bluuebunny/crossref_metadata_2025 huggingface dataset:
Hubungan Tingkat Pengetahuan Gizi Ibu, Pendapatan Keluarga, dan Berat Badan Lahir Rendah Dengan Kejadian Stunting Pada Balita
Authors: Munadi Ahmad , Abdurrachim Rijanti , Hariati Niken Widyastuti
Published: August 2024

Stunting merupakan masalah gizi jangka panjang yang disebabkan oleh kurangnya asupan makanan bergizi dalam tubuh anak sehingga pertumbuhan terhambat dan tidak sesuai usianya. Prevalensi stunting Kabupaten Hulu Sungai Utara tahun 2020 masih diatas 31% dan wilayah kerja Puskesmas Pasar Sabtu tahun 2021 yaitu 21,7% sedangkan target RPJMN tahun 2024 yaitu 14%. Tujuan penelitian ini untuk mengetahui hubungan pengetahuan gizi ibu, pendapatan keluarga, dan berat badan lahir rendah dengan kerjadian stunting pada balita usia 24-59 bulan. Metode yang digunakan observasional analitik dengan rancangan Case Control. Populasi adalah seluruh balita usia 24-56 bulan sampelnya adalah sebagian dari populasi dengan jumlah sampel 35 kelompok kasus dan 35 kelompok kontrol. Sampel didapatkan dengan teknik purposive sampling. Data penelitian diambil menggunakan kuesioner. Analisis data menggunakan uji korelasi Rank Spearman α=0,05. Hasil penelitian menunjukkan bahwa pengetahuan gizi ibu stunting tergolong kurang kelompok kasus 85,7% dan kelompok kontrol 62,8%, pendapatan keluarga stunting yang rendah kelompok kasus 77,1% dan pendapatan tinggi pada kelompok kontrol 68,6%,) dan dan berat badan lahir balita stunting BBLR pada kelompok kasus 8,6% dan BBLR pada kelompok kontrol tidak ada 0%. Sehingga dapat disimpulkan bahwa ada hubungan pengetahuan gizi ibu dan pendapatan keluarga dengan kejadian stunting dan tidak ada hubungan berat badan lahir dengan kejadian stunting. Bagi pihak puskesmas untuk meningkatkan intensitas penyuluhan tentang stunting disetiap kegiatan posyandu dengan bantuan media leaflet kepada ibu balita supaya mengetahui mengenai kejadian stunting dan dapat mencegah kejadian stunting dengan gizi seimbang.
Hubungan Tingkat Pengetahuan, Persepsi Tentang Susu Formula dan Dukungan Keluarga dengan Pemberian ASI Eksklusif
Authors: Wati Isma , Abdurrachim Rijanti
Published: August 2024

ASI merupakan Air Susu Ibu yang menjadi sumber gizi terbaik untuk tumbuh kembang bayi. Persentase pemberian ASI eksklusif menurut data Dinkes Kota Banjarbaru Tahun 2020 yaitu 44,72% dan wilayah kerja Puksesmas Banjarbaru Utara Kota Banjarbaru  Tahun 2020 hanya 34,2% sedangkan target nasional yaitu 80%. Tujuan penelitian untuk mengetahui hubungan tingkat pengetahuan, persepsi tentang susu formula dan dukungan keluarga dengan pemberian ASI eksklusif di wilayah kerja Puskesmas Banjarbaru Utara Kota Banjarbaru. Jenis penelitian bservasional analitik dengan rancangan cross sectional. Populasi adalah seluruh balita usia 6-12 bulan sebanyak 330 bayi dan sampelnya adalah bagian dari populasi dengan jumlah sampel 78 orang. Sampel didapatkan dengan teknik accidental sampling. Data penelitian diambil menggunakan kuesioner. Analisis data menggunakan uji korelasi Rank Spearman α=0,05. Hasil menunjukkan tingkat pengetahuan kurang (39,7%) dengan signifikan (p = 0,000), persepsi tentang susu formula negatif dan positif (50%) dengan signifikan (p = 0,042), dukungan keluarga rendah (35,9%) dengan signifikan (p =  0,000) dan lebih banyak bayi yang tidak ASI eksklusif (52,6%). Kesimpulan, adanya hubungan  antara tingkat pengetahuan, persepsi tentang susu formula, dan dukungan keluarga dengan pemberian ASI eksklusif di Wilayah Kerja Puskesmas Banjarbaru Utara Kota Banjarbaru. Bagi pihak puskesmas untuk memberikan sosialisasi secara berkala kepada ibu hamil dan anggota keluarga mengenai ASI eksklusif serta ASI lebih baik untuk bayi.
Hubungan Pola Asuh Orang Tua terhadap Pemberian Makan dan Berat Badan Lahir dengan Kejadian Stunting pada Balita
Authors: Adiratna Atikah , Ego Pratama Jordi , Yanti Rusmini
Published: August 2024

Stunting adalah kondisi panjang atau tinggi badan yang kurang jika dibandingkan dengan umur pada anak balita akibat kekurangan gizi kronis terutama dalam 1000 Hari Pertama Kehidupan (HPK). Stunting adalah kondisi dimana balita memiliki panjang atau tinggi badan yang kurang jika dibandingkan dengan umur. Stunting pada anak menjadi permasalahan karena berhubungan dengan meningkatnya risiko kesakitan dan kematian, gangguan pada perkembangan otak, gangguan terhadap perkembangan motorik dan terhambatnya pertumbuhan mental anak. Tujuan penelitian untuk mengetahui hubungan pola asuh orang tua terhadap pemberian makan dan berat badan lahir dengan kejadian stunting di Sungai Lulut RT 13. Jenis penelitian ini adalah penelitian observasional analitik. Kriteria stunting ditentukan berdasarkan TB/U. Data pola asuh orang tua terhadap pemberian makan diperoleh dengan melakukan wawancara kepada ibu balita. Data berat badan lahir diperoleh dari buku KIA. Analisis dilakukan dengan rank spearman untuk mengetahui hubungan antar variabel. Hasil penelitian didapatkan balita stunting 56% dan tidak stunting 44%, pola asuh orang tua terhadap pemberian makan anak rendah 84% dan tinggi 16%, dan berat badan lahir rendah 12% dan normal 88%. Balita stunting dengan pola asuh orang tua terhadap pemberian makan anak rendah 56%, dan terdapat hubungan pola asuh orang tua terhadap pemberian makan dengan kejadian stunting (p=0,021<0,05). Balita stuting dengan berat badan lahir rendah 8%, dan tidak terdapat hubungan berat badan lahir dengan kejadian stunting (p=0,972>0,05). Ada hubungan antara pola asuh orang tua terhadap pemberian makan dengan kejadian stunting. Tidak terdapat hubungan antara berat badan lahir dengan kejadian stunting.
Hubungan Praktik Pemberian Makan, Ketahanan Pangan dan Akses Informasi dengan Kejadian Stunting
Authors: Rahmawati Feby , Magdalena  , Anawar Rosihan
Published: August 2024

Stunting adalah gangguan pertumbuhan dan perkembangan yang dialami anak-anak yang mengalami kekurangan gizi, infeksi berulang, dan stimulasi psikososial yang tidak memadai. Prevalensi stunting di Wilayah Puskesmas Rawat Inap Cempaka pada 2018 adalah 40%. Penelitian ini bertujuan untuk mengetahui hubungan praktik pemberian makan, ketahanan pangan rumah tangga dan akses informasi dengan kejadian stunting pada baduta 6-23 bulan. Jenis penelitian analitik observasional dengan pendekatan cross sectional. Baduta stunting sebagian besar memiliki praktik pemberian makan yang cukup (67.2%). Baduta stunting didominasi ketahanan pangan rumah tangganya tergolong rawan pangan dengan derajat kelaparan sedang (31%). Akses informasi pada penelitian ini ditunjukkan paling banyak adalah kategori kurang (53.4%). Kesimpulan, ada hubungan praktik pemberian makan, ketahanan pangan rumah tangga, dan akses informasi dengan kejadian stunting.
Hubungan Pengetahuan Gizi, Pendapatan Keluarga dan Pola Konsumsi dengan Status Gizi Remaja
Authors: Elyagizi19 Elyawati , Abdurrachim Rijanti , Anwar Rosihan
Published: August 2024

Remaja sangat rawan dengan kebutuhan zat gizi karena tumbuh kembang yang pesat dan aktivitas beragam. Berdasarkan data Riskesdas tahun 2018 prevalensi status gizi remaja usia 16-18 tahun kategori kurus di Kabupaten Hulu Sungai Selatan sebesar 13,9%, angka ini menunjukkan prevalensi yang tinggi dibandingkan prevalensi secara nasional 9,5% dan provinsiKalimantan Selatan 8,9%. Tujuan penelitian ini untuk menganalisis hubungan pengetahuan gizi, pendapatan keluarga dan pola konsumsi dengan status gizi remaja di tiga sekolah menengah atas di wilayah Negara Kabupaten Hulu Sungai Selatan. Metode yang digunakan yaitu Observasional analitik dengan rancangan Cross Sectional. Populasi seluruh siswa kelas X dan XI berjumlah 955 orang dengan sampel 100 orang diambil dengan teknik simple random sampling. Data penelitian diambil menggunakan kuesioner. Analisis data menggunakan uji korelasi Rank Spearman α=0,05. Hasil penelitian ini menunjukkan pengetahuan gizi cukup (58%), pendapatan keluarga rendah (54%), pola konsumsi kurang (52%) dan status gizi baik (77%). Kesimpulan dari penelitian ini menunjukkan tidak ada hubungan bermakna antara pengetahuan gizi dengan status gizi remaja, ada hubungan bermakna antara pendapatan keluarga dan pola konsumsi dengan status gizi remaja. Remaja disarankan untuk memperhatikan status gizi dengan menggali informasi mengenai gizi dan meningkat asupan makan sesuai PUGS dan keanekaragaman makanannya yang bisa diperoleh dengan harga terjangkau.
Hubungan Aktifitas Fisik, Konsumsi Buah dan Sayur, dan Kualitas Tidur dengan Kejadian Hipertensi
Authors: Dyah Samsiati  , Nurhamidi  , Anwar Rosihan
Published: August 2024

Hipertensi merupakan penyakit yang cukup dominan yang terjadi di seluruh dunia dan mengakibatkan kesakitan yang sangat tinggi. Hipertensi adalah suatu keadaan saat seseorang mengalami peningkatan tekanan darah diatas batas normal yaitu ≥140/90 mmHg. Penelitian ini bertujuan untuk mengetahui hubungan antara aktivitas fisik, konsumsi buah dan sayur, dan kualitas tidur dengan kejadian hipertensi di wilayah kerja Puskesmas Martapura 1 Kabupaten Banjar tahun 2023. Tujuan penelitian untuk mengetahui hubungan antara aktivitas fisik, konsumsi buah dan sayur, dan kualitas tidur dengan kejadian hipertensi di wilayah kerja Puskesmas Martapura 1 Kabupaten Banjar tahun 2023. Jenis penelitian observasional analitik dengan desain cross sectional. Populasi dalam penelitian ini adalah semua orang yang hipertensi dan tidak hipertensi yang teregister di posbindu Puskesmas Martapura 1. Sampel diambil menggunakan teknik purposive sampling sebanyak 86 subjek. Data primer primer berupa karakteristik responden, konsumsi buah dan sayur, dan kualitas tidur diperoleh melalui wawancara menggunakan kuesioner. Analisis data menggunakan uji korelasi rank spearman. Hasil penelitian dengan uji korelasi rank spearman menunjukkan bahwa ada hubungan antara aktivitas fisik dengan kejadian hipertensi (ρ=0,007), ada hubungan antara konsumsi buah dan sayur dengan kejadian hipertensi (ρ=0,001), ada hubungan antara kualitas tidur dengan kejadian hipertensi (ρ=0,000)
Perceptions and knowledge of lung volume reduction procedures for chronic obstructive pulmonary disease in the Republic of Ireland: A survey of respiratory healthcare professionals
Authors: Plunkett Daire , Eaton Donna , Brown Rachel , Redmond Karen , O’Neill Brenda
Published: August 2024

OBJECTIVE: Lung volume reduction (LVR) procedures can improve quality of life and survival in appropriately selected individuals with emphysema. LVR is recommended in national and international guidelines for the management of chronic obstructive pulmonary disease. Between 2016 and May 2023, less than 300 procedures were completed in the Republic of Ireland (ROI). Therefore, this survey of Irish Thoracic Society (ITS) members aimed to explore respiratory healthcare professionals’ knowledge of the LVR referral pathway and criteria. It also aimed to identify and understand any obstacles which currently limit referral and assessment for LVR procedures as well as any training needs which may exist. METHODS: An online survey was developed and distributed to ITS members in April 2023. The survey focused on respondents’ knowledge of the LVR service, perceptions of LVR procedures and understanding of the referral pathway and criteria. RESULTS: There were 84 responses from 423 potential respondents, with a response rate of 20%. Most respondents are aware of the LVR service in the Mater Misericordiae University Hospital, with 30% reporting good knowledge of the eligibility criteria and 18% reporting a good understanding of the referral pathway. Appropriate patient selection and surgical complications are the main concerns cited by respondents. CONCLUSIONS: Work is needed to develop the knowledge and understanding of LVR procedures and referral process in ROI, particularly the eligibility criteria and referral pathway. Survey responses highlight the need for educational resources around LVR and this research will help to give focus to the development of these resources.
Identifying Patterns in Long Term CGM and Insulin Data – An Explorative Study
Authors: Giese Iben E. , Jensen Morten H. , Kronborg Thomas , Søndergaard Therese H. , Hangaard Stine
Published: August 2024

The increased utilization of continuous glucose monitors (CGM) and smart insulin pens (SIP) among people with type 2 diabetes generates significant health data. This study explored possible patterns in long term CGM and SIP data.
Physicians’ Views of a Digital Intervention to Support Respiratory Patients: Take-a-Breath Pre-Pilot Usability Study
Authors: Bonotis Panos , Siopis Nikolaos , Lalas Antonios , Votis Konstantinos , Tzovaras Dimitrios , Natsiavas Pantelis
Published: August 2024

Although eHealth interventions are increasingly recognized as a useful tool to support healthcare, relatively few studies focus on the physician-end’s usability. This study aims to evaluate the Healthcare Professional’s (HCP) platform of the Take-A-Breath project, a Greek initiative for personalized respiratory disease monitoring, training and self-management. The pre-pilot usability study, involving 10 participants, combines qualitative methods, behavioral observations, and standardized measures of user experience and usability. While relatively high scores indicate overall acceptance, concerns are also discussed, particularly related with the volume of information provided and actions available to the users, hindering the usability of the system due to an overload effect. Findings emphasize also the need for more tailored in-app wordings as well as the integration of similar systems with the already set up electronic health record systems. This study contributes to understanding digital intervention success among HCPs in respiratory healthcare.
Preferences for Injection Device Settings and the Association with Adherence to Growth Hormone Treatment in Patient with Growth Disorders
Authors: van Dommelen Paula , Rivera-Romero Octavio , Arnaud Lilian , Zucchiatti Chantal , Koledova Ekaterina
Published: August 2024

Adherence to recombinant human growth hormone (r-hGH; somatropin, [Saizen®], Merck Healthcare KGaA, Darmstadt, Germany) treatment is important to achieve positive growth and other outcomes in children with growth disorders. Automated injection devices can facilitate the delivery of r-hGH, injections of which are required daily for a number of years. The ability to adjust injection device settings may improve patient comfort and needle anxiety, influencing adoption and acceptance of such devices, thereby improving treatment adherence. Here, we present the results of a retrospective observational study which investigated the association between injection device settings and adherence in the first 3 months of treatment in patients with growth disorders. Patients aged ≥2 and <18.75 years of age at treatment start, with ≥3 months of adherence data from start of treatment with the third generation of the easypod® device (EP3; Merck Healthcare KGaA, Darmstadt, Germany) were selected (N=832). The two most chosen combinations of device settings at treatment start were the default settings for injection speed, depth and time, or a slow injection speed and default depth and time. These combinations also demonstrated the highest adherence rates (94% and 95%, respectively) compared to other device settings (89%). A higher proportion of patients with intermediate/low adherence in the first month of treatment (31%, n=18/59) changed the device settings during treatment compared with those with high adherence (16%, n=128/803) (p=0.005). The ability to adjust injection device settings offers a valuable opportunity for personalizing treatment, improving patient comfort and treatment adherence.
Semantic Mapping of Named-Entities in openEHR Templates and Ad-hoc Generation of Compositions
Authors: Ladas Nektarios , Franz Stefan , Wolff Dominik , Rehberg Alina , Marschollek Michael , Gietzelt Matthias
Published: August 2024

Integration of free texts from reports written by physicians to an interoperable standard is important for improving patient-centric care and research in the medical domain. In the context of unstructured clinical data, NLP Information Extraction serves in finding information in unstructured text. To our best knowledge, there is no efficient solution, in which extracted Named-Entities of an NLP pipeline can be ad-hoc inserted in openEHR compositions. We therefore developed a software solution that solves this data integration problem by mapping Named-Entities of an NLP pipeline to the fields of an openEHR template. The mapping can be accomplished by any user without any programming intervention and allows the ad-hoc creation of a composition based on the mappings.
Univie: An App for Managing Digital Health Data of Refugees
Authors: Salah Ahmad , Alobid Salah , Jalalzai Sahel , Junger Denise , Burgert Oliver
Published: August 2024

The refugee crisis has reached historic proportions, with more than 82 million people on the run. Access to healthcare is often difficult for them due to a lack of medical records and language barriers. This paper examines a digital medical documentation system for refugees that captures, stores, and translates records. International data protection standards are considered. The contribution consists of designing a system that manages and translates medical data across borders and integrates a prediction model for epidemics in refugee camps.
Extracting Evidence from Health IT Studies to Populate Logic Models
Authors: Bindel Michelle , Kreyer Christiane , Ammenwerth Elske
Published: August 2024

Background: Logic models graphically present the socio-technical components of a variety of ’programs’ such as educational programs. They show the underlying logic and assumptions of how a program is supposed to work. We suggest that they can be used to describe the mechanisms of complex socio-technical health IT interventions. Objective: To assess the suitability of logic models to describe cause-effect chains of health IT. Results: We are currently conducting an integrative review of the impact of patient portals on patient outcomes. We extracted the following elements of logic models from the found publications: resources, activities, output, outcome, and impact. These factors are then used to populate the logic model and form a structured graphical representation of the evidence. Until now, all the evidence we found could be fit into the logic model. The logic model was able to accommodate diverse types of evidence. Conclusion: Logic models seem to be suitable for representing evidence on the impact of health IT.
Using Natural Language Processing on Expert Panel Discussions to Gain Insights for Recruitment, Retention and Intervention Adherence for Online Social Support Interventions on a Stage II-III Clinical Trial Among Hispanic and African American Dementia Care
Authors: Odlum Michelle , Moon Soyoung , Broadwell Peter , Huang Niya , Sun Frederick , Tipani Dante , Davis Nicole , Kim Milea , Yoon Sunmoo
Published: August 2024

We applied natural language processing (NLP) to a corpus extracted from 4 hours of expert panel discussion transcripts to determine the sustainability of a Stage II-III clinical trial of online social support interventions for Hispanic and African American dementia caregivers. Prominent topics included Technology/hard to reach populations, Training younger populations, Building trust, Privacy and security issues, Simplification of screening questions and recruitment procedures, Understanding participants’ needs, Planning strategies and logistics, Potential recruitment places, Adjusting intervention size downwards to engage elderly participants, Targeting different generations, Internet-based interventions by age range, and Providing step-by-step instructions and an overview of the entire research process during recruitment. The application of NLP to qualitative data on a dementia caregiving clinical trial provides useful insights for recruitment, retention, and adherence to guidelines for such interventions serving Hispanic and African American dementia caregivers.
Towards Autonomous Living Meta-Analyses: A Framework for Automation of Systematic Review and Meta-Analyses
Authors: Górska Anna , Tacconelli Evelina
Published: August 2024

Systematic review and meta-analysis constitute a staple of evidence-based medicine, an obligatory step in developing the guideline and recommendation document. It is a formalized process aiming at extracting and summarizing knowledge from the published work, grading, and considering the quality of the included studies. It is very laborious and time-consuming. Therefore, the meta-analyses are rarely updated and seldom living, decreasing their utility with time. Here, we present a framework for integrating the large language models and natural language processing techniques applied to the previously published systematic review and meta-analysis of the diagnostic test accuracy of the point of care tests. We show that the framework can be used to automate the screening step of the existing meta-analyses with minimal costs to quality and, to a large extent, the extraction step while maintaining the strict nature of the systematic review process.
Sustainability in Secondary Use of Health Data – A Scoping Review
Authors: Kilgus Tim , Nowak Alessia , Gersch Martin , Fürstenau Daniel
Published: August 2024

This scoping review investigates sustainability in the reuse of health data on a technological, intra-organizational, inter-organizational, and regulatory level. Thereby, it focuses on the evolutionary, relational, and durational perspective of sustainability. The study highlights various challenges in achieving data sustainability, from regulatory norms such as FAIR principles towards data governance processes and responsibilities in organizations that facilitate data sharing. By highlighting the need for economic sustainability of health data sharing platforms and adapted principles for data sharing, this study aims to analyze current practices that aim for sustainability in the secondary use of health data.
Development of a Concourse for Two Q-Method Studies Exploring Perspectives of Patients with Multiple Sclerosis and Healthcare Professionals on the Use of Wearable Technology-Lessons Learnt
Authors: Alsulami Shemah , Konstantinidis Stathis Th. , Wharrad Heather
Published: August 2024

This paper presents the generation of a Q-set for two Q studies investigating the perspectives of (1) patients with multiple sclerosis and (2) their healthcare professionals on the use of wearable technology. It describes the adopted Q methodology and how it was applied in different phases. The concourse is derived from the relevant literature, based on the unified theory of acceptance and use of technology (UTAUT2) model, with privacy as a moderator, and Hofstede’s “Cultural Dimensions” framework, incorporates statements drawn from the concourse following review by experts This is followed by a pilot study involving 4 stakeholders to improve the relevance and quality of the research. A 43-statement Q-sample was developed for the first Q study, and a 32-statement Q-sample was developed for the second Q study. This preliminary study reported the development of a legitimate and reliable concourse in a transparent and comprehensive manner. The lessons learnt from developing the concourse in this study could be beneficial for future research when conducted in a similar digital healthcare context and in the context of MS where individuals often experience symptoms related to vision, sensation, coordination, and movement.
Quality of Electrocardiography Recorded with Consumer Smart Shirts
Authors: Haas Paulo , Spicher Nicolai , Warnecke Joana M. , Deserno Thomas M.
Published: August 2024

Smart wearables support continuous monitoring of vital signs for early detection of deteriorating health. However, the devices and sensors require sufficient quality to produce meaningful signals, in particular, if data is acquired in motion. In this study, we equipped 48 subjects with smart shirts recording one-lead electrocardiography (ECG), thoracic and abdominal respiratory inductance plethysmography, and three-axis acceleration. For 10 min each, the subjects sit, stand, walk, and run, with a resting period of 5 min in between each activity. We preprocessed the electrocardiogram and applied a signal quality index. We analyzed the signal quality index grouped by the activity and participants. For sitting, standing, walking, and running, the ECG signals provide acceptable quality over 73.20 %, 91.85 %, 12.26 %, and 13.14 % of the recording time. In conclusion, smart wearables may be useful for continuous health monitoring of people with a sedentary lifestyle, but rather not for sportive activities.
Predicting Epileptic Seizures Using Biometric Sensors – A Pilot Study Protocol
Authors: Norbye Anja Davis , Henriksen André , Hartvigsen Gunnar , Farbu Erlend Hoftun
Published: August 2024

Epilepsy is a prevalent neurological disorder characterized by unpredictable seizures despite some known risk factors. Using an mHealth system and Empatica EmbracePlus, we aim to examine if biometric sensor data can detect and predict epileptic seizures. Five participants with epilepsy will be followed for a minimum of 15 seizures. Expected results and possible implications are presented. This study may provide new insights into biological indicators for seizure forecasting.
Explainable Machine Learning Based Prediction of Severity of Heart Failure Using Primary Electronic Health Records
Authors: Ganesan Rajarajeswari , Habraken Simon C. , van de Vosse Frans N. , Huberts Wouter
Published: August 2024

Heart Failure (HF) is a life-threatening condition. It affects more than 64 million people worldwide. Early diagnosis of HF is extremely crucial. In this study, we propose utilization of machine learning (ML) models to predict severity of HF from primary Electronic Health Records (EHRs). We used a public dataset of 2008 HF patients for the study. Gaussian Naive Bayes, Random Forest and CatBoost methods were used for prediction. The study shows that CatBoost works best for the goal. In addition to that, the largest contributors for tree-based models harmonize well with clinically important parameters, which exhibits the trustworthiness of these models. Hence, we conclude that utilization of ML methods on primary EHRs is a promising step for time-efficient diagnosis of HF patients.
Automatic Classification of Conclusions from Multi-Tracer Reports of PET Brain Imaging in Cognitive Impairment
Authors: Goldman Jean-Philippe , Jané Pablo , Zaghir Jamil , Pirazzo Andrade Teixeira Eliluane , Peretti Débora Elisa , Garibotto Valentina , Lovis Christian
Published: August 2024

The goal of this paper is to build an automatic way to interpret conclusions from brain molecular imaging reports performed for investigation of cognitive disturbances (FDG, Amyloid and Tau PET) by comparing several traditional machine learning (ML) techniques-based text classification methods. Two purposes are defined: to identify positive or negative results in all three modalities, and to extract diagnostic impressions for Alzheimer’s Disease (AD), Fronto-Temporal Dementia (FTD), Lewy Bodies Dementia (LBD) based on metabolism of perfusion patterns. A dataset was created by manual parallel annotation of 1668 conclusions of reports from the Nuclear Medicine and Molecular Imaging Division of Geneva University Hospitals. The 6 Machine Learning (ML) algorithms (Support Vector Machine (Linear and Radial Basis function), Naive Bayes, Logistic Regression, Random Forrest, and K-Nearest Neighbors) were trained and evaluated with a 5-fold cross-validation scheme to assess their performance and generalizability. The best classifier was SVM showing the following accuracies: FDG (0.97), Tau (0.94), Amyloid (0.98), Oriented Diagnostic (0.87 for a diagnosis among AD, FTD, LBD, undetermined, other), paving the way for a paradigm shift in the field of data handling in nuclear medicine research.
Benchmarking Approaches: Time Series Versus Feature-Based Machine Learning in ECG Analysis on the PTB-XL Dataset
Authors: Bickmann Lucas , Plagwitz Lucas , Varghese Julian
Published: August 2024

Extensive research has been conducted on time series and tabular data in the context of classification tasks, considering their distinct data domains. While feature extraction enables the transformation of series into tabular data, direct comparative comparisons between these data types remain scarce. Especially in the domain of medical data, such as electrocardiograms (ECGs), deep learning faces challenges due to its lack of easy and fast interpretability and explainability. However, these are crucial aspects for a wide and reliable adoption in the field. In our study, we assess the performance of XGBoost and InceptionTime on ECG features and time series data respectively. Our findings reveal that features extracted from ECG signals not only achieve competitive performance but also retain advantages during training and inference. These advantages encompass accuracy, resource efficiency, stability, and a high level of explainability.
Beyond Tokens: Fair Evaluation of French Large Language Models for Clinical Named Entity Recognition
Authors: Zaghir Jamil , Bjelogrlic Mina , Goldman Jean-Philippe , Bensahla Adel , Zheng Yuanyuan , Lovis Christian
Published: August 2024

Named Entity Recognition (NER) models based on Transformers have gained prominence for their impressive performance in various languages and domains. This work delves into the often-overlooked aspect of entity-level metrics and exposes significant discrepancies between token and entity-level evaluations. The study utilizes a corpus of synthetic French oncological reports annotated with entities representing oncological morphologies. Four different French BERT-based models are fine-tuned for token classification, and their performance is rigorously assessed at both token and entity-level. In addition to fine-tuning, we evaluate ChatGPT’s ability to perform NER through prompt engineering techniques. The findings reveal a notable disparity in model effectiveness when transitioning from token to entity-level metrics, highlighting the importance of comprehensive evaluation methodologies in NER tasks. Furthermore, in comparison to BERT, ChatGPT remains limited when it comes to detecting advanced entities in French.
Synthetic Generation of Patient Service Utilization Data: A Scalability Study
Authors: Howie Joseph , Balasubramanian Sowmya , Bambi Jonas , Moselle Kenneth , Srinivasan Venkatesh , Thomo Alex
Published: August 2024

To address privacy and ethical issues in using health data for machine learning, we evaluate the scalability of advanced synthetic data generation methods like GANs, VAEs, copulaGAN, and transformer models specifically for patient service utilization data. Our study examines five models on data from a Canadian health authority, focusing on training and generation efficiency, data resemblance, and practical utility. Our findings indicate that statistical models excel in efficiency, while most models produce synthetic data that closely mirrors real data, and is also useful for real-world applications.
Healthcare LLMs Go to Market: A Realist Review of Product Launch News
Authors: Sharifi Salma , Namvar Morteza , Intezari Ali , Akhlaghpour Saeed
Published: August 2024

We provide a realist review of product launches for Large Language Models (LLMs) in the healthcare industry. Through a systematic search in the Factiva database and the application of a Context, Intervention, Mechanism, Outcome (CIMO) framework, we identified and assessed 23 significant records, representing 17 unique product launches between January 2023 and February 2024. This manuscript contributes to the emerging literature on health LLMs and Generative AI by focusing on actual product launches of healthcare LLM products—a less explored aspect than theoretical potential. Our use of the CIMO framework to dissect the application of LLMs in healthcare adds a fresh perspective to the discourse, helping to understand the outcomes and the mechanisms driving these outcomes. Among the LLM application themes that emerged from our review, we focused on four primary themes: Clinical Care and Health Services, Healthcare Documentation and Data Management, Insurance and Healthcare Financial Services, and Nutrition, Wellness, and Chronic Disease Management. Our findings demonstrate LLMs’ potential to transform patient care through personalization and efficiency, highlighting their role in enhancing healthcare delivery systems, reducing administrative burdens, and supporting decision-making processes. Specific implementations by health start-ups and large tech firms discussed in this paper underscore the immediate impact of these technologies on patient care and healthcare management. This realist model offers a new perspective on LLMs within healthcare, providing an empirical basis for future technological integration and policy development in digital health. Our study contributes to understanding how LLMs operate within the healthcare sector, emphasizing the importance of context in their successful deployment and serving as a strategic guide for future AI integration in sensitive healthcare services.
A Qualitative Study with Informal Caregivers and Healthcare Professionals for Individuals with Head and Neck Cancer on the Usage of AI Chatbots
Authors: Ahmad Awais , Premanandan Shweta , Cajander Åsa , Langegård Ulrica , Uereten Ece , Tiblom Ehrsson Ylva
Published: August 2024

Informal caregivers (ICs), including the patient’s spouse, close relatives, or friends, play an important role in caregiving individuals with head and neck cancer (HNC). AI-based chatbots might offer information and assistance related to caregiving. This study presents the viewpoints of ICs and healthcare professionals (HCPs) on using AI-based chatbots in caring for individuals with HNC. A total of six focus groups were conducted with 15 ICs and 13 HCPs from three Swedish university hospitals. The study uncovers a widespread hesitancy toward the intention to use AI-based chatbots among ICs and HCPs. Factors contributing to this reluctance include their distrust in chatbot-provided information, negative past experiences of using chatbots, and lack of human connection in chatbot interactions. Embracing a holistic approach is crucial when designing chatbots, ensuring active user engagement and incorporating their perspectives into the design process.
Enhancing Healthcare Efficiency: Integrating ChatGPT in Nursing Documentation
Authors: Chen Chia-Jung , Liao Chia-Te , Tung Yu-Chen , Liu Chung-Feng
Published: August 2024

Our study at Chi Mei Medical Center introduced “A+ Nurse,” a ChatGPT-based LLM tool, into the nursing documentation process to enhance efficiency and accuracy. The tool offers optimized recording and critical reminders, reducing documentation time from 15 to 5 minutes per patient while maintaining record quality. Nurses appreciated the tool’s intuitive design and its effectiveness in improving documentation. This successful integration of AI-generated content in healthcare illustrates the potential of AI to streamline processes and improve patient care, setting a precedent for future AI-driven healthcare innovations.
Comparative Analysis of Macular and Optic Disc Perfusion Pre and Post Silicone Oil Removal: A Machine Learning Approach
Authors: Feretzakis Georgios , Karakosta Christina , Gkoulalas-Divanis Aris , Karapiperis Dimitris , Gkontzis Andreas F. , Paxinou Evgenia , Kourentis Christina , Verykios Vassilios S.
Published: August 2024

In the realm of ophthalmic surgeries, silicone oil is often utilized as a tamponade agent for repairing retinal detachments, but it necessitates subsequent removal. This study harnesses the power of machine learning to analyze the macular and optic disc perfusion changes pre and post-silicone oil removal, using Optical Coherence Tomography Angiography (OCTA) data. Building upon the foundational work of prior research, our investigation employs Gaussian Process Regression (GPR) and Long Short-Term Memory (LSTM) networks to create predictive models based on OCTA scans. We conducted a comparative analysis focusing on the flow in the outer retina and vessel density in the deep capillary plexus (superior-hemi and perifovea) to track perfusion changes across different time points. Our findings indicate that while machine learning models predict the flow in the outer retina with reasonable accuracy, predicting the vessel density in the deep capillary plexus (particularly in the superior-hemi and perifovea regions) remains challenging. These results underscore the potential of machine learning to contribute to personalized patient care in ophthalmology, despite the inherent complexities in predicting ocular perfusion changes.
Advancing Cardiovascular Mortality Trend Analysis: A Machine Learning Approach to Predict Future Health Policy Needs
Authors: Feretzakis Georgios , Theodorakis Nikolaos , Vamvakou Georgia , Hitas Christos , Anagnostou Dimitrios , Kalantzi Sofia , Spyridaki Aikaterini , Kollia Zoi , Christodoulou Michalitsa , Kalles Dimitris , Gkontzis Andreas F. , Verykios Vassilios S. , Nikolaou Maria
Published: August 2024

This study investigates the forecasting of cardiovascular mortality trends in Greece’s elderly population. Utilizing mortality data from 2001 to 2020, we employ two forecasting models: the Autoregressive Integrated Moving Average (ARIMA) and Facebook’s Prophet model. Our study evaluates the efficacy of these models in predicting cardiovascular mortality trends over 2020-2030. The ARIMA model showcased predictive accuracy for the general and male population within the 65-79 age group, whereas the Prophet model provided better forecasts for females in the same age bracket. Our findings emphasize the need for adaptive forecasting tools that accommodate demographic-specific characteristics and highlight the role of advanced statistical methods in health policy planning.
Machine Learning upon RDF Knowledge Graphs for Drug Safety: A Case Study on Reactome Data
Authors: Kastampolidou Kalliopi , Gavriilidis George I. , Natsiavas Pantelis
Published: August 2024

Artificial Intelligence (AI), particularly Machine Learning (ML), has gained attention for its potential in various domains. However, approaches integrating symbolic AI with ML on Knowledge Graphs have not gained significant focus yet. We argue that exploiting RDF/OWL semantics while conducting ML could provide useful insights. We present a use case using signaling pathways from the Reactome database to explore drug safety. Promising outcomes suggest the need for further investigation and collaboration with domain experts.
A Digital Tool Supporting Pathology Practice and Identifying Leucocytes
Authors: Apostol Dumitru-Cristian , Chiuzbăian Antonela-Maria , Crisan-Vida Mihaela , Stoicu-Tivadar Lăcrămioara , Marcu Marius
Published: August 2024

The aim of this work is to deliver an all-in-one package that contains both the part where the pathologist can manipulate the data as well as predefined models, altogether with the digital pathology interface with a comprehensive component that provides traceability between the identified leucocytes and the underlying possible outcomes of the potential disease. The aim is to directly provide the number of leucocytes and the mass of the cell, only from the image, with minimal intervention from the pathologist, necessary to have a PoC (Proof of Concept) or a prototype. The model was trained on a dataset of around 20,000 models, and the achieved accuracy was approximately 85%. Approximately 82% of the identified areas of interest, as determined by the models, were true positive predictions. The models correctly identified approximately 89% of the actual positive instances - areas of interest - identified by the pathologist. Approximately 6% of the total actual negative instances were incorrectly classified as positive by the models. The tool provides visual scripting, reducing the learning curve for pathology analysis techniques and offers an intuitive interface for healthcare professionals.
Experimentation of AI Models Towards the Prediction of Medium-Risk Emergency Department Cases Disposition Outcome
Authors: Siakopoulou Styliana , Billis Antonis , Logaras Evangelos , Stelmach Veroniki , Zouka Maria , Fyntanidou Varvara , Bamidis Panagiotis
Published: August 2024

The overwhelming volume of patients in emergency departments (EDs) is a significant problem that hinders the delivery of high quality healthcare. Despite their great value, triage protocols are challenging to put into practice. This paper examines the utility of prediction models as a tool for clinical decision support, with a focus on medium-severity patients as defined by the ESI algorithm. 689 cases of medium-risk patients were gathered from the AHEPA hospital, evaluated, and their data fed three classifiers: XGBoost (XGB), Random Forest (RF) and Logistic Regression (LR), with the prediction goal being the outcome of their visit, i.e. admission or discharge. Essential features for the prediction task were determined using feature importance and distribution analysis. Despite having many missing values or high sparsity datasets, several symptoms and metrics are recommended as crucial for outcome prediction. When fed the patients’ vital signs, XGB achieved an accuracy score of 91.30%. Several chief complaints were also proven beneficial. Prediction models can, in general, not only lessen the drawbacks of triage implementation, but also enhance its delivery.
Development of a Data Model to Predict Nursing Workload Using Routine Clinical Data
Authors: Hunstein Dirk , Frischen Lena , Fiebig Madlen
Published: August 2024

The effective management of human resources in nursing is fundamental to ensuring high-quality care. The necessary staffing levels can be derived from the nursing-related health status. Our approach is based on the use of artificial intelligence (AI) and machine learning (ML) to recognize key workload-driving predictors from routine data in the first step and derive recommendations for staffing levels in the second step. The precedent analysis was a multi-center study with data provided by three hospitals. The SPI (Self Care Index = sum score of 10 functional/cognitive items of the epaAC (epaAC = nursing assessment tool for AcuteCare (abbreviated from the German-language effiziente Pflege-Analyse AcuteCare))) was identified as a strong predictor of nursing workload. The SPI alone explains the variance in minutes with an adjusted R2 of 40% to 66%. With the addition of further predictors such as “fatigue” or “pain intensity”, the adjusted R2 can be increased by up to 17%. The resulting model can be used as a foundation for data-based personnel controlling using AI-based prediction models.
Deep Learning Based Automatic Fibroglandular Tissue Segmentation in Breast Magnetic Resonance Imaging Screening
Authors: Pehlivan Guelsuem , Wild Carl Mathis , Baumgartl Julia , Hartmann Dennis , Ditsch Nina , Kramer Frank , Mueller Dominik
Published: August 2024

In light of the global increase in breast cancer cases and the crucial importance of the density of fibroglandular tissue (FGT) in assessing risk and predicting the course of the disease, the accurate measurement of FGT emerges as a significant challenge in diagnostic imaging. The current study focuses on the automatic segmentation of breast glandular tissue in MRI scans using a deep learning model. The aim is to establish a solid foundation for the development of methods for the precise quantification of fibroglandular tissue. For this purpose, the publicly available ‘Duke Breast Cancer MRI’ dataset was systematically processed to train a deep neural network model utilizing the nnU-Net (‘no-new-Net’) framework, which was then subjected to a quantitative evaluation. The results show the following macro-averaged metrics with standard deviation: Dice Similarity Coefficient 0.827 ± 0.152, accuracy 0.997 ± 0.003, sensitivity 0.825 ± 0.158, and specificity 0.999 ± 0.001. The effectiveness of our model in segmenting FGT is underscored by the high values of the Dice coefficient, Accuracy, Sensitivity, and Specificity, which reflect the precision and reliability of our results. The findings of this study lay a solid foundation for developing automated methods to quantify FGT. Our research efforts, especially driven by clinical studies at the University Hospital Augsburg, are focused on further exploring and validating these potentials.
Rapid Deployment of a Pseudonymization Service in a Distributed Research Data Infrastructure – Lessons Learned
Authors: Abu Attieh Hammam , Halilovic Mehmet , Meurers Thierry , Müller Armin , Neves Diogo Telmo , Prasser Fabian
Published: August 2024

The SARS-CoV-2 pandemic highlighted the importance of fast, collaborative research in biomedicine. Within the ORCHESTRA consortium, we rapidly deployed a pseudonymization service with minimal training and maintenance efforts under time-critical conditions to support a complex, multi-site research project. Over two years, the service was deployed in 13 sites across 11 countries to register more than 10,000 study participants and 15,000 biosamples. In this work, we present lessons learned as part of this process. Most importantly, we learned that common challenges can be overcome by creatively utilizing widely available tools and that having a dedicated partner to manage software rollout and pre-configure software packages for each site fosters the effective implementation.
Challenges and Opportunities of Artificial Intelligence in CDSS and Patient Safety
Authors: Gong Yang , Min Hua , Jing Xia , Yu Ping
Published: August 2024

Ensuring patient safety in healthcare involves training professionals and implementing clinical decision support systems (CDSS) and health IT solutions to reduce errors and adverse events. The integration of artificial intelligence (AI) into health IT has revolutionized clinical settings by enabling real-time insights and personalized recommendations. However, the use of health IT can lead to unintended consequences that are not adequately addressed during training and implementation. These consequences can hinder the maximization of benefits and limit equitable access to healthcare. In this paper, we explore the impact of AI on CDSS and health IT, discuss the challenges in educating clinical informaticians, and aim to promote patient safety through collaboration with practitioners, researchers, and educators.
Semantic Interoperability of Nursing Data – Mapping an Interface Terminology to SNOMED CT
Authors: Ranegger Renate , Baumberger Dieter , Grgic Pero , Jagfeld Glorianna
Published: August 2024

Using nursing intervention data within the scope of eHealth from a primary system (hospital information system) in a secondary system (electronic patient dossier) requires a common terminology across systems. For this purpose, nursing interventions from the LEP nursing interface terminology widely used in Germany, Switzerland, and Austria were mapped to the international reference terminology SNOMED CT with the support of the National Release Center Switzerland. The completed mapping comprises 467 nursing interventions from LEP Nursing Version 3.5.0 mapped to pre-coordinated SNOMED CT version 2022-12-31 procedures. Moreover, 83 new submissions were included in the international SNOMED CT edition and seven in the Swiss SNOMED CT extension. As a next step, the mapping will be tested to examine the possibilities of semantic interoperability in nursing practice.