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Background: Identification of the factors associated with a rapid decline in forced expiratory volume in 1 second (FEV1) at an early stage of chronic obstructive pulmonary disease (COPD) is of particular clinical importance because it may allow timely medical intervention to slow down progression of lung function decline in patients. This study aimed to compare the time profile of FEV1 after COPD diagnosis among rapid decliners, slow decliners, and sustainers and the patient characteristics in the year of COPD diagnosis.Methods: COPD patients were identified from data collected from the annual medical checkup records of Hitachi, Ltd. employees in Japan from April 1998 to March 2019. Patients were categorized by FEV1 for 5 years (years 1-5) in 3 groups (rapid decliner, slow decliner, and sustainer). Patients with a decrease of FEV1 ≥63 mL/year were considered rapid decliners; those with 1 across the onset of COPD (from year ‑3 to 5) were investigated in those groups.Results: Of 1294 eligible participants, 241 (18.6%) were classified as rapid decliners. The annual rates of FEV1 decline were similar 3 years before and until COPD diagnosis (year -3 to 0) and were 0.05 mL in rapid decliners, 0.05 mL in slow decliners, and 0.07 mL in sustainers. After COPD diagnosis, these 3 categories started diverging in their lung function trajectory. The mean FEV1 in rapid decliners was 2.82 L in year 0 and 2.41 L in year 5. Rapid decliners had decreased body mass index, waist circumference, and body fat percentage at the time of COPD diagnosis compared with sustainers.Conclusions: FEV1 declined yearly before diagnosis in rapid decliners, slow decliners, and sustainers. The time profiles of FEV1 were different in the 3 groups after COPD diagnosis. Therefore, regular lung function tests are necessary to follow FEV1 decline after COPD onset in a timely manner.

Background and Objectives: The established treatment target for most patients with type 2 diabetes mellitus (T2DM) is controlling HbA1c levels to less than 7%; however, about half of patients fail to achieve this target. In order to support optimal prescription selection, we have been developing a prescription selection support system that predicts expected outcomes by treatment options. In this presentation, we report a novel analytical method named Treatment Pathway Based Estimation (TPGE) , which predicts the probability of achieving a treatment goal, such as HbA1c less than 7% in 3 months, using a treatment pathway graph built from the data. Methods: The TPGE model was built and evaluated using EHR data from 27,9 patients with T2DM. TPGE was compared with a conventional machine learning approach, gradient boosting decision tree (GBT) , across two dimensions. First, the prediction error of the probability of achieving treatment was evaluated by the Brier Score. Second, the characteristics of the prescriptions recommended with the highest probability were investigated, referring to the prescriptions actually prescribed by clinicians. Results: TPGE achieved a prediction error of 0.158, which is lower than the prediction error of 0.162 of GBT. In addition, the calibration error was 0.002 (84.6% reduction) , much lower than that of GBT (0.013) . A prescription to add two or more drugs was selected by clinicians for 0.7% of cases. GBT recommended that prescription for 42.2 % of cases, while TPGE recommended for 1.4%, which was closer to the clinicians’ decision. In summary, the superiority of TPGE over GBT was confirmed. Conclusion: We have developed a pharmacotherapy decision support technology for T2DM to help clinicians and patients select the optimal prescription. Disclosure S.Tarumi: None. W.Takeuchi: None. G.Chalkidis: Employee; Hitachi, Ltd. H.Ban: None. K.Kawamoto: Consultant; Korean Society of Medical Informatics, NORC at the University of Chicago, Pfizer Inc., RTI International, Security Risk Solutions, Other Relationship; MD Aware, Research Support; Hitachi, Ltd., Pfizer Inc.

Objectives Artificial intelligence (AI), including predictive analytics, has great potential to improve the care of common chronic conditions with high morbidity and mortality. However, there are still many challenges to achieving this vision. The goal of this project was to develop and apply methods for enhancing chronic disease care using AI.Methods Using a dataset of 27,904 patients with diabetes, an analytical method was developed and validated for generating a treatment pathway graph which consists of models that predict the likelihood of alternate treatment strategies achieving care goals. An AI-driven clinical decision support system (CDSS) integrated with the electronic health record (EHR) was developed by encapsulating the prediction models in an OpenCDS Web service module and delivering the model outputs through a SMART on FHIR (Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources) web-based dashboard. This CDSS enables clinicians and patients to review relevant patient parameters, select treatment goals, and review alternate treatment strategies based on prediction results.Results The proposed analytical method outperformed previous machine-learning algorithms on prediction accuracy. The CDSS was successfully integrated with the Epic EHR at the University of Utah.Conclusion A predictive analytics-based CDSS was developed and successfully integrated with the EHR through standards-based interoperability frameworks. The approach used could potentially be applied to many other chronic conditions to bring AI-driven CDSS to the point of care.

BACKGROUND Airflow limitation is a critical physiological feature in chronic obstructive pulmonary disease (COPD), for which long-term exposure to noxious substances, including tobacco smoke, is an established risk. However, not all long-term smokers develop COPD, meaning that other risk factors exist. OBJECTIVE This study aimed to predict the risk factors for COPD diagnosis using machine learning in an annual medical check-up database. METHODS In this retrospective observational cohort study (ARTDECO [Analysis of Risk Factors to Detect COPD]), annual medical check-up records for all Hitachi Ltd employees in Japan collected from April 1998 to March 2019 were analyzed. Employees who provided informed consent via an opt-out model were screened and those aged 30 to 75 years without a prior diagnosis of COPD/asthma or a history of cancer were included. The database included clinical measurements (eg, pulmonary function tests) and questionnaire responses. To predict the risk factors for COPD diagnosis within a 3-year period, the Gradient Boosting Decision Tree machine learning (XGBoost) method was applied as a primary approach, with logistic regression as a secondary method. A diagnosis of COPD was made when the ratio of the prebronchodilator forced expiratory volume in 1 second (FEV1) to prebronchodilator forced vital capacity (FVC) was RESULTS Of the 26,101 individuals screened, 1213 met the exclusion criteria, and thus, 24,815 individuals were included in the analysis. The top 10 predictors for COPD diagnosis were FEV1/FVC, smoking status, allergic symptoms, cough, pack years, hemoglobin A1c, serum albumin, mean corpuscular volume, percent predicted vital capacity, and percent predicted value of FEV1. The areas under the receiver operating characteristic curves of the XGBoost model and the logistic regression model were 0.956 and 0.943, respectively. CONCLUSIONS Using a machine learning model in this longitudinal database, we identified a number of parameters as risk factors other than smoking exposure or lung function to support general practitioners and occupational health physicians to predict the development of COPD. Further research to confirm our results is warranted, as our analysis involved a database used only in Japan.

Continuity is required for diet therapy, but it depends on patients. We examined the utility of a new tool, the customized online nutrition guidance system, in patients with nonalcoholic fatty liver disease (NAFLD). Seven patients plotted their body weight (BW) and marked a customized task card on completion for 90 days on a website. The instructors encouraged them by e-mail. BW, serum transaminase levels, and system usage were evaluated. The results showed that BW and serum alanine aminotransferase levels were significantly lower than at baseline. BW and task visualization as well as encouragement by e-mails were effective in ensuring continuity. Thus, this system is effective in keeping NAFLD patients motivated to continue their diet therapy.

Bayesian network is a powerful tool to represent patterns inside past data. It can be used to predict future by calculating the posterior probability of future events. Machine learning techniques that can construct a Bayesian network from past data automatically are well developed in recent years. If we consider past data as a sampling set from an original probabilistic distribution, the "learning" process is actually trying to reproduce the original probabilistic distribution from the sampling set. Therefore, the finiteness of size of sampling set will bring uncertainties to the reproduced parameters of constructed Bayesian network. When the constructed Bayesian network is used to predict future, the uncertainties of reproduced parameters will be transferred to the uncertainty of query response. Here, the query response is the posterior probability that we are interested in. Evaluating the uncertainty of query response is critical to some strict industrial applications. Previous researches have proposed a method to evaluate the uncertainty. The consequence is shown as a variance of the query response. However, the conventional method need to work together with the bucket elimination, an exact inference method. Therefore, the conventional method can not deal with large Bayesian networks that used in real applications because of its calculation cost. We proposed a new approach to calculate the uncertainty of query responses by using joint probability distribution in this research. The proposed method can work with any inference method. Therefore, it can give an approximate evaluation even when the Bayesian network is large by using an approximate inference method. To investigate the accuracy of our proposed method, six well used public Bayesian networks are used as test cases. By comparing the approximate results with the exact results, an average error of -13.60% is got.

This paper proposes a method of rotating a three-dimensional image on a screen by using a two-dimensional pointer (e.g., a mouse cursor) to make medical diagnoses easier. The apparatus rotates the three-dimensional image by directly clicking and dragging areas of interest with a pointer so that a user (e.g., a busy doctor) can examine the image from any number of angles without a break in his/her concentration. Experiments show that the proposed method is faster by 20 percent and simpler and easier to use without special training as compared with a conventional method (in which a mouse cursor points in a numerical viewing angle direction).

We propose a high-speed, three-dimensional display method for objects represented by voxels intended for three-dimensional image diagnostic systems that can be operated interactively. The method extends the concept of the conventional range image and introduces the multilayer range image that stores the distance from the projection plane to the object regardless of whether the object is visible or not. A range image is generated on the surface of any projection plane from the multilayer range images of 6 projection planes perpendicular to the coordinate axes and displayed in three dimensions after shading. Fast speeds are achieved by selecting the multilayer range images that correspond to the projection direction for processing and by eliminating operations on voxels which are not needed in the processing. We were able to display a 2563—voxel object on a 22 MIPS workstation in about 0.5 seconds. Moreover, we anticipate a response of less than one second on the next generation of 100 MIPS workstations.

Objective Metabolic syndrome is associated with a high risk of cardiovascular morbidity and mortality. The predominant cause of metabolic syndrome is an unhealthy lifestyle. Healthy habits are represented by Breslow's 7 healthy practices, Morimoto's 8 items and Ikeda's 6 healthy habits. This study was done to determine which set of healthy habits was most likely to result in a reduced risk of developing the metabolic syndrome. Methods From April 1, 2000 through March 31, 2007, 6,765 males and 2,789 females underwent a medical check-up at Jikei University Hospital in Japan. They completed a simple, self-administered lifestyle questionnaire based on the 3 classifications of healthy habits. The responses were divided into 3 groups (poor, moderate and favorable) according to each of the healthy habit criteria. The incidence of metabolic syndrome was defined in participants who were newly diagnosed during the follow-up using Japanese-specific diagnostic criteria. The Kaplan-Meier cumulative 7-year incidence was calculated. Kaplan-Meier curves were compared using the long-rank test adjusted for age. Results In females, Breslow's, Morimoto's and Ikeda's healthy habits showed significant differences in the incidence between poor and moderate groups, and between poor and favorable groups. In males, a significant difference was observed among the poor, moderate and favorable groups for Ikeda's healthy habits. However, no significant difference was observed for Breslow's healthy practices. Morimoto's items only showed a significant difference between the poor and moderate groups. Conclusion Among the 3 models tested, Ikeda's healthy habits were the most useful for decreasing the risk of metabolic syndrome in Japanese.

We propose a new system to manage modalities in radiology departments. Our system introduces a novel method to accurately estimate some indexes in order to provide an analysis of resource plans within three broad categories: financial affairs, patient satisfaction, and productivity. The main idea is to simulate the operational status of modalities. Results from a preliminary hospital evaluation show that use of the system results in more efficient resource management.