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Today, an increasing number of people work online. However, they are often separated from each other or often drop out of tasks due to workload, and sometimes do not perform the task well (e.g., making incorrect judgments). Therefore, there is a growing need for an intervention to ensure the job performance of distributed workers. In this study, we propose a simple and cost-effective intervention to improve judgment accuracy, namely, making people wait for one second at the beginning of a task. We examined whether inserting a short time (e.g., one second) during which people cannot respond to a task enhances an appropriate allocation of cognitive resources into thinking tasks. Through behavioral experiments using a perceptual binary-choice task, we found that participants who waited for 1s showed higher accuracy than those assigned to a control group (i.e., waited for 0s) and felt less mental workload than those who waited for 2.5s. Furthermore, analyses of mouse trajectories during choice behaviors revealed that a waiting time helped participants avoid impulsive or “straight” movements. These results suggest that waiting for one second is an effective intervention to boost people’s competence regarding judgments. We believe that our proposed intervention can be applied in various fields because of its simplicity.

Electronic health records should efficiently store the information required for clinical decision-making and contain progress notes that reference this information. However, beyond the inclusion of subjective data, objective data, assessment, and plan framework, the content required to make progress notes useful for readers with diverse specialties has not been clarified. Moreover, the documentation burden that including additional content places on medical doctors (MDs) has not been determined. We conducted a questionnaire with 74 MDs, nurses, and other clinical professionals to determine whether they found progress notes with varying specific contents useful. In addition, the degree of the burden of writing progress notes that contain specific content was measured when 25 MDs were instructed to add specific content. Our results reveal that progress notes are more useful for clinical reasoning for readers other than MDs when more specific information is included; this can be achieved without increasing the documentation burden.

We compared the distribution of laboratory test values with several parametric statistical distributions to show that a lognormal distribution can represent the distribution of laboratory test values. Then, we estimated the distributions of laboratory test values of four datasets including only three published values: two endpoints of reference interval (RI) and one median.

Electronic health records should efficiently store the information required for clinical decision-making and contain progress notes that reference this information. However, beyond the inclusion of subjective data, objective data, assessment, and plan framework, the content required to make progress notes useful for readers with diverse specialties has not been clarified. Moreover, the documentation burden that including additional content places on medical doctors (MDs) has not been determined. We conducted a questionnaire with 74 MDs, nurses, and other clinical professionals to determine whether they found progress notes with varying specific contents useful. In addition, the degree of the burden of writing progress notes that contain specific content was measured when 25 MDs were instructed to add specific content. Our results reveal that progress notes are more useful for clinical reasoning for readers other than MDs when more specific information is included; this can be achieved without increasing the documentation burden.

We compared the distribution of laboratory test values with several parametric statistical distributions to show that a lognormal distribution can represent the distribution of laboratory test values. Then, we estimated the distributions of laboratory test values of four datasets including only three published values: two endpoints of reference interval (RI) and one median.

Making rational judgments is not always easy for humans. Given that aggregation of the distributed labor force on the Internet has become common and necessary, a simple and cost-effective solution should be developed to improve the quality of workers’ judgments. We refer to resource rationality and assume that there is an optimal decision time for each worker according to the trade-off between improving their correct answer rate and cognitive load. In this study, we verify whether an enforced decision time boosts job performance by not allowing workers to provide answers within a certain short time after being presented with a task (i.e., forcing workers to think). As experimental materials, we evaluated the judgments of binary medical images in a situation in which large, accurately annotated medical datasets for machine learning were created. First, through a theoretical analysis, we confirmed the existence of an optimal decision time that improved workers’ performance by optimally allocating their cognitive resources. Second, in a total of four large-scale behavioral experiments with physicians (N = 628) and nurses (N = 651), job performances under various enforced decision times were compared, which were improved by enforcing 1.0 s of additional decision time. The additional 1.0 s was suggested to optimize the trade-off between the workers’ improved correct answer rate and cognitive load. Since our proposed boosting enforcing additional decision time can be implemented in a general-purpose and low-cost manner, it has the potential to be applied to a wide variety of situations.

The demand for the privacy-preserving survival analysis of medical data integrated from multiple institutions or countries has been increased. However, sharing the original medical data is difficult because of privacy concerns, and even if it could be achieved, we have to pay huge costs for cross-institutional or cross-border communications. To tackle these difficulties of privacy-preserving survival analysis on multiple parties, this study proposes a novel data collaboration Cox proportional hazards (DC-COX) model based on a data collaboration framework for horizontally and vertically partitioned data. By integrating dimensionality-reduced intermediate representations instead of the original data, DC-COX obtains a privacy-preserving survival analysis without iterative cross-institutional communications or huge computational costs. DC-COX enables each local party to obtain an approximation of the maximum likelihood model parameter, the corresponding statistic, such as the p-value, and survival curves for subgroups. Based on a bootstrap technique, we introduce a dimensionality reduction method to improve the efficiency of DC-COX. Numerical experiments demonstrate that DC-COX can compute a model parameter and the corresponding statistics with higher performance than the local party analysis. Particularly, DC-COX demonstrates outstanding performance in essential feature selection based on the p-value compared with the existing methods including the federated learning-based method.

When asked for numerical estimations, people can respond by stating their estimates (e.g., writing down a number) or indicating a number on a scale. Although these methods are logically the same, such differences may affect the responses to the numerical estimations. In this study, we examined how differences in response format affected responses to numerical estimations using two behavioral experiments. We found that participants showed a round number bias (i.e., people answered estimates with round numbers) when simply stating a number and the distribution of responses tended to be less diverse. In contrast, this tendency was not observed when the participants responded using a scale. Participants provided more diverse estimates when they answered using a scale. Furthermore, we analyzed how this difference in response distribution was related to the wisdom of crowds (the aggregated judgment is as accurate as, or sometimes better than, the best individual judgment in the group) using computer simulations. The results indicated that round number bias affected the achievement of the wisdom of crowds. Particularly, when the group size was small, biased responses resulted in less effective achievement. Our findings suggest that using an appropriate scale is a low-cost method for eliminating round number bias and efficiently achieving the wisdom of crowds.

Cooking Recipe Annotation Manual for deep and detailed categories

Objectives Electronic health record (EHR)-based phenotyping is an automated technique for identifying patients diagnosed with a particular disease using EHR data. However, EHR-based phenotyping has difficulties in achieving satisfactorily high performance because clinical notes include disease mentions that ultimately signify something other than the patient’s diagnosis (such as differential diagnosis or screening). Our objective is to quantify the influence of such disease mentions on EHR-based phenotyping performance. Methods Physicians manually reviewed whether the disease mentions indicated the patients’ diseases in 487,300 clinical notes of 4,430 patients. Particular focus was placed on disease mentions that did not signify the patient’s diagnosis even though they did not have any syntactic modifier or indicator in the same sentences. Patients were then classified according to whether their clinical notes included such disease mentions. Results Among the patients whose clinical notes included disease mentions without any modifier or indicator, the proportion of patients whose disease mentions signified the patients’ diagnosis was 78.1% (on average). This value can be interpreted as the bias of disease mentions that did not signify the patient’s diagnosis on the precision of EHR-based phenotyping by extracting disease mentions from clinical notes. Conclusion This study quantified the bias occurred owing to disease mentions that incorrectly signify a patient’s diagnosis in the value of precision of EHR-based phenotyping from four dataset types. The results of this study will help researchers in diverse research environments with different available data types.

In Japan, nursing records are not easily put to secondary use because nursing documentation is not standardized. In recent years, electronic health records have necessitated the creation of Japanese nursing terminology. The purpose of this study was to develop and evaluate an automatic classification system for narrative nursing records using natural language processing technology and machine learning. We collected a week's worth of narrative nursing records from an academic hospital. The authors independently annotated the text data, dividing it into morphemes, the smallest meaningful unit in a language. During preprocessing when creating feature quantities, we used a Japanese tokenizer, MeCab, an open-source morphological parser, and the bag-of-words model. A support vector machine was adopted as a classifier for machine learning. The accuracy was 0.96 and 0.86 on the training set and test set, respectively, and the F value was 0.82. Our findings provide useful information regarding the development of an automatic classification system for Japanese nursing records using nursing terminology and natural language processing techniques.

Machine learning, applied to medical data, can uncover new knowledge and support medical practices. However, analyzing medical data by machine learning methods presents a trade-off between accuracy and privacy. To overcome the trade-off, we apply the data collaboration analysis method to medical data. This method using artificial dummy data enables analysis to compare distributed information without using the original data. The purpose of our experiment is to identify patients diagnosed with diabetes mellitus (DM), using 29,802 instances of real data obtained from the University of Tsukuba Hospital between 01/03/2013 and 30/09/2018. The whole data is divided into a number of datasets to simulate different hospitals. We propose the following improvements for the data collaboration analysis. (1) Making the dummy data which has a reality and (2) using non-linear reconverting functions into the comparable space. Both can be realized using the generative adversarial network (GAN) and Node2Vec, respectively. The improvement effects of dummy data with GAN scores more than 10% over the effects of dummy data with random numbers. Furthermore, the improvement effect of the re-conversion by Node2Vec with GAN anchor data scores about 20% higher than the linear method with random dummy data. Our results reveal that the data collaboration method with appropriate modifications, depending on data type, improves analysis performance.

BACKGROUND Sharing progress notes as a common social capital is essential in research and education, but the content of progress notes is sensitive and needs to be kept confidential. Publishing actual progress notes are difficult due to privacy concerns. OBJECTIVE This study aims to generate a large repository of pseudo-progress notes of authentic quality. We focused on two requirements for authentic quality: the validity and consistency of the data, from the perspective of medical practice, and the empirical and semantic characteristics of progress notes, such as shorthand styles used for reporting changes in a patient's physical status, long narrative sentences detailing patient anxiety, and interprofessional communications. METHODS We proposed a practical framework that consists of a simulation of the notes and evaluation of the simulated notes. The framework utilized two human cognitive traits: (1) the ability to use imitation to simulate objects with diverse characteristics without background knowledge and (2) the use of comparison as a strategy for deep thinking. This enabled crowd workers to generate a large number of progress notes. Our framework involved three steps. In step 1, crowd workers imitated actual progress notes decomposed into subject data (S), object data (O), and assessment and plan (A/P). These imitated texts were then shuffled and recomposed in S, O, and A/P in order to create simulated progress notes. In step 2, crowd workers identified the characteristics of actual progress notes based on comparisons between actual and dummy progress notes. These characteristics were clustered based on their similarities. Each cluster exhibited the empirical and semantic characteristics of the actual progress notes. Finally, in step 3, the texts from step 1 that exhibited the identified characteristics from step 2 were evaluated as quality-guaranteed progress notes that met the two requirements. All data were preprocessed to protect patient privacy. RESULTS Step 1: By recomposing the 700 imitated texts, 9,856 simulated progress notes were generated. Step 2: 3,938 differences between actual progress notes and dummy progress notes were identified. After clustering, 166 characteristics were evaluated to be appropriate as empirical and semantic characteristics of the actual progress notes. Step 3: 500 crowd workers demonstrated that 83.0% of the simulated progress notes satisfied at least one of the characteristics obtained in step 2. The crowd workers' artificially-reproduced progress notes were evaluated to determine the most realistic, based on four metrics: disease, morpheme, readability, and reality. CONCLUSIONS Our results demonstrated that crowd workers could generate and evaluate highly professional documents. We have made our large repository of high-quality crowdsourced progress notes publicly available, and we encourage their use in the development of medical education and research.

Objective The goal of this work was to capture diseases in patients by comprehending the fine-grained medical conditions and disease progression manifested by transitions in medical conditions. We realize this by introducing our earlier work on a state-of-the-art knowledge presentation, which defines a disease as a causal chain of abnormal states (CCAS). Here, we propose a framework, EHR2CCAS, for constructing a system to map electronic health record (EHR) data to CCAS. Materials and methods EHR2CCAS is a framework consisting of modules that access heterogeneous EHR to estimate the presence of abnormal states in a CCAS for a patient in a given time window. EHR2CCAS applies expert-driven (rule-based) and data-driven (machine learning) methods to identify abnormal states from structured and unstructured EHR data. It features data-driven approaches for unlocking clinical texts and imputations based on the EHR temporal properties and the causal CCAS structure. This study presents the CCAS of chronic kidney disease as an example. A mapping system between the EHR from the University of Tokyo Hospital and CCAS of chronic kidney disease was constructed and evaluated against expert annotation. Results The system achieved high prediction performance in identifying abnormal states that had strong agreement among annotators. Our handling of narrative varieties in texts and our imputation of the presence of an abnormal state markedly improved the prediction performance. EHR2CCAS presents patient data describing the temporal presence of abnormal states in CCAS, which is useful in individual disease progression management. Further analysis of the differentiation of transition among abnormal states outputted by EHR2CCAS can contribute to detecting disease subtypes. Conclusion This work represents the first step toward combining disease knowledge and EHR to extract abnormality related to a disease defined as fine-grained abnormal states and transitions among them. This can aid in disease progression management and deep phenotyping.

Background: Phenotyping is an automated technique that can be used to distinguish patients based on electronic health records. To improve the quality of medical care and advance type 2 diabetes mellitus (T2DM) research, the demand for T2DM phenotyping has been increasing. Some existing phenotyping algorithms are not sufficiently accurate for screening or identifying clinical research subjects. Objective: We propose a practical phenotyping framework using both expert knowledge and a machine learning approach to develop 2 phenotyping algorithms: one is for screening; the other is for identifying research subjects. Methods: We employ expert knowledge as rules to exclude obvious control patients and machine learning to increase accuracy for complicated patients. We developed phenotyping algorithms on the basis of our framework and performed binary classification to determine whether a patient has T2DM. To facilitate development of practical phenotyping algorithms, this study introduces new evaluation metrics: area under the precision-sensitivity curve (AUPS) with a high sensitivity and AUPS with a high positive predictive value. Results: The proposed phenotyping algorithms based on our framework show higher performance than baseline algorithms. Our proposed framework can be used to develop 2 types of phenotyping algorithms depending on the tuning approach: one for screening, the other for identifying research subjects. Conclusions: We develop a novel phenotyping framework that can be easily implemented on the basis of proper evaluation metrics, which are in accordance with users’ objectives. The phenotyping algorithms based on our framework are useful for extraction of T2DM patients in retrospective studies.

Phenotyping is an automated technique for identifying patients diagnosed with a particular disease based on electronic health records (EHRs). To evaluate phenotyping algorithms, which should be reproducible, the annotation of EHRs as a gold standard is critical. However, we have found that the different types of EHRs cannot be definitively annotated into CASEs or CONTROLs. The influence of such "possible patients" on phenotyping algorithms is unknown. To assess these issues, for four chronic diseases, we annotated EHRs by using information not directly referring to the diseases and developed two types of phenotyping algorithms for each disease. We confirmed that each disease included different types of possible patients. The performance of phenotyping algorithms differed depending on whether possible patients were considered as CASEs, and this was independent of the type of algorithms. Our results indicate that researchers must share annotation criteria for classifying the possible patients to reproduce phenotyping algorithms.

Cell membranes, which consist of a lipid bilayer, play a critical role in cellular bioactivities. The lipid bilayer interacts dynamically with water as well as ions. Membrane fluidity is lesser in ionic solutions than in pure water, although how ions reduce membrane fluidity has not yet been clarified. Here, we performed 4×100-ns molecular dynamics simulations of a POPE (1-palmitoyl-2-oleoyl-phosphatidyl-ethanolamine [18:1]) lipid bilayer in the presence or absence of ions (NaCl, KCl, or CaCl 2 ) and compared the systems to evaluate the influence of ions on the structural dynamics of membranes. Our simulation revealed that membrane fluidity (no ion-system>Ca-system>K-system>Na-system) did not correspond to the distribution or the direct binding of cations to lipids (Ca 2+ >Na + >K + ). Interestingly, we found that the presence of cations induces dehydration of lipids. The number of cations in the interface region corresponded to the degree of lipid dehydration and the reduction in membrane fluidity (Na + >K + >Ca 2+ ). We therefore propose that membrane fluidity is not affected by direct interactions between cations and lipids, but by the dehydration of lipid molecules in the presence of cations located adjacent to the membrane.

The cell membrane that consists of lipid bilayer plays a critical role in physiology. Lipid molecules are directly contacted with water molecules and ions (i.e. Na+, K+, Ca2+, and Cl-). Fluidity of the lipid bilayer decrease in aqueous salt solutions compared with pure water, which is affected by bonding of ions to lipids. However, its mechanism has not been clarified yet, and decrease in membrane fluidity may be influenced by other factors.Lipid, water and ions interact with each other, which contribute to the membrane dynamics. Water molecules construct “hydrogen bond network”. Some ions change static and dynamic properties of this network. In addition, water molecules directly affect dynamic properties of lipid bilayers. Therefore, we hypothesized that the hydrogen bond network of water, which is modified by ions, may play a role in dynamic behavior of lipid bilayer.To analyze the effect of ions and water molecules on membrane dynamics, we constructed four model systems, POPE (1-palmitoyl-2-oleoyl-phosphatidyl-ethanolamine (18:1)) lipid bilayer and water molecules without (WATER) or with ions (KCl, CaCl2 and NaCl). We performed 100ns MD simulations and compared each system. Each system consists of 282 lipids and about 22000 water molecules, and the salt concentration is about 0.09 M, which is similar with biophysical conditions. MD simulations were carried out using Amber 8 program on HITACHI HA8000 cluster system at the University of Tokyo.We found that the self-diffusion-coefficient of water molecules in each system away from lipid bilayer were essentially the same as those in experimental data (KCl>WATER>NaCl>CaCl2). However, self-diffusion-coefficient of water molecules adjacent the bilayer are different from those in bulk solutions (NaCl>KCl>CaCl2>WATER). Our study suggests that the increase of self-diffusion-coefficient of water is related to the decrease in membrane fluidity.