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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. The aim of this study was to investigate the practice of active involvement of patients in medical research in Japan by utilizing a digital platform, and to analyze the outcomes to clarify what specific approaches could be put into practice. Methods We developed the RUDY JAPAN system, an ongoing rare disease medical research platform, in collaboration with the Rare and Undiagnosed Diseases Study (RUDY) project in the UK. After 2 years of preparation, RUDY JAPAN was launched in December 2017. Skeletal muscle channelopathies were initially selected as target diseases, and hereditary angioedema was subsequently added. Several approaches for active patient involvement were designed through patient-researcher collaboration, namely the Steering Committee, questionnaire development, dynamic consent, and other communication strategies. We analyzed our practices and experiences focusing on how each approach affected and contributed to the research project. Results RUDY JAPAN has successfully involved patients in this research project in various ways. While not a part of the initial decision-making phase to launch the project, patients have increasingly been involved since then. A high level of patient involvement was achieved through the Steering Committee, a governance body that has made a major contribution to RUDY JAPAN, and the process of the questionnaire development. The creation of the Patient Network Forum, website and newsletter cultivated dialogue between patients and researchers. The registry itself allowed patient participation through data input and control of data usage through dynamic consent. Conclusions We believe the initial outcomes demonstrate the feasibility and utility of active patient involvement in Japan. The collaboration realized through RUDY JAPAN was enabled by digital technologies. It allowed busy patients and researchers to find the space to meet and work together for the Steering Committee, questionnaire development and various communication activities. While the practice of active patient involvement in Japan is still in its early stages, this research confirms its viability if the right conditions are in place. (331 words).

Intracranial artery stenosis is the predominant etiology of ischemic stroke in the Asian population. Furthermore, the presence of theUsing a prospective database, we identified adult patients with intracranial artery stenosis who underwent periodic MRI examinations for5 years. We evaluated stenosis progression using a validated visual grading system. We excluded patients diagnosed with moyamoya disease at the time of initial MRI. Genotyping ofAmong 52 eligible patients, 22 (42%) were carriers of theOur findings indicated that the

BackgroundMild cognitive impairment (MCI) is a high-risk condition for conversion to dementias, including Alzheimer's disease (AD) dementia. However, individuals with MCI show heterogeneity in patterns of pathology, and MCI does not always convert to AD dementia. Detailed subtyping of MCI and accurate prediction of the patients in whom MCI will convert to AD dementia may support new trial designs and enable evaluation of the efficacy of drugs within small numbers of patients during clinical trials. MethodsWe constructed a decision tree model by the heterogeneous mixture learning (HML) method, integrating cerebrospinal fluid (CSF) biomarker data, structural MRI data, APOE genotype data, and a recorded age at examination. The decision tree model was applied to predict conversion to AD dementia and to identify subtypes of MCI. After the test performances of HML models were assessed, MCI subjects were classified into some subtypes based on a decision tree. Then, we characterized each MCI subtype in terms of the degree of CSF biomarker abnormalities and brain atrophy, declines of cognitive functions, and gene expression alterations derived from peripheral blood samples.ResultsWe identified five subtypes of MCI using the HML approach and categorized them into three groups: those similar to CN subjects with low conversion rates; those with intermediate conversion rates; and those similar to patients with AD with high conversion rates. Furthermore, the subtypes with intermediate conversion rates were separated into the subtype with CSF biomarker abnormalities or the subtype with brain atrophy. The results from the CSF inflammation marker and gene expression analysis suggested the occurrence of aberrant inflammatory immune responses in the CSF and blood of the subjects in the subtypes with CSF biomarker abnormalities. ConclusionThe subtypes that were identified in this study exhibited varying conversion rates to AD as well as differing levels of biological features. Focusing on specific subtypes in which conversion to AD can be predicted with the most accuracy could enable more efficient clinical trials to be conducted.

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. Therefore, we used the report information to automatically produce training data for the object detection task. Methods First, we use semantic segmentation model PSP-Net to recognize lung fields described in the CXR reports. Next, a classification model ResNeSt-50 is used to discriminate the nodule in segmented right and left field. It also can provide attention map by Grad-Cam. If the attention region corresponds to the location of the nodule in the CXR reports, an attention bounding box is generated. Finally, object detection model Faster-RCNN was performed using generated attention bounding box. The bounding boxes predicted by Faster-RCNN were filtered to satisfy the location extracted from CXR reports. Results For lung field segmentation, a mean intersection of union of 0.889 was achieved in our best model. 15,156 chest radiographs are used for classification. The area under the receiver operating characteristics curve was 0.843 and 0.852 for the left and right lung, respectively. The detection precision of the generated attention bounding box was 0.341 to 0.531 depending on the binary setting for attention map. Through object detection process, the detection precisions of the bounding boxes were improved to 0.567 to 0.800. Conclusion We successfully generated bounding boxes with nodule on CXR images based on the positional information of the diseases extracted from the CXR reports. Our method has the potential to provide bounding boxes for various lung lesions which can reduce the annotation burden for specialists. Short abstract Machine learning for computer aided image diagnosis requires annotation of images, but manual annotation is time-consuming for medical doctor. In this study, we tried to create a machine-learning method that creates bounding boxes with disease lesions on chest X-ray (CXR) images using the positional information extracted from CXR reports. We set the nodule as the target lesion. First, we use PSP-Net to segment the lung field according to the CXR reports. Next, a classification model ResNeSt-50 was used to discriminate the nodule in segmented lung field. We also created an attention map using the Grad-Cam algorithm. If the area of attention matched the area annotated by the CXR report, the coordinate of the bounding box was considered as a possible nodule area. Finally, we used the attention information obtained from the nodule classification model and let the object detection model trained by all of the generated bounding boxes. Through object detection model, the precision of the bounding boxes to detect nodule is improved.

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. Objective In this study, we generated a patient medication history database from physician orders recorded in EMRs, which allowed the period of medication to be clearly identified. Methods We developed a method for detecting ADEs based on the chronological relationship between the presence of an adverse event and the medication period. To verify our method, we detected ADEs with alanine aminotransferase elevation in patients receiving aspirin, clopidogrel, and ticlopidine. The accuracy of the detection was evaluated with a chart review and by comparison with the Roussel Uclaf Causality Assessment Method (RUCAM), which is a standard method for detecting drug-induced liver injury. Results The calculated rates of ADE with ALT elevation in patients receiving aspirin, clopidogrel, and ticlopidine were 3.33% (868/26,059 patients), 3.70% (188/5076 patients), and 5.69% (226/3974 patients), respectively, which were in line with the rates of previous reports. We reviewed the medical records of the patients in whom ADEs were detected. Our method accurately predicted ADEs in 90% (27/30patients) treated with aspirin, 100% (9/9 patients) treated with clopidogrel, and 100% (4/4 patients) treated with ticlopidine. Only 3 ADEs that were detected by the RUCAM were not detected by our method. Conclusions These findings demonstrate that the present method is effective for detecting ADEs based on EMR data.

Background: Electronic health record (EHR) systems are necessary for sharing medical information between care delivery organizations (CDOs). While many standardized data can be shared, it is still difficult to share nonstandardized clinical data. Furthermore, there remains the problem that the EHR system cannot finely control the disclosure status. We have been operating a documentbased electronic medical record system called the Document Archiving and Communication System (DACS) in which printed images of all medical records are stored in PDF format. Objectives: To develop a document-based EHR system that can disclose selected clinical documents in PDF format. Methods: With a document-based EHR system, any documents stored in the DACS can be opened to a designated CDO. These open documents can be selected by the service CDO staff. In order to manage the disclosure status, an access control list (ACL) file was designed based on the HL7 clinical document architecture header. The PDF file and its ACL file were transiently stored in the Japanese standard repository, SS-MIX2. The doctor of the designated CDO was then able to access the documents according to the information in the ACL file. Results: From March 2017 to September 2017, 308 documents of 20 patients were disclosed to 3 CDOs using this document-based EHR system. These documents included examination reports, agreement forms concerning the EHR, progress notes, summaries and surgical reports generated from five different systems. Conclusions: The document-based EHR system was able to reveal clinical documents from the EMR to medical staff under a controlled disclosure environment.

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. The presence of IP in the reports was assessed by a method using natural language‐processing, in which IP was estimated according to the product of the likelihood ratio of characteristic keywords in each report. The sensitivity and the specificity of the method for chest CT reports were 0.92 and 0.97, while those for chest X‐ray reports were 0.83 and 1, respectively. The occurrence of DIP was estimated by the patterns of presence of IP before, during, and after the administration of the target medicine. The occurrence rate of DIP in cases administered Gefitinib; Methotrexate (MTX); Tegafur, Gimeracil, and Oteracil potassium (TS‐1); and Tegafur and Uracil (UTF) was 6.0%, 2.3%, 1.4%, and 0.7%, respectively. The estimated DIP cases were checked by having the medical records independently reviewed by medical doctors. By chart review, the positive predictive values of DIP against Gefitinib, MTX, TS‐1, and UFT were 69.2%, 44.4%, 58.6%, and 77.8%, respectively. Although the cases extracted by this method included some that did not have DIP, this method can estimate the relative risk of DIP between medicines.