bluuebunny/crossref_metadata_2025 huggingface dataset:
Using Natural Language Processing to Predict Risk in Electronic Health Records
Authors: Van Le Duy , Montgomery James , Kirkby Kenneth , Scanlan Joel
Published: January 2024

Clinical narratives recording behaviours and emotions of patients are available from EHRs in a forensic psychiatric centre located in Tasmania. This rich data has not been used in risk prediction. Prior work demonstrates natural language processing can be used to identify patient symptoms in these free-text records and can then be used to predict risk. Four dictionaries containing descriptive words of harm were created using the Diagnostic and Statistical Manual of Mental Disorders, the Unified Medical Language System repository, English negative and positive sentiment words, and high-frequency words from the Corpus of Contemporary American English. However, a model based only on these keywords is limited in predictive power. In this study, we introduce an improved NLP approach with a social interaction component to extract additional information about the behavioural and emotional state of patients. These social interactions are subsequently used in a machine-learning model to enhance risk prediction performance.
Extracting Drug-Protein Relation from Literature Using Ensembles of Biomedical Transformers
Authors: Das Avisha , Li Zhao , Wei Qiang , Li Jianfu , Huang Liang-chin , Hu Yan , Li Rongbin , Zheng Wenjin Jim , Xu Hua
Published: January 2024

Automatic extraction of relations between drugs/chemicals and proteins from ever-growing biomedical literature is required to build up-to-date knowledge bases in biomedicine. To promote the development of automated methods, BioCreative-VII organized a shared task – the DrugProt track, to recognize drug-protein entity relations from PubMed abstracts. We participated in the shared task and leveraged deep learning-based transformer models pre-trained on biomedical data to build ensemble approaches to automatically extract drug-protein relation from biomedical literature. On the main corpora of 10,750 abstracts, our best system obtained an F1-score of 77.60% (ranked 4th among 30 participating teams), and on the large-scale corpus of 2.4M documents, our system achieved micro-averaged F1-score of 77.32% (ranked 2nd among 9 system submissions). This demonstrates the effectiveness of domain-specific transformer models and ensemble approaches for automatic relation extraction from biomedical literature.
Graph Neural Network Based Multi-Label Hierarchical Classification for Disease Predictions in General Practice
Authors: Chi Shengqiang , Wang Yuqing , Zhang Ying , Zhu Weiwei , Li Jingsong
Published: January 2024

General practitioners are supposed to be better diagnostics to detect patients with serious diseases earlier, and conduct early interventions and appropriate referrals of patients. However, in the current general practice, primary general practitioners lack sufficient clinical experiences, and the correct rate of general disease diagnosis is low. To assist general practitioners in diagnosis, this paper proposes a multi-label hierarchical classification method based on graph neural network, which integrates medical knowledge and electronic health record (EHR) data to build a disease prediction model. The experimental results based on data consist of 231,783 visits from EHR show that the proposed model outperforms all baseline models in the general disease prediction task with a top-3 recall of 0.865. The interpretable results of the model can effectively help clinicians understand the basis of the model’s decision-making.
Pericardial Effusion Detection on Post-Mortem Computed Tomography Images Using Convolutional Neural Networks
Authors: Kong Haoyu , Rong Jia , Bain Chris , Zhang Xinyu , Parsons Sarah , Chen Guanliang , Bassed Richard
Published: January 2024

Pericardial effusion can be a sign of significant underlying diease and, in some cases, may lead to death. Post-mortem computed tomography (PMCT) is a well-established tool to assist death investigation processes in the forensic setting. In practice, the scarcity of well-trained radiologists is a challenge in processing raw whole-body PMCT images for pericardial effusion detection. In this work, we propose a Pericardial Effusion Automatic Detection (PEAD) framework to automatically process raw whole-body PMCT images to filter out the irrelevant images with heart organ absent and focus on pericardial effusion detection. In PEAD, the standard convolutional neural network architectures of VGG and ResNet are carefully modified to fit the specific characteristics of PMCT images. The experimental results prove the effectiveness of the proposed framework and modified models. The modified VGG and ResNet models achieved superior detection accuracy than the standard architecture with reduced processing speed.
A Multi-Resolution Denoising Method for Low-Dose CT Based on the Reconstruction of Wavelet High-Frequency Channel
Authors: Hu Jinnan , Hu Peijun , Gao Yiwei , Zhao Yanxia , Li Jingsong
Published: January 2024

Computed tomography (CT) is widely applied in contemporary clinic. Due to the radiation risks carried by X-rays, the imaging and post-processing methods of low-dose CT (LDCT) become popular topics in academia and industrial community. Generally, LDCT presents strong noise and artifacts, while existing algorithms cannot completely overcome the blurring effects and meantime reduce the noise. The proposed method enables CT extend to independent frequency channels by wavelet transformation, then two separate networks are established for low-frequency denoising and high-frequency reconstruction. The clean signals from high-frequency channel are reconstructed through channel translation, which is essentially effective in preserving detailed structures. The public dataset from Mayo Clinic was used for model training and testing. The experiments showed that the proposed method achieves a better quantitative result (PSNR: 37.42dB, SSIM: 0.8990) and details recovery visually, which demonstrates our framework can better restore the detailed features while significantly suppressing the noise.
Graphical Association Analysis for Identifying Variation in Provider Claims for Joint Replacement Surgery
Authors: Kemp James , Barker Christopher , Good Norm , Bain Michael
Published: January 2024

Identifying potentially fraudulent or wasteful medical insurance claims can be difficult due to the large amounts of data and human effort involved. We applied unsupervised machine learning to construct interpretable models which rank variations in medical provider claiming behaviour in the domain of unilateral joint replacement surgery, using data from the Australian Medicare Benefits Schedule. For each of three surgical procedures reference models of claims for each procedure were constructed and compared analytically to models of individual provider claims. Providers were ranked using a score based on fees for typical claims made in addition to those in the reference model. Evaluation of the results indicated that the top-ranked providers were likely to be unusual in their claiming patterns, with typical claims from outlying providers adding up to 192% to the cost of a procedure. The method is efficient, generalizable to other procedures and, being interpretable, integrates well into existing workflows.
PEPS: Polygenic Epistatic Phenotype Simulation
Authors: Reguant Roc , O’Brien Mitchell J. , Bayat Arash , Hosking Brendan , Jain Yatish , Twine Natalie A. , Bauer Denis C.
Published: January 2024

Genetic data is limited and generating new datasets is often an expensive, time-consuming process, involving countless moving parts to genotype and phenotype individuals. While sharing data is beneficial for quality control and software development, privacy and security are of utmost importance. Generating synthetic data is a practical solution to mitigate the cost, time and sensitivities that hamper developers and researchers in producing and validating novel biotechnological solutions to data intensive problems. Existing methods focus on mutation frequencies at specific loci while ignoring epistatic interactions. Alternatively, programs that do consider epistasis are limited to two-way interactions or apply genomic constraints that make synthetic data generation arduous or computationally intensive. To solve this, we developed Polygenic Epistatic Phenotype Simulator (PEPS). Our tool is a probabilistic model that can generate synthetic phenotypes with a controllable level of complexity.
A Symptom-Based Natural Language Processing Surveillance Pipeline for Post-COVID-19 Patients
Authors: Silverman Greg M. , Rajamani Geetanjali , Ingraham Nicholas E. , Glover James K. , Sahoo Himanshu S. , Usher Michael , Zhang Rui , Ikramuddin Farha , Melnik Tanya E. , Melton Genevieve B. , Tignanelli Christopher J.
Published: January 2024

Post-acute sequelae of SARS CoV-2 (PASC) are a group of conditions in which patients previously infected with COVID-19 experience symptoms weeks/months post-infection. PASC has substantial societal burden, including increased healthcare costs and disabilities. This study presents a natural language processing (NLP) based pipeline for identification of PASC symptoms and demonstrates its ability to estimate the proportion of suspected PASC cases. A manual case review to obtain this estimate indicated our sample incidence of PASC (13%) was representative of the estimated population proportion (95% CI: 19±6.22%). However, the high number of cases classified as indeterminate demonstrates the challenges in classifying PASC even among experienced clinicians. Lastly, this study developed a dashboard to display views of aggregated PASC symptoms and measured its utility using the System Usability Scale. Overall comments related to the dashboard’s potential were positive. This pipeline is crucial for monitoring post-COVID-19 patients with potential for use in clinical settings.
Artificial Intelligence Approach for Severe Dengue Early Warning System
Authors: Anggraini Ningrum Dina Nur , Li Yu-Chuan (Jack) , Hsu Chien-Yeh , Solihuddin Muhtar Muhammad , Pandu Suhito Hanif
Published: January 2024

Dengue fever is a viral infectious disease transmitted through mosquito bites, and has symptoms ranging from mild flu-like symptoms to deadly complications. Dengue fever is one of the global burden diseases which annually have 50-100 million cases with 500,000 cases of severe dengue fever, of which 22,000 deaths occur mostly in children. Despite the discovery of vaccines, vector control is still the main approach for prevention efforts. Early detection and accessibility to medical care can reduce severe Dengue mortality rate from 50% to 2%. In the previous study, both statistical and machine learning methods have the potential for predicting a Dengue outbreak, but the study is still fragmented and limited on implementing the generated model into an early warning system application. In this study, we developed an artificial intelligence model with spatiotemporal to predict Dengue outbreak and Dengue incidence case which is ready to be implemented into an early warning system application. Indonesia, especially Semarang City, has experienced an endemic Dengue. We used Semarang City spatiotemporal, meteorological, climatological, and Dengue surveillance epidemiology data from January 2014 to December 2021 in 16 districts of Semarang City. We reviewed 7208 samples from 16 districts and 1 city per week during 8 years. The entire dataset was divided into training (80%) and testing (20%) to develop a prediction model. We used machine learning and Long Short Term Memory (LSTM) to predict Dengue outbreak 1 week before the event for each district. and machine learning to predict Dengue incident cases 1 week before the event for each district. Accuracy, area under the receiver operating characteristic curve (AUROC), precision, recall, and F1 score were considered to evaluate the Dengue outbreak prediction model. The Dengue incidence cases prediction model will evaluate using Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-Squared (R2). Extra Trees Classifier model shown outperform in Dengue outbreak prediction, with accuracy 0.8925, AUROC 0. 9529, Recall 0.6117, precision 0.8880, and F1 score 0.7238. CatBoost Regressor model is shown to outperform in Dengue incidence cases prediction, with R2 0.5621, MAE 0.6304, MSE 1.1997, and RMSE 1.0891. The study proves that Artificial Intelligence (AI) with a spatiotemporal approach can give higher performance in Dengue outbreak and incidence cases prediction. Utilization of AI approaches that are sensitive with spatiotemporal feasibility to implement in Dengue early warning system application may contribute to increase the policy makers and community attention to do accurate community-based vector control.
Characterizing Cluster-Based Frailty Phenotypes in a Multicenter Prospective Cohort of Kidney Transplant Candidates
Authors: Abidi Syed Hani Raza , Zincir-Heywood Nur , Abidi Syed Sibte Raza , Jalakam Kranthi , Abidi Samina , Gunaratnam Lakshman , Suri Rita , Cardinale Héloïse , Vinson Amanda , Prasad Bhanu , Walsh Michael , Yohanna Seychelle , Worthen George , Tennankore Karthik
Published: January 2024

Frailty is associated with a higher risk of death among kidney transplant candidates. Currently available frailty indices are often based on clinical impression, physical exam or an accumulation of deficits across domains of health. In this paper we investigate a clustering based approach that partitions the data based on similarities between individuals to generate phenotypes of kidney transplant candidates. We analyzed a multicenter cohort that included several features typically used to determine an individual’s level of frailty. We present a clustering based phenotyping approach, where we investigated two clustering approaches—i.e. neural network based Self-Organizing Maps (SOM) with hierarchical clustering, and KAMILA (KAy-means for MIxed LArge data sets). Our clustering results partition the individuals across 3 distinct clusters. Clusters were used to generate and study feature-level phenotypes of each group.
A Deep Multi-Task Network to Learn Tumor Pathological Representations for Lymph Node Metastasis Prediction
Authors: Hu Danqing , Liu Bing , Cheng Lechao , Guo Rui , Wang Jin , Lu Xudong , Wu Nan
Published: January 2024

Lymph node metastasis is of paramount importance for patient treatment decision-making, prognosis evaluation, and clinical trial enrollment. However, accurate preoperative diagnosis remains challenging. In this study, we proposed a multi-task network to learn the primary tumor pathological features using the pT stage prediction task and leverage these features to facilitate lymph node metastasis prediction. We conducted experiments using electronic medical record data from 681 patients with non-small cell lung cancer. The proposed method achieved a 0.768 area under the receiver operating characteristic curve (AUC) value with a 0.073 standard deviation (SD) and a 0.448 average precision (AP) value with a 0.113 SD for lymph node metastasis prediction, which significantly outperformed the baseline models. Based on the results, we can conclude that the proposed multi-task method can effectively learn representations about tumor pathological conditions to support lymph node metastasis prediction.
Explainable Artificial Intelligence for Deep-Learning Based Classification of Cystic Fibrosis Lung Changes in MRI
Authors: Ringwald Friedemann G. , Martynova Anna , Mierisch Julian , Wielpütz Mark , Eisenmann Urs
Published: January 2024

Algorithms increasing the transparence and explain ability of neural networks are gaining more popularity. Applying them to custom neural network architectures and complex medical problems remains challenging. In this work, several algorithms such as integrated gradients and grad came were used to generate additional explainable outputs for the classification of lung perfusion changes and mucus plugging in cystic fibrosis patients on MRI. The algorithms are applied on top of an already existing deep learning-based classification pipeline. From six explain ability algorithms, four were implemented successfully and one yielded satisfactory results which might provide support to the radiologist. It was evident, that the areas relevant for the classification were highlighted, thus emphasizing the applicability of deep learning for classification of lung changes in CF patients. Using explainable concepts with deep learning could improve confidence of clinicians towards deep learning and introduction of more diagnostic decision support systems.
Whole-Liver Based Deep Learning for Preoperatively Predicting Overall Survival in Patients with Hepatocellular Carcinoma
Authors: Huang Chao , Hu Peijun , Tian Yu , Wang Yangyang , Gao Yiwei , Qi Qianqian , Zhang Qi , Liang Tingbo , Li Jingsong
Published: January 2024

Survival prediction is crucial for treatment decision making in hepatocellular carcinoma (HCC). We aimed to build a fully automated artificial intelligence system (FAIS) that mines whole-liver information to predict overall survival of HCC. We included 215 patients with preoperative contrast-enhance CT imaging and received curative resection from a hospital in China. The cohort was randomly split into developing and testing subcohorts. The FAIS was constructed with convolutional layers and full-connected layers. Cox regression loss was used for training. Models based on clinical and/or tumor-based radiomics features were built for comparison. The FAIS achieved C-indices of 0.81 and 0.72 for the developing and testing sets, outperforming all the other three models. In conclusion, our study suggest that more important information could be mined from whole liver instead of only the tumor. Our whole-liver based FAIS provides a non-invasive and efficient overall survival prediction tool for HCC before the surgery.
Pneumococcal Vaccination Lowers the Risk of Alzheimer’s Disease: A Study Utilizing Data from the IBM® MarketScan® Database
Authors: Huo Xingyue , Finkelstein Joseph
Published: January 2024

Previous studies demonstrated an association between influenza vaccination and the likelihood of developing Alzheimer’s disease. This study was aimed at assessing whether pneumococcal vaccinations are associated with a lower risk of Alzheimer’s disease based on analysis of data from the IBM® MarketScan® Database. Vaccinated and unvaccinated matched cohorts were generated using propensity-score matching with the greedy nearest-neighbor matching algorithm. The conditional logistic regression method was used to estimate the relationship between pneumococcal vaccination and the onset of Alzheimer’s disease. There were 142,874 subjects who received the pneumococcal vaccine and 14,392 subjects who did not. The conditional logistic regression indicated that the people who received the pneumococcal vaccine had a significantly lower risk of developing Alzheimer’s disease as compared to the people who did not receive any pneumococcal vaccine (OR=0.37; 95%CI: 0.33-0.42; P-value < .0001). Our findings demonstrated that the pneumococcal vaccine was associated with a 63% reduction in the risk of Alzheimer’s disease among US adults aged 65 and older.
Architecture of the Mass Spectrometry Data Management Pipeline in the SMART-CARE Project
Authors: Ringwald Friedemann G. , Dudchenko Aleksei , Knaup Petra , Czernilofsky Felix , Dietrich Sascha , Ganzinger Matthias
Published: January 2024

In the SMART-CARE project- a systems medicine approach to stratification of cancer recurrence in Heidelberg, Germany - a streamlined mass-spectrometry (MS) workflow for identification of cancer relapse was developed. This project has multiple partners from clinics, laboratories and computational teams. For optimal collaboration, consistent documentation and centralized storage, the linked data repository was designed. Clinical, laboratory and computational group members interact with this platform and store meta- and raw-data. The specific architectural choices, such as pseudonymization service, uploading process and other technical specifications as well as lessons learned are presented in this work. Altogether, relevant information in order to provide other research groups with a head-start for tackling MS data management in the context of systems medicine research projects is described.
Prediction of Coronary Artery Disease Risk Using Genetic and Phenotypic Variables
Authors: Sng Letitia M.F. , Sharma Reevanshi , Bagot Sam , Bauer Denis C. , Twine Natalie A.
Published: January 2024

Coronary artery disease (CAD) has the highest disease burden worldwide. To manage this burden, predictive models are required to screen patients for preventative treatment. A range of variables have been explored for their capacity to predict disease, including phenotypic (age, sex, BMI and smoking status), medical imaging (carotid artery thickness) and genotypic. We use a machine learning models and the UK Biobank cohort to measure the prediction capacity of these 3 variable categories, both in combination and isolation. We demonstrate that phenotypic variables from the Framingham risk score have the best prediction capacity, although a combination of phenotypic, medical imaging and genotypic variables deliver the most specific models. Furthermore, we demonstrate that Variant Spark, a random forest based GWAS platform, performs effective feature selection for SNP-based genotype variables, identifying 115 significantly associated SNPs to the CAD phenotype.
Tabular, Annotated, Visual, or Trends + Contextual Information? Preferences for Online Laboratory Results Displays
Authors: Monkman Helen , MacDonald Leah , Joseph Amanda L. , Lesselroth Blake J.
Published: January 2024

People are increasingly offered access to their personal health information (e.g., laboratory results, clinical notes, diagnostic imaging results). However, this information is the same as that used by health care providers with clinical expertise and training in medical terminology, which citizens typically do not have. In this study, we examined participants (N = 24) preferences for four different types of displays for online laboratory (lab) results: Tabular, Annotated, Visual, and Trends + Contextual Information. The Friedman test of difference comparing participants’ ratings of the four displays was significant, χ2(3)=10.8, P=.013, and the Wilcoxon signed rank pairwise comparison tests revealed that participants rated the visual lab results display significantly more favourably than the traditional display (Z=-2.746, P=.006). These findings indicate that many people prefer lab results displayed using more visual cues and some perceived this format as easier to understand than the other display formats. Given the importance of people accessing, understanding, and using their own health information, it is crucial for displays and systems to provide a better user experience. Displaying data (e.g., lab results) visually is one possible way to improve interpretability of personal health information provided to the public.
Fighting the Same Battles on a New Battleground: Embedding Technologies in a Virtual Care Environment
Authors: Bamgboje-Ayodele Adeola , Boscolo Adrian , Hutchings Owen , Shaw Miranda , Burger Mitchell , Taggart Richard , Simpson Mark , Shaw Tim , McPhail Steven , Baysari Melissa T.
Published: January 2024

The pandemic necessitated the rapid design, development and implementation of technologies to allow remote monitoring of COVID-19 patients at home. This study aimed to explore the environmental barriers and facilitators to the successful development and implementation of virtual care technologies in this fast-paced context. We interviewed eight staff at a virtual hospital in Australia. We found key facilitators to be a learning organizational culture and strong leadership support. Barriers included interoperability issues, legislative constraints and unrealistic clinician expectations. Also, we found that a combination of hot-desking and the lack of single sign on in the virtual care environment, was reported to create additional work for staff. Overall, despite this unique context, our findings are consistent with prior work examining design and implementation of healthcare technologies. The fast pace and high-pressure environment appeared to magnify previously reported barriers, but also cultivate and foster a learning culture.
Why Parents Do Not Use Digital Health Interventions for Their Child’s Mental Health?
Authors: Peyton Daniel , Wadley Greg , Hackworth Naomi , Hiscock Harriet
Published: January 2024

There are free, evidence-based Digital Health Interventions (DHIs) that can help children’s mental health, but few parents use them. We sought to understand what influenced uptake of DHIs by parents of children aged 2-12 years old with a mental health problem. We interviewed parents and analysed data using inductive content analysis. Four factors emerged: i) personal capacity; ii) awareness of DHIs and where to find one; iii) credibility, including trust and endorsement from experts; and iv) the suitability of a DHI to their child’s specific problem and level of need. Incorporating these themes into the design of future DHIs may improve uptake.
Building Research Infrastructure to Develop Greater Learning Efficiencies (BRIDGE)
Authors: Elbers Danne C. , Fillmore Nathanael R. , La Jennifer , Tosi Hannah M. , Ajjarapu Samuel , Dhond Rupali , Murray Karen , Valley Danielle , Shannon Colleen , Brophy Mary T. , Do Nhan V.
Published: January 2024

In this manuscript, we outline our developed version of a Learning Health System (LHS) in oncology implemented at the Department of Veterans Affairs (VA). Transferring healthcare into an LHS framework has been one of the spearpoints of VA’s Central Office and given the general lack of evidence generated through randomized control clinical trials to guide medical decisions in oncology, this domain is one of the most suitable for this change. We describe our technical solution, which includes a large real-world data repository, a data science and algorithm development framework, and the mechanism by which results are brought back to the clinic and to the patient. Additionally, we propose the need for a bridging framework that requires collaboration between informatics specialists and medical professionals to integrate knowledge generation into the clinical workflow at the point of care.
Case-Based Learning in a Simulated Electronic Medical Record: Digital Health Education for Nursing Students
Authors: Jones Sophie , Gerdtz Marie , Ukovich Deanne , Marriott Philippa , Merolli Mark
Published: January 2024

Nursing graduates require competence in the use of digital health technologies. The Department of Nursing at the University of Melbourne has integrated an Electronic Medical Record (EMR) training platform into the entry-to-practice curriculum to enhance students’ digital literacy. The Case Based Learning (CBL) software, developed by CSIRO and the University of Queensland, presents students with custom-built simulated cases in an EMR. Over 300 entry-to-practice students have used the CBL in 2022. Student experience, acceptability and usability of this software is reported. Data was collected via Qualtrics XM© survey. Student feedback suggests the software was easy to use and has the potential to enhance their understanding of digital health. Students suggested adjustments to the software to maximise their opportunities for clinical decision-making and improve fidelity.
Health Service Managers’ Digital Competencies: A Conceptual Framework
Authors: Brommeyer Mark , Whittaker Maxine , Liang Zhanming
Published: January 2024

Health service managers play a crucial role in managing and leading in the digital health environment. Development of the health management workforce that is ready to lead and manage digital health transformation requires partnerships across sectors, in not only developing workforce competence but also in developing supportive mechanisms that can translate competencies into practice. A framework presenting a systematic approach in enabling the development of a competent health management workforce in the digital health era has recently been published. The purpose of this paper is to explore and discuss the application of the framework in the Australian context, informed by the findings of a PhD research project that uses an empirically validated four-step approach to confirm the health service management workforce development needs in the digital health context. The PhD project has already confirmed: 1) the paucity of Australian Health Informatics Competency Framework (AHICF) competencies being included in Australian health service management postgraduate program curricula; 2) five key strategies that contribute to developing health management workforce competency and capacity; and 3) seven key factors that enable health management workforce development in the digital health context. Further understanding of the barriers and enablers for health service managers to develop capability and manage in the digital health environment, and the factors that influence digital health policy and practice will be developed, by critically analysing findings from focus group discussions with health managers and semi-structured interviews with digital health leaders, to be completed by May 2023.
An Exploration of the Certified Health Informatician Australasia (CHIA) Participants
Authors: McNeile McCormick Desiree , Bichel-Findlay Jen , O’Driscoll David , Butler-Henderson Kerryn , Tarabay Tanija
Published: January 2024

The Certified Health Informatician Australasian (CHIA) is an assessment of a candidate’s capabilities measured using a core set of health informatics competencies. The aim of this paper is to describe the outcomes of the first eight years since the program’s launch. This paper contributes to the competency framework and certification discourse, and knowledge of the increasing importance and recognition of health informaticians through certification. An analysis of results and possible contributing factors is discussed.
Resilience Informatics for Public Health
Authors: Iyengar M. Sriram , Merchant Nirav , Ernst Kacey , Rains Stephen , Arora Mona , Ngaybe Maiya G. Block , Gonzalez Myla
Published: January 2024

Resilience research is attracting increasing attention as stressors such as pandemics and climate change impact normal life worldwide. Informatics tools can play an important role in enhancing resilience of people, communities, and organizations. We present Resilience Informatics as a sub-discipline of resilience research and propose a conceptual framework for Resilience Informatics to aid in the development and effective deployment of informatics systems for resilience.
Citizens’ Access to Online Health Information – An International Survey of IMIA Member Countries
Authors: Eriksen Jeppe , Monkman Helen , Adler-Milstein Julia , Tornbjerg Eriksen Kristina , Nøhr Christian
Published: January 2024

Citizens’ access to their online health information is pivotal. Therefore, this study examines citizens’ access to their online health information across countries and healthcare settings. The study is based on a survey design targeting the 98 IMIA representatives of the national societies. Results indicate that Test results and Medications are the two types of online information that citizens in most cases have access to. Ten countries provide citizens access to all the different types of information included in the study. That relatively few countries provide citizens access to all the included types of online health information underscores the importance of continuous emphasis on accessibility and research within this field.
Survey of Potential Disparity Issues Among Minority Stroke Survivors in Central Brooklyn
Authors: Jumbo Adiebonye E. , Kaufman David R. , Faysel Mohammad A. , Afable Aimee , Levine Steven R.
Published: January 2024

Medical data and information are ubiquitous due to internet availability. However, most people persist in using and trusting their healthcare professionals for health information. They are increasingly flooded with health information from diverse sources such as the internet, community-based organizations, and family or friends. It is crucial to understand where stroke patients vary in the use and trust of health information to improve their conditions. This pilot study aimed to better understand the nature of the problems confronted by stroke patients, such as the source of reliable health information, trust in the healthcare system, and technology trend awareness. African American and Afro-Caribbean stroke patients (n=64) residing in Central Brooklyn, New York, participated in a survey. The results showed that physicians remained the most highly trusted information sources for stroke patients, particularly among patients with higher education, irrespective of the available communication sources.
Measuring Associations Between Community-Level Social Determinants of Health and Bariatric Surgery Weight Loss Outcomes
Authors: Skoufis Nicholas , Zhang Rui , Chen You
Published: January 2024

Bariatric surgery is a crucial intervention in managing obesity and related conditions. However, weight loss outcomes can vary significantly, and social determinants of health (SDoH) at the community level may play a role. Our objective is to identify community-level SDoH factors associated with reduced weight loss after bariatric surgery. We conducted an analysis of electronic health records and the social vulnerability index (SVI) of 3,800 patients who underwent bariatric surgery at Vanderbilt University Medical Center. We measured the associations between SVI social factors and the percent change in body mass index three months after surgery using linear regression. The SVI factors with a false discovery rate-adjusted p-value < 0.05 were deemed significant. Statistical results show that patients who reside in communities with racial minority groups or lower insurance rates had reduced weight loss three months after surgery.
Digital Health Consent – for Better Interoperability and Consumer Control
Authors: Milosevic Zoran , Pyefinch Frank
Published: January 2024

We present a framework for digital health consent in support of computable expression of medico-legal policies to govern consumer data sharing across providers. The approach is aligned with HL7 FHIR© standard, and is based on generic formalism for policy modelling, grounded in normative systems and deontic logic. We show the use of the framework for a clinical research scenario.
Usability of Clinical Decision Support for Adult Sepsis Detection
Authors: Ackermann Khalia , Westbrook Johanna , Li Ling
Published: January 2024

This review summarized current literature investigating the usability of computerized clinical decision support (CCDS) systems for the early detection of sepsis in adult inpatients. Ten databases were systematically searched, identifying nine studies. Overall, the lack of good usability testing and the critical need for setting-specific testing were highlighted, as each different CCDS and unique hospital environment brings a diverse range of usability concerns that must be managed for CCDS systems to effectively improve patient care.
An Image Retrieval Pipeline in a Medical Data Integration Center
Authors: Cheng Ka Yung , Pazmino Santiago , Bergh Björn , Lange-Hegermann Markus , Schreiweis Björn
Published: January 2024

Medical images need annotations with high-level semantic descriptors, so that domain experts can search for the desired dataset among an enormous volume of visual media within a Medical Data Integration Center. This article introduces a processing pipeline for storing and annotating DICOM and PNG imaging data by applying Elasticsearch, S3 and Deep Learning technologies. The proposed method processes both DICOM and PNG images to generate annotations. These image annotations are indexed in Elasticsearch with the corresponding raw data paths, where they can be retrieved and analyzed.
Exploring Nurse’ Use of Digital Nursing Technology
Authors: Chen Wen-Chun , Lee Ting-Ting
Published: January 2024

This is a quantitative cross-sectional study using the characteristics of innovation diffusion theory to evaluate nurse’ acceptance and adoption of digital nursing technology (DNT). Data were collected through questionnaires based on innovation diffusion theory in the wards of a regional hospital in Taiwan from March 21 to May 31, 2022. Results indicated that the higher the innovative characteristics of DNT, the higher the DNT acceptance. Difficulties with network connections contributed to negative experiences and led to recommendations for future system improvement.
Effectiveness of Leakage Prevention System in Communicating Important Diagnostic Information
Authors: Tani Yuji , Hayashi Tatsuya , Iwata Tatsuya
Published: January 2024

In Japan, oversights of imaging or pathology examination results and diagnoses provided to patients have become a major problem because they affect patient prognosis. We have jointly developed and used the “Anti-Impact Information Leakage Prevention System (AiR)” since December 2019. This system works effectively because its introduction, which uses a data warehouse, has increased versatility and considerably improved the situation of confirmation and communication. We believe this system is working effectively.
Automated Feature Selection from Medical Literature
Authors: Purpura Alberto , Boschi Tobia , Bonin Francesca , Ordonez-Hurtado Rodrigo , Mulligan Natasha , Bettencourt-Silva Joao H. , Pascale Alessandra
Published: January 2024

We propose an automated approach to rank the most salient variables related to a certain clinical phenomenon from scientific literature. Our solution is an automated approach to improve the efficiency of the collection of different health-related measures from a population, and to accelerate the discovery of novel associations and dependencies between health-related concepts.
Detecting Emotional Context for Safer Digital Mental Health Agents
Authors: Choi Adi , Li Weihua , Warren Jim
Published: January 2024

Digital tools for mental health show great promise, but concerns arise when they fail to recognize the user state. We train a classifier to detect the emotional context of dialogs among 6 categories, achieving 78% accuracy on top choice. Importantly greatest areas of confusion (excited-hopeful, angry-sad) are not of the most unsafe kind. Such a classifier could serve as a resource to the dialog managers of future digital mental health agents.
A Linguistic Analysis Examining the Impact of COVID-19 on Pneumonia Diagnosis and Disease Models
Authors: Chapman Alec B. , Peterson Kelly S. , Rutter Elizabeth , Nevers Mckenna , Ying Jian , Classen David , Jones Makoto , Samore Matthew , Jones Barbara
Published: January 2024

Written clinical language embodies and reflects the clinician’s mental models of disease. Prior to the COVID-19 pandemic, pneumonia was shifting away from concern for healthcare-associated pneumonia and toward recognition of heterogeneity of pathogens and host response. How these models are reflected in clinical language or whether they were impacted by the pandemic has not been studied. We aimed to assess changes in the language used to describe pneumonia following the COVID-19 pandemic.
Machine Learning Model to Extract Malnutrition Data from Nursing Notes
Authors: Alkhalaf Mohammad , Yin Mengyang , Deng Chao , Chang Hui-Chen (Rita) , Yu Ping
Published: January 2024

Malnutrition is a severe health problem that is prevalent in older people residing in residential aged care facilities. Recent advancements in machine learning have made it possible to extract key insight from electronic health records. To date, few researchers applied these techniques to classify nursing notes automatically. Therefore, we propose a model based on ClinicalBioBert to identify malnutrition notes. We evaluated our approach with two mainstream approaches. Our approach had the highest F1-score of 0.90.
Uncovering Variations in Clinical Notes for NLP Modeling
Authors: Liu Jinghui , Capurro Daniel , Nguyen Anthony , Verspoor Karin
Published: January 2024

Clinical text contains rich patient information and has attracted much research interest in applying Natural Language Processing (NLP) tools to model it. In this study, we quantified and analyzed the textual characteristics of five common clinical note types using multiple measurements, including lexical-level features, semantic content, and grammaticality. We found there exist significant linguistic variations in different clinical note types, while some types tend to be more similar than others.
RHEA: Real-World Observational Health Data Exploration Application
Authors: Seol Soobeen , Park Jimyung , Kim Chungsoo , Lee Dong Yun , Park Rae Woong
Published: January 2024

We developed a standardized framework named RHEA to represent longitudinal status of patient with cancer. RHEA generates a dashboard to visualize patients’ data in the Observational Medical Outcomes Partnership-Common Data Model format. The generated dashboard consists of three main parts for providing the macroscopic characteristics of the patient: 1) cohort-level visualization, 2) individual-level visualization and 3) cohort generation.
Developing an Appendicectomy Surgical Pathway Ontology (ASPO)
Authors: Pathiraja Rathnayaka Hitige Nadeesha , Yu Ping
Published: January 2024

This study proposes an ontology model to represent the surgical pathways for appendicectomy, incorporating domain knowledge extracted from literature and electronic records in an Australian health district. The ontology comprised 108 concepts and 81 object properties. The model is useful for continuous quality improvement initiatives, i.e., process mining to understand what happens in the hospital versus what is required by the policy.
Automated Diabetic Retinopathy Diagnosis for Improved Clinical Decision Support
Authors: Boyle Justin , Vignarajan Janardhan , Saha Sajib
Published: January 2024

We report on the prediction performance of artificial intelligence components embedded into a telehealth platform underlying a newly established eye screening service connecting metropolitan-based ophthalmologists to patients in remote indigenous communities in Northern Territory and Queensland. Two AI-based components embedded into the telehealth platform were evaluated on retinal images collected from 328 unique patients: an image quality alert system and a diabetic retinopathy detection system. Compared to ophthalmologists, at an individual image level, the image quality detection algorithm was correct 72% of the time, and 85% accurate at a patient level. The retinopathy detection algorithm was correct 85% accurate at an individual image level, and 87% accurate at a patient level. This evaluation provides assurances for future service models using AI to complement and support decisions of eye health assessment teams.
Moderate-to-Vigorous Physical Activity Changes with the Risk of SARS-CoV-2 Infection: A Nested Case-Control Study
Authors: Lim Yo hwan , Lee Myeong Hoon , Kim Gang Hyun , Pyun Dan Bi , Jeong Seogsong , Han Hyun Wook
Published: January 2024

We identified the association of changes in moderate-to-vigorous physical activity (MVPA) with SARS-CoV-2 infection. From 6,396,500 patients, we performed a nested case-control study who participated in both biennial check-ups. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) were calculated using multivariable logistic regression. From physically inactive patients at period I, the odds increased when engaged in 1-2, 3-4, or ≥5 times of MVPA/week at period II. This study found that MVPA was directly associated with SARS-CoV-2 infection.
Sharing Colorectal Cancer Follow-Up Using an E-Care Plan Between Cancer Services and Primary Health Care
Authors: Taggart Jane , Chin Melvin , Liauw Winston , Harris Mark F.
Published: January 2024

High quality, long term follow-up care for cancer patients needs to be coordinated, comprehensive and tailored to the diverse needs of patients. This study implemented shared follow-up care using an interactive e-care plan that provided a collaborative space to schedule and share goals, tasks and information and support the monitoring of care. Qualitative results identified good relational coordination. Increasing communication from the cancer service is important.
Patient Access to Health Data: A Review of Philosophic and Healthcare Issues
Authors: Xu Andrew , Chapman Brian E.
Published: January 2024

In 2012 Australia created a national Personal Controlled Electronic Health Record (PCEHR) known as “My Health Record” (MHR). However, MHR has seen low patient utilization. Debate regarding MHR has centered on utility and moral issues (e.g. data privacy). We conducted a narrative review to assess patient perception and clinical utility of PCEHRs worldwide. Results show patient and clinician support for PCEHRs but little evidence of improved outcomes and patient concerns regarding data providence.
Evaluating Informatics Competencies Requirements for Hospital Nurse Managers in Japan
Authors: Shobuzawa Sachiko , Endo Yoshihito , Okada Mizuho
Published: January 2024

This study classifies the nursing informatics competency requirements for nurses and healthcare leaders in the United States according to each of the four levels listed in the “Management Ladder for Hospital Nurse Managers (JNA version)” published by the Japanese Nursing Association. Computer skills were included in Level I. Fifteen informatics knowledge items and four informatic competency items that could not be classified for the levels of the management ladder for nurse managers in Japan. This list of nursing informatics competencies, categorized according to the management levels of hospital nurse managers, can be used to provide nursing informatics training to them.
Providing Practical Knowledge and Skills to Handle Real-World Data? Lessons Learned from Med RWD Program
Authors: Kubota Kazumi , Seki Tomohisa , Miyake Kana , Okada Masafumi , Nishio Kazuyuki , Ohe Kazuhiko
Published: January 2024

Both lectures and hands-on education are essential for the development of human resources that can use real-world data (RWD). The University of Tokyo has launched a new hybrid-style RWD educational program entitled “Medical Real World Data Utilization Human Resource Development Project” from FY2019 onwards. We present an overview of the overall picture of the project, including the development process of the educational program and the challenges associated with it.
Optimal Transport System for Acute Ischemic Stroke Patients: A Cost-Effectiveness Analysis
Authors: Morii Yasuhiro , Osanai Toshiya , Fujiwara Kensuke , Tani Yuji , Takamiya Soichiro , Tanikawa Takumi , Ogasawara Katsuhiko
Published: January 2024

This study conducted cost utility analysis comparing 4 systems of transporting acute ischemic stroke patients in Hokkaido, Japan. Hypothetical patients were generated on a geographic information system, and their outcomes were estimated according to their transport time to hospitals administering tissue plasminogen activator and/or endovascular thrombectomy. The transport systems where a neurointerventionist traveled for earlier endovascular thrombectomy were most cost-effective in some rural areas, while direct transportation to comprehensive stroke centers was more cost-effective in other areas.