Oversampling effect in pretraining for bidirectional encoder representations from transformers (BERT) to localize medical BERT and enhance biomedical BERT.

Background: Pretraining large-scale neural language models on raw texts has made a significant contribution to improving transfer learning in natural language processing. With the introduction of transformer-based language models, such as bidirectional encoder representations from transformers (BERT), the performance of information extraction from free text has improved significantly in both the general and medical domains. However, it is difficult to train specific BERT models to perform well in domains for which few databases of a high quality and large size are publicly available.

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.

Enhancing evidence-informed policymaking in medicine and healthcare: stakeholder involvement in the Commons Project for rare diseases in Japan.

Background: Although stakeholder involvement in policymaking is attracting attention in the fields of medicine and healthcare, a practical methodology has not yet been established. Rare-disease policy, specifically research priority setting for the allocation of limited research resources, is an area where evidence generation through stakeholder involvement is expected to be effective. We generated evidence for rare-disease policymaking through stakeholder involvement and explored effective collaboration among stakeholders.

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.

Effect of the p.R4810K Variant on the Progression of Intracranial Artery Stenosis: A 15-Year Follow-up Study.

Background And Objectives: Intracranial artery stenosis is the predominant etiology of ischemic stroke in the Asian population. Furthermore, the presence of the p.R4810K variant, which is a susceptibility gene for moyamoya disease, increases the risk of ischemic stroke attributable to large-artery atherosclerosis.

Identification of mild cognitive impairment subtypes predicting conversion to Alzheimer's disease using multimodal data.

Mild cognitive impairment (MCI) is a high-risk condition for conversion to Alzheimer's disease (AD) dementia. However, individuals with MCI show heterogeneous patterns of pathology and conversion to AD dementia. Thus, detailed subtyping of MCI subjects and accurate prediction of the patients in whom MCI will convert to AD dementia is critical for identifying at-risk populations and the underlying biological features.

Practical use of a multicenter clinical research support system connected to electronic medical records.

Background: Electronic medical records (EMRs) are widely used, but in many cases, they are used within a network physically separated from the Internet. Multicenter clinical studies use Internet-connected electronic data capture (EDC) systems to collect data, where data entered into the EMR are manually transcribed into the EDC system. In addition, medical images for clinical research are also collected manually.

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.

Detecting Adverse Drug Events Through the Chronological Relationship Between the Medication Period and the Presence of Adverse Reactions From Electronic Medical Record Systems: Observational Study.

Background: Medicines may cause various adverse reactions. An enormous amount of money and effort is spent investigating adverse drug events (ADEs) in clinical trials and postmarketing surveillance. Real-world data from multiple electronic medical records (EMRs) can make it easy to understand the ADEs that occur in actual patients.

The practice of active patient involvement in rare disease research using ICT: experiences and lessons from the RUDY JAPAN project.

Background: The role of patients in medical research is changing, as more emphasis is being placed on patient involvement, and patient reported outcomes are increasingly contributing to clinical decision-making. Information and communication technology provides new opportunities for patients to actively become involved in research. These trends are particularly noticeable in Europe and the US, but less obvious in Japan.

An Automatic Image Collection System for Multicenter Clinical Studies.

The acquisition of medical images from multiple medial institutions has become important for high-quality clinical studies. In recent years, electronic data submission has enabled the transmission of image data to independent institutions more quickly and easily than before. However, the selection, anonymization, and transmission of medical images still require human resources in the form of clinical research collaborators.

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).

Screening of anticancer drugs to detect drug-induced interstitial pneumonia using the accumulated data in the electronic medical record.

Because drug-induced interstitial pneumonia (DIP) is a serious adverse drug reaction, its quantitative risk with individual medications should be taken into due consideration when selecting a medicine. We developed an algorithm to detect DIP using medical record data accumulated in a hospital. Chest computed tomography (CT) is mainly used for the diagnosis of IP, and chest X-ray reports, KL-6, and SP-D values are used to support the diagnosis.

Method to Identify Diagnostic Rules for Pancreatic Cancer Using Laboratory Data Based on Bayesian Estimation.

Early diagnosis and treatment of pancreatic cancer is challenging. We attempted to find diagnostic rules for pancreatic cancer from laboratory data in the Osaka University Hospital's data warehouse using Bayesian estimation. We calculated the pretest odds based on the number of laboratory tests and the cutoff value at which the diagnostic accuracy is over 20%.

A Document-Based EHR System That Controls the Disclosure of Clinical Documents Using an Access Control List File Based on the HL7 CDA Header.

Electronic health record (EHR) systems are necessary for the sharing of medical information between care delivery organizations (CDOs). We developed a document-based EHR system in which all of the PDF documents that are stored in our electronic medical record system can be disclosed to selected target CDOs. An access control list (ACL) file was designed based on the HL7 CDA header to manage the information that is disclosed.

Evaluation of Secure Computation in a Distributed Healthcare Setting.

Issues related to ensuring patient privacy and data ownership in clinical repositories prevent the growth of translational research. Previous studies have used an aggregator agent to obscure clinical repositories from the data user, and to ensure the privacy of output using statistical disclosure control. However, there remain several issues that must be considered.

Cross-institutional document exchange system using clinical document architecture (CDA) with virtual printing method.

Recently one patient received care from several hospitals at around the same time. When the patient visited a new hospital, the new hospital's physician tried to get patient information the previous hospital. Thus, patient information is frequently exchanged between them.

Development of a database and processing method for detecting hematotoxicity adverse drug events.

Adverse events are detected by monitoring the patient's status, including blood test results. However, it is difficult to identify all adverse events based on recognition by individual doctors. We developed a system that can be used to detect hematotoxicity adverse events according to blood test results recorded in an electronic medical record system.