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. OBJECTIVE: We hypothesized that this problem could be addressed by oversampling a domain-specific corpus and using it for pretraining with a larger corpus in a balanced manner. In the present study, we verified our hypothesis by developing pretraining models using our method and evaluating their performance. METHODS: Our proposed method was based on the simultaneous pretraining of models with knowledge from distinct domains after oversampling. We conducted three experiments in which we generated (1) English biomedical BERT from a small biomedical corpus, (2) Japanese medical BERT from a small medical corpus, and (3) enhanced biomedical BERT pretrained with complete PubMed abstracts in a balanced manner. We then compared their performance with those of conventional models. RESULTS: Our English BERT pretrained using both general and small medical domain corpora performed sufficiently well for practical use on the biomedical language understanding evaluation (BLUE) benchmark. Moreover, our proposed method was more effective than the conventional methods for each biomedical corpus of the same corpus size in the general domain. Our Japanese medical BERT outperformed the other BERT models built using a conventional method for almost all the medical tasks. The model demonstrated the same trend as that of the first experiment in English. Further, our enhanced biomedical BERT model, which was not pretrained on clinical notes, achieved superior clinical and biomedical scores on the BLUE benchmark with an increase of 0.3 points in the clinical score and 0.5 points in the biomedical score. These scores were above those of the models trained without our proposed method. CONCLUSIONS: Well-balanced pretraining using oversampling instances derived from a corpus appropriate for the target task allowed us to construct a high-performance BERT model.

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. In this study, we have developed a clinical specimen information management system that works with electronic data capture system for efficient specimen information management and the system workflow has verified at Osaka University Hospital. In addition, by combining this system with medical image collection system that we have developed previously, the integrated management of clinical information, medical image, and clinical specimen information will become possible. This specimen information management system may be expected to provide the platform for integrated analysis utilizing clinical information, medical image, and data from clinical specimens in multicenter clinical studies.

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. Furthermore, we applied a deep learning classification model to determine its applicability to in-house radiology reports. We trained and evaluated the model using 540 in-house chest computed tomography reports. The deep learning model achieved a micro F1-score of 97.61%, which indicated that our ordinal scale was suitable for measuring the diagnostic certainty of observations and findings in a report.

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.

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. For each module, state-of-the-art deep learning models were applied. We trained and evaluated the models using 1040 in-house Japanese computed tomography (CT) reports annotated by medical experts. We also evaluated the performance of the entire pipeline of our system. In addition, the ratio of annotated entities in the reports was measured to validate the coverage of the clinical information with our information model. Results: The microaveraged F1-scores of our best-performing model for entity extraction and relation extraction were 96.1% and 97.4%, respectively. The microaveraged F1-score of the 2-stage system, which is a measure of the performance of the entire pipeline of our system, was 91.9%. Our system showed encouraging results for the conversion of free-text radiology reports into a structured format. The coverage of clinical information in the reports was 96.2% (6595/6853). Conclusions: Our 2-stage deep system can extract clinical information from chest and abdomen CT reports accurately and comprehensively.

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. Therefore, to reduce the time spent on nursing documentation, it is necessary to assist in the appropriate conversion or citation of patient reports to professional records. However, few studies have been conducted on systems for capturing patient reports in electronic medical records. In addition, there have been no reports on whether such a system reduces the time spent on nursing documentation. OBJECTIVE: This study aims to develop a patient self-reporting system that appropriately converts data to nursing records and evaluate its effect on reducing the documenting burden for nurses. METHODS: An electronic medical record-connected questionnaire and a preadmission nursing questionnaire were administered. The questionnaire responses entered by the patients were quoted in the patient profile for inpatient assessment in the nursing system. To clarify its efficacy, this study examined whether the use of the electronic questionnaire system saved the nurses' time entering the patient profile admitted between August and December 2022. It also surveyed the usability of the electronic questionnaire between April and December 2022. RESULTS: A total of 3111 (78%) patients reported that they answered the electronic medical questionnaire by themselves. Of them, 2715 (88%) felt it was easy to use and 2604 (85%) were willing to use it again. The electronic questionnaire was used in 1326 of 2425 admission cases (use group). The input time for the patient profile was significantly shorter in the use group than in the no-use group (P<.001). Stratified analyses showed that in the internal medicine wards and in patients with dependent activities of daily living, nurses took 13%-18% (1.3 to 2 minutes) less time to enter patient profiles within the use group (both P<.001), even though there was no difference in the amount of information. By contrast, in the surgical wards and in the patients with independent activities of daily living, there was no difference in the time to entry (P=.50 and P=.20, respectively), but there was a greater amount of information in the use group. CONCLUSIONS: The study developed and implemented a system in which self-reported patient data were captured in the hospital information network and quoted in the nursing system. This system contributes to improving the efficiency of nurses' task recordings.

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. METHODS: We constructed a space called 'Evidence-generating Commons', where patients, family members, researchers, and former policymakers can share their knowledge and experiences and engage in continual deliberations on evidence generation. Ten rare diseases were consequently represented. In the 'Commons', 25 consecutive workshops were held predominantly online, from 2019 to 2021. These workshops focused on (1) clarification of difficulties faced by rare-disease patients, (2) development and selection of criteria for priority setting, and (3) priority setting through the application of the criteria. For the first step, an on-site workshop using sticky notes was held. The data were analysed based on KJ method. For the second and third steps, workshops on specific themes were held to build consensus. The workshop agendas and methods were modified based on participants' feedback. RESULTS: The 'Commons' was established with 43 participants, resulting in positive effects such as capacity building, opportunities for interactions, mutual understanding, and empathy among the participants. The difficulties faced by patients with rare diseases were classified into 10 categories. Seven research topics were identified as priority issues to be addressed including 'impediments to daily life', 'financial burden', 'anxiety', and 'burden of hospital visits'. This was performed by synthesising the results of the application of the two criteria that were particularly important to strengthen future research on rare diseases. We also clarified high-priority research topics by using criteria valued more by patients and family members than by researchers and former policymakers, and criteria with specific perspectives. CONCLUSION: We generated evidence for policymaking in the field of rare diseases. This study's insights into stakeholder involvement can enhance evidence-informed policymaking. We engaged in comprehensive discussions with policymakers regarding policy implementation and planned analysis of the participants' experiences in this project.

Stakeholder involvement is significant for effective policymaking in the field of rare diseases. However, practical methods for this involvement have not yet been established. Therefore, we developed the 'Commons project' to generate valuable policymaking information and explore effective ways for stakeholders' collaboration. This article explains the process and results of 25 continuous workshops, held from 2019 to 2021 with 43 participants, including patients, family members, researchers, and former policymakers. The main achievements of the discussion that took place in the 'Commons' included a presentation of the overview of the difficulties faced by patients with rare diseases and formulation of high priority research topics.First, the difficulties faced by patients with rare diseases were grouped into 10 categories. Second, seven research topics were identified as priority issues including 'impediments to daily life', 'financial burden', 'anxiety', and 'burden of hospital visits'. During the project process, positive effects such as capacity building, opportunities for interactions, mutual understanding, and empathy among the participants, were identified. Beyond the context of the field of rare diseases and science of policy, these findings are useful for the future of society, including co-creation among stakeholders and patient and public involvement. Based on this study's results, we have initiated communications with policy stakeholders in the field of rare diseases, with the aim of policy implementation.

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. To this end, we developed a model that simultaneously subtypes MCI subjects and predicts conversion to AD and performed an analysis of the underlying biological characteristics of each subtype. In particular, a heterogeneous mixture learning (HML) method was used to build a decision tree-based model based on multimodal data, including cerebrospinal fluid (CSF) biomarker data, structural magnetic resonance imaging (MRI) data, APOE genotype data, and age at examination. The HML model showed an average F1 score of 0.721, which was comparable to the random forest method and had significantly more predictive accuracy than the CART method. The HML-generated decision tree was also used to classify-five subtypes of MCI. Each MCI subtype was characterized in terms of the degree of abnormality in CSF biomarkers, brain atrophy, and cognitive decline. The five subtypes of MCI were further categorized into three groups: one subtype with low conversion rates (similar to cognitively normal subjects); three subtypes with moderate conversion rates; and one subtype with high conversion rates (similar to AD dementia patients). The subtypes with moderate conversion rates were subsequently separated into a group with CSF biomarker abnormalities and a group with brain atrophy. The subtypes identified in this study exhibited varying MCI-to-AD conversion rates and differing biological profiles.

Effect of the RNF213 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 RNF213 p.R4810K variant, which is a susceptibility gene for moyamoya disease, increases the risk of ischemic stroke attributable to large-artery atherosclerosis. Accordingly, we hypothesized that this genetic variant may affect the long-term outcome of intracranial artery stenosis in the East Asian population. We thus aimed to examine the effect of this variant on the long-term progression and prognosis of intracranial artery stenosis. Methods: Using a prospective database, we identified adult patients with intracranial artery stenosis who underwent periodic MRI examinations for >5 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 of RNF213 p.R4810K was performed at the end of the follow-up period. Results: Among 52 eligible patients, 22 (42%) were carriers of the RNF213 p.R4810K variant. The median follow-up duration was 10.3 years. During the follow-up period, progression of intracranial artery stenosis was observed in 64% variant carriers and 27% noncarriers. There was a significant association of the variant with time to progression of intracranial artery stenosis (hazard ratio [HR] 3.31, 95% CI 1.38-7.90, p = 0.007), and time to the composite endpoint of symptomatic stroke and transient ischemic attack (HR 3.70, 95% CI 1.15-11.86, p = 0.028), but not to symptomatic stroke alone (HR 2.18, 95% CI 0.62-7.74, p = 0.23). Two variant carriers with progression were newly diagnosed with moyamoya disease. Discussion: Our findings indicated that the RNF213 p.R4810K variant increases the risk of intracranial artery stenosis progression.

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. 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.

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. 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.

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. Variations in EMRs and differing data structures among vendors hamper the use of data for clinical research. METHODS: We solved this problem by developing a network infrastructure for clinical research between Osaka University Hospital and affiliated hospitals in the Osaka area and introducing a clinical data collection system (CDCS). In each hospital's EMR network, we implemented a CRF reporter that accumulated data for clinical research using a template and then sent the data to a management server in the Osaka University Hospital Data Center. To organize the patient profile data and clinical laboratory data stored in each EMR for use in clinical research, the data are retrieved from the template by an interface module developed by each vendor, according to our common data output interface specification. The data entered into the CRF reporter template for clinical research are also recorded in the EMR progress notes and sent to the data management server. This network infrastructure can also be used as a medical image collection system that automatically collects images for research from PACS at each hospital. These systems are managed under common subject numbers issued by the CDCS. RESULTS: A network infrastructure was established among 19 hospitals, and a CRF reporter was incorporated into the EMR. A medical image transfer system was introduced in 13 hospitals. Since 2013, 28 clinical studies have been conducted using this system, and data for 9,987 cases have been collected as of December 31, 2020. CONCLUSION: Incorporating a CRF reporter with medical image transfer system into the EMR has proven useful for collecting research data.

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. 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).

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. In this study, we developed an image collection system that works with the electronic data capture (EDC) system. In this image collection system, medical images are selected based on EDC input information, patient ID is anonymized to a subject ID issued by the EDC, and the selected anonymized images are transferred to the research institute without human intervention. In the research institute, clinical information registered by the EDC and clinical images collected by the image collection system are managed by the same subject ID and can be used for clinical studies. In October 2019, our image collection system was introduced to 13 medical institutions and has now begun collecting medical images from the in-hospital picture archiving and communication system (PACS) of those institutions.

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). In case clinical data not stored in the DWH are needed, we extract the data from PDF documents. Even from scanned PDFs, the data can be extracted with high accuracy.

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. 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.

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.

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%. By this method, we identified diagnostic rules of 6 types for one item and 79 types for 2 items. Pancreatic cancer is difficult to detect from only general laboratory tests. However, this method may be promising in early diagnosis.

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. One such issue is that a data breach may occur when multiple nodes conspire. Another is that the agent may eavesdrop on or leak a user's queries and their results. We have implemented a secure computing method so that the data used by each party can be kept confidential even if all of the other parties conspire to crack the data. We deployed our implementation at three geographically distributed nodes connected to a high-speed layer two network. The performance of our method, with respect to processing times, suggests suitability for practical use.

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. The blood test results were graded based on Common Terminology Criteria for Adverse Events (CTCAE) and changes in the blood test results (Up, Down, Flat) were assessed according to the variation in the grade. The changes in the blood test and injection data were stored in a database. By comparing the date of injection and start and end dates of the change in the blood test results, adverse events related to a designated drug were detected. Using this method, we searched for the occurrence of serious adverse events (CTCAE Grades 3 or 4) concerning WBC, ALT and creatinine related to paclitaxel at Osaka University Hospital. The rate of occurrence of a decreased WBC count, increased ALT level and increased creatinine level was 36.0%, 0.6% and 0.4%, respectively. This method is useful for detecting and estimating the rate of occurrence of hematotoxicity adverse drug events.