Post-marketing surveillance of anticancer drugs using natural language processing of electronic medical records.

This study demonstrates that adverse events (AEs) extracted using natural language processing (NLP) from clinical texts reflect the known frequencies of AEs associated with anticancer drugs. Using data from 44,502 cancer patients at a single hospital, we identified cases prescribed anticancer drugs (platinum, PLT; taxane, TAX; pyrimidine, PYA) and compared them to non-treatment (NTx) group using propensity score matching. Over 365 days, AEs (peripheral neuropathy, PN; oral mucositis, OM; taste abnormality, TA; appetite loss, AL) were extracted from clinical text using an NLP tool.

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.

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.

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).

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.