Senility deaths in aged societies: The case of Japan.

Senility is now the third largest cause of death in Japan, comprising 11.4% of the total number of deaths in 2022. Although senility deaths were common in the period before the Second World War, they declined sharply from 1950 to 2000 and then increased up to the present. The recent increase is more than what we could expect from an increasing number of very old persons or the increasing number of deaths at facilities. The senility death description in the death certificate is becoming poorer, with 93.8% of them only with a single entry of "senility". If other diseases are mentioned, those are again vague diseases or conditions. Senility, dementia and Alzheimer's disease, sequelae of cerebrovascular disease, and heart failure are the largest causes of death in which senility is mentioned in the death certificate. The period from senility onset to death is often described within a few months, but it varies. In some cases, the deceased's age was written out of a conviction that the ageing process starts from birth. As senility is perceived differently among the certifying doctors, a standardised protocol to certify the senility death is needed. On the other hand, senility death is the preferred cause of death and many people do not wish to receive invasive medical examinations before dying peacefully. Together with other causes of death related to frailty, there would be a need to capture senility as a proper cause of death, not just as a garbage code, in the aged, low-mortality population.

Towards Structuring Clinical Texts: Joint Entity and Relation Extraction from Japanese Case Report Corpus.

Important pieces of information related to patient symptoms and diagnosis are often written in free-text form in clinical texts. To utilize these texts, information extraction using natural language processing is required. This study evaluated the performance of named entity recognition (NER) and relation extraction (RE) using machine-learning methods. The Japanese case report corpus was used for this study, which had 113 types of entities and 36 types of relations that were manually annotated. There were 183 cases comprising 2,194 sentences after preprocessing. In addition, a machine learning model based on bidirectional encoder representations from transformers was used. The results revealed that the maximum micro-averaged F1 scores of NER and RE were 0.912 and 0.759, respectively. The results of this study are comparable to those of previous studies. Hence, these results could be of substantial baseline accuracy.

Influence of Tweets Indicating False Rumors on COVID-19 Vaccination: Case Study.

BACKGROUND: As of December 2022, the outbreak of COVID-19 showed no sign of abating, continuing to impact people's lives, livelihoods, economies, and more. Vaccination is an effective way to achieve mass immunity. However, in places such as Japan, where vaccination is voluntary, there are people who choose not to receive the vaccine, even if an effective vaccine is offered. To promote vaccination, it is necessary to clarify what kind of information on social media can influence attitudes toward vaccines. OBJECTIVE: False rumors and counterrumors are often posted and spread in large numbers on social media, especially during emergencies. In this paper, we regard tweets that contain questions or point out errors in information as counterrumors. We analyze counterrumors tweets related to the COVID-19 vaccine on Twitter. We aimed to answer the following questions: (1) what kinds of COVID-19 vaccine-related counterrumors were posted on Twitter, and (2) are the posted counterrumors related to social conditions such as vaccination status? METHODS: We use the following data sets: (1) counterrumors automatically collected by the "rumor cloud" (18,593 tweets); and (2) the number of COVID-19 vaccine inoculators from September 27, 2021, to August 15, 2022, published on the Prime Minister's Office's website. First, we classified the contents contained in counterrumors. Second, we counted the number of COVID-19 vaccine-related counterrumors from data set 1. Then, we examined the cross-correlation coefficients between the numbers of data sets 1 and 2. Through this verification, we examined the correlation coefficients for the following three periods: (1) the same period of data; (2) the case where the occurrence of the suggestion of counterrumors precedes the vaccination (negative time lag); and (3) the case where the vaccination precedes the occurrence of counterrumors (positive time lag). The data period used for the validation was from October 4, 2021, to April 18, 2022. RESULTS: Our classification results showed that most counterrumors about the COVID-19 vaccine were negative. Moreover, the correlation coefficients between the number of counterrumors and vaccine inoculators showed significant and strong positive correlations. The correlation coefficient was over 0.7 at -8, -7, and -1 weeks of lag. Results suggest that the number of vaccine inoculators tended to increase with an increase in the number of counterrumors. Significant correlation coefficients of 0.5 to 0.6 were observed for lags of 1 week or more and 2 weeks or more. This implies that an increase in vaccine inoculators increases the number of counterrumors. These results suggest that the increase in the number of counterrumors may have been a factor in inducing vaccination behavior. CONCLUSIONS: Using quantitative data, we were able to reveal how counterrumors influence the vaccination status of the COVID-19 vaccine. We think that our findings would be a foundation for considering countermeasures of vaccination.

Computational Pipeline for Glomerular Segmentation and Association of the Quantified Regions with Prognosis of Kidney Function in IgA Nephropathy.

The histopathological findings of the glomeruli from whole slide images (WSIs) of a renal biopsy play an important role in diagnosing and grading kidney disease. This study aimed to develop an automated computational pipeline to detect glomeruli and to segment the histopathological regions inside of the glomerulus in a WSI. In order to assess the significance of this pipeline, we conducted a multivariate regression analysis to determine whether the quantified regions were associated with the prognosis of kidney function in 46 cases of immunoglobulin A nephropathy (IgAN). The developed pipelines showed a mean intersection over union (IoU) of 0.670 and 0.693 for five classes (i.e., background, Bowman's space, glomerular tuft, crescentic, and sclerotic regions) against the WSI of its facility, and 0.678 and 0.609 against the WSI of the external facility. The multivariate analysis revealed that the predicted sclerotic regions, even those that were predicted by the external model, had a significant negative impact on the slope of the estimated glomerular filtration rate after biopsy. This is the first study to demonstrate that the quantified sclerotic regions that are predicted by an automated computational pipeline for the segmentation of the histopathological glomerular components on WSIs impact the prognosis of kidney function in patients with IgAN.

Development of comprehensive annotation criteria for patients' states from clinical texts.

In clinical records, much of the clinical information is recorded as free text, thus necessitating the use of advanced automatic information extraction technology. The development of practical technologies requires a corpus with finer granularity annotations that describe the information in the corpus, but such annotation criteria have not been researched enough thus far. This study aimed to develop fine grained annotation criteria that exhaustively cover patients' states in case reports. We collected 362 case reports-written in Japanese-of intractable diseases that were expected to contain a broad range of patients' states. Criteria were developed by repeatedly revising and annotating the clinical case report text. A set of annotation criteria for patients' states, consisting of 46 entity types, 9 attributes, and 36 relations, was obtained it allows more detailed information to be expressed than in previous studies by broader range of concept types including treatment, and captures clinical information based on a combination of causality and judgment, which could not be expressed before.

Impact of a Clinical Text-Based Fall Prediction Model on Preventing Extended Hospital Stays for Elderly Inpatients: Model Development and Performance Evaluation.

BACKGROUND: Falls may cause elderly people to be bedridden, requiring professional intervention; thus, fall prevention is crucial. The use of electronic health records (EHRs) is expected to provide highly accurate risk assessment and length-of-stay data related to falls, which may be used to estimate the costs and benefits of prevention. However, no studies to date have investigated the extent to which hospital stays could be shortened through fall avoidance resulting from the use of prediction tools. OBJECTIVE: We first estimated the extended length of hospital stay caused by falls among elderly inpatients. Next, we developed a model that predicts falls using clinical text as input and evaluated its accuracy. Finally, we estimated the potentially shortened hospital stay that would be made possible by appropriate interventions based on the prediction model. METHODS: Patients aged 65 years or older were selected as subjects, and the EHRs of 1728 falls and 70,586 nonfalls were subjected to analysis. The extended-stay lengths were estimated using propensity score matching of 49 associated variables. Bidirectional encoder representations from transformers and bidirectional long short-term memory methods were used to predict falls from clinical text. The estimated length of stay and the outputs of the prediction model were used to determine stay reductions. RESULTS: The extended length of hospital stay due to falls was estimated to be 17.8 days (95% CI 16.6-19.0), which dropped to 8.6 days when there were unobserved covariates at an odds ratio of 2.0. The accuracy of the prediction model was as follows: area under the receiver operating characteristic curve, 0.851; F-value, 0.165; recall, 0.737; precision, 0.093; and specificity, 0.839. When assuming interventions with 25% or 100% effectiveness against cases where the model predicted a fall, the stay reduction was estimated at 0.022 and 0.099 days/day, respectively. CONCLUSIONS: The accuracy of the prediction model using clinical text is considered to be higher than the prediction accuracy of conventional assessments. However, our model's precision remained low at 9.3%. This may be due, in part, to the inclusion of cases in which falls did not occur because of preventative interventions during hospitalization. Nonetheless, it is estimated that interventions for cases when falls were predicted will reduce medical costs by 886 Yen/day (~US $6.50/day) of intervention, even if the preventative effect is 25%. Limitations include the fact that these results cannot be extrapolated to short- or long-term hospitalization cases, and that this was a single-center study.

A clinical specific BERT developed using a huge Japanese clinical text corpus.

Generalized language models that are pre-trained with a large corpus have achieved great performance on natural language tasks. While many pre-trained transformers for English are published, few models are available for Japanese text, especially in clinical medicine. In this work, we demonstrate the development of a clinical specific BERT model with a huge amount of Japanese clinical text and evaluate it on the NTCIR-13 MedWeb that has fake Twitter messages regarding medical concerns with eight labels. Approximately 120 million clinical texts stored at the University of Tokyo Hospital were used as our dataset. The BERT-base was pre-trained using the entire dataset and a vocabulary including 25,000 tokens. The pre-training was almost saturated at about 4 epochs, and the accuracies of Masked-LM and Next Sentence Prediction were 0.773 and 0.975, respectively. The developed BERT did not show significantly higher performance on the MedWeb task than the other BERT models that were pre-trained with Japanese Wikipedia text. The advantage of pre-training on clinical text may become apparent in more complex tasks on actual clinical text, and such an evaluation set needs to be developed.

Automatic Classification of Electronic Nursing Narrative Records Based on Japanese Standard Terminology for Nursing.

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.

Glomerular Classification Using Convolutional Neural Networks Based on Defined Annotation Criteria and Concordance Evaluation Among Clinicians.

INTRODUCTION: Diagnosing renal pathologies is important for performing treatments. However, classifying every glomerulus is difficult for clinicians; thus, a support system, such as a computer, is required. This paper describes the automatic classification of glomerular images using a convolutional neural network (CNN). METHOD: To generate appropriate labeled data, annotation criteria including 12 features (e.g., "fibrous crescent") were defined. The concordance among 5 clinicians was evaluated for 100 images using the kappa (κ) coefficient for each feature. Using the annotation criteria, 1 clinician annotated 10,102 images. We trained the CNNs to classify the features with an average κ ≥0.4 and evaluated their performance using the receiver operating characteristic-area under the curve (ROC-AUC). An error analysis was conducted and the gradient-weighted class activation mapping (Grad-CAM) was also applied; it expresses the CNN's focusing point with a heat map when the CNN classifies the glomerular image for a feature. RESULTS: The average κ coefficient of the features ranged from 0.28 to 0.50. The ROC-AUC of the CNNs for test data varied from 0.65 to 0.98. Among the features, "capillary collapse" and "fibrous crescent" had high ROC-AUC values of 0.98 and 0.91, respectively. The error analysis and the Grad-CAM visually showed that the CNN could not distinguish between 2 different features that had similar visual structures or that occurred simultaneously. CONCLUSION: The differences in the texture or frequency of the co-occurrence between the different features affected the CNN performance; thus, to improve the classification accuracy, methods such as segmentation are required.

EHR2CCAS: A framework for mapping EHR to disease knowledge presenting causal chain of disorders - chronic kidney disease example.

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.

Bias of Inaccurate Disease Mentions in Electronic Health Record-based Phenotyping.

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.

Can Staff Distinguish Falls: Experimental Hypothesis Verification Using Japanese Incident Reports and Natural Language Processing.

Falls are generally classified into two groups in clinical settings in Japan: falls from the same level and falls from one level to another. We verified whether clinical staff could distinguish between these two types of falls by comparing 3,078 free-text incident reports about falls using a natural language processing technique and a machine learning technique. Common terms were used in reports for both types of falls, but the similarity score between the two types of reports was low, and the performance of identification based on the classification model constructed by support vector machine and deep learning was low. Although it is possible that adjustment of hyper parameters during construction of the classification model was required, we believe that clinical staff cannot distinguish between the two types of falls and do not record the distinction in incident reports.

Development of Type 2 Diabetes Mellitus Phenotyping Framework Using Expert Knowledge and Machine Learning Approach.

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.

The Impact of "Possible Patients" on Phenotyping Algorithms: Electronic Phenotype Algorithms Can Only Be Reproduced by Sharing Detailed Annotation Criteria.

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.

A Semi-Automatic Framework to Identify Abnormal States in EHR Narratives.

Disease ontology, defined as a causal chain of abnormal states, is believed to be a valuable knowledge base in medical information systems. Automatic mapping between electronic health records (EHR) and disease ontology is indispensable for applying disease ontology in real clinical settings. Based on an analysis of ontologies of 148 chronic diseases, approximately 41% of abnormal states require information extraction from clinical narratives. This paper presents a semi-automatic framework to identify abnormal states in clinical narratives. This framework aims to effectively build mapping modules between EHR and disease ontology. We show that the proposed method is effective in data mapping for 18%-33% of the abnormal states in the ontologies of chronic diseases. Moreover, we analyze the abnormal states for which our method is invalid in extracting information from clinical narratives.

A qualitative model for physiology: apart from function and abnormality.

Physiological knowledge is often described in terms of mathematical models in the domain of bioinformatics, and some ontologies have been developed to integrate these models. However, such models do not explicitly describe clinicians' qualitative knowledge, which is required for clinical applications including decision support and counseling of patients to help them understand their clinical situation. This paper proposes a description framework for a qualitative and context-independent ontology of physiology, QliP, which has three features: 1) It models physiological knowledge qualitatively without mathematical knowledge; 2) The described knowledge is independent of surrounding anatomical entities and abnormality; and 3) It targets physiological components in varying degrees of granularity, from cells to organ systems. An ontology based on this proposed model enables automatic generation of a physiological state transition, starting and ending with a given state.

An analysis of the openehr archetype semantics based on a typed lambda theory.

The openEHR has adopted the dual model architecture consisting of Reference Model and Archetype. The specification, however, lacks formal definitions of archetype semantics, so that its behaviors have remained ambiguous. The objective of this poster is to analyze semantics of the openEHR archetypes: its variance and mutability. We use a typed lambda calculus as an analyzing tool. As a result, we have reached the conclusion that archetypes should be 1) covariant and 2) immutable schema.