Radiology Reports.

Radiology reports include various types of clinical information that are used for patient care. Reports are also expected to have secondary uses (e.g.

The Acquisition of Structured Clinical Data from a Document-Based Electronic Medical Record System.

Our hospital stores all clinical records as Portable Document Formats (PDFs). These PDFs are delivered by each system with a document profile XML file. Using this interface, the items thought to be important for clinical studies are described in the document profile XML and delivered to the data warehouse (DWH).