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Objectives: To introduce the 2022 International Medical Informatics Association (IMIA) Yearbook by the editors. Methods: The editorial provides an introduction and overview to the 2022 IMIA Yearbook whose special topic is “Inclusive Digital Health: Addressing Equity, Literacy, and Bias for Resilient Health Systems”. The special topic, survey papers, section editor synopses and some best papers are discussed. The sections’ changes in the Yearbook Editorial Committee are also described. Results: As shown in the previous edition, health informatics in the context of a global pandemic has led to the development of ways to collect, standardize, disseminate and reuse data worldwide. The Corona Virus Disease 2019 (COVID-19) pandemic has demonstrated the need for timely, reliable, open, and globally available information to support decision making. It has also highlighted the need to address social inequities and disparities in access to care across communities. This edition of the Yearbook acknowledges the fact that much work has been done to study health equity in recent years in the various fields of health informatics research. Conclusion: There is a strong desire to better consider disparities between populations to avoid biases being induced in Artificial Intelligence algorithms in particular. Telemedicine and m-health must be more inclusive for people with disabilities or living in isolated geographical areas.

Objective As the storage of clinical data has transitioned into electronic formats, medical informatics has become increasingly relevant in providing diagnostic aid. The purpose of this review is to evaluate machine learning models that use text data for diagnosis and to assess the diversity of the included study populations. Methods We conducted a systematic literature review on three public databases. Two authors reviewed every abstract for inclusion. Articles were included if they used or developed machine learning algorithms to aid in diagnosis. Articles focusing on imaging informatics were excluded. Results From 2,260 identified papers, we included 78. Of the machine learning models used, neural networks were relied upon most frequently (44.9%). Studies had a median population of 661.5 patients, and diseases and disorders of 10 different body systems were studied. Of the 35.9% (N = 28) of papers that included race data, 57.1% (N = 16) of study populations were majority White, 14.3% were majority Asian, and 7.1% were majority Black. In 75% (N = 21) of papers, White was the largest racial group represented. Of the papers included, 43.6% (N = 34) included the sex ratio of the patient population. Discussion With the power to build robust algorithms supported by massive quantities of clinical data, machine learning is shaping the future of diagnostics. Limitations of the underlying data create potential biases, especially if patient demographics are unknown or not included in the training. Conclusion As the movement toward clinical reliance on machine learning accelerates, both recording demographic information and using diverse training sets should be emphasized. Extrapolating algorithms to demographics beyond the original study population leaves large gaps for potential biases.

Summary Objective: To summarize recent research and present a selection of the best papers published in 2015 in the field of clinical Natural Language Processing (NLP). Method: A systematic review of the literature was performed by the two section editors of the IMIA Yearbook NLP section by searching bibliographic databases with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. Section editors first selected a shortlist of candidate best papers that were then peer-reviewed by independent external reviewers. Results: The clinical NLP best paper selection shows that clinical NLP is making use of a variety of texts of clinical interest to contribute to the analysis of clinical information and the building of a body of clinical knowledge. The full review process highlighted five papers analyzing patient-authored texts or seeking to connect and aggregate multiple sources of information. They provide a contribution to the development of methods, resources, applications, and sometimes a combination of these aspects. Conclusions: The field of clinical NLP continues to thrive through the contributions of both NLP researchers and healthcare professionals interested in applying NLP techniques to impact clinical practice. Foundational progress in the field makes it possible to leverage a larger variety of texts of clinical interest for healthcare purposes.

Objectives: To identify current patient identification techniques and approaches used worldwide in today’s healthcare environment. To identify challenges associated with improper patient identification. Methods: A literature review of relevant peer-reviewed and grey literature published from January 2015 to October 2019 was conducted to inform the paper. The focus was on: 1) patient identification techniques and 2) unintended consequences and ramifications of unresolved patient identification issues. Results: The literature review showed six common patient identification techniques implemented worldwide ranging from unique patient identifiers, algorithmic approaches, referential matching software, biometrics, radio frequency identification device (RFID) systems, and hybrid models. The review revealed three themes associated with unresolved patient identification: 1) treatment, care delivery, and patient safety errors, 2) cost and resource considerations, and 3) data sharing and interoperability challenges. Conclusions: Errors in patient identification have implications for patient care and safety, payment, as well as data sharing and interoperability. Different patient identification techniques ranging from unique patient identifiers and algorithms to hybrid models have been implemented worldwide. However, no current patient identification techniques have resulted in a 100% match rate. Optimizing algorithmic matching through data standardization and referential matching software should be studied further to identify opportunities to enhance patient identification techniques and approaches. Further efforts to improve patient identity management include adoption of patients’ photos at registration, naming conventions, and standardized processes for recording patients’ demographic data attributes.

Background An accurate and reproducible method to delineate tumor margins is of great importance in clinical diagnosis and treatment. In nasopharyngeal carcinoma (NPC), due to limitations such as high variability, low contrast, and discontinuous boundaries in presenting soft tissues, tumor margin can be extremely difficult to identify in magnetic resonance imaging (MRI), increasing the challenge of NPC segmentation task. Objectives The purpose of this work is to develop a semiautomatic algorithm for NPC image segmentation with minimal human intervention, while it is also capable of delineating tumor margins with high accuracy and reproducibility. Methods In this paper, we propose a novel feature selection algorithm for the identification of the margin of NPC image, named as modified random forest recursive feature selection (MRF-RFS). Specifically, to obtain a more discriminative feature subset for segmentation, a modified recursive feature selection method is applied to the original handcrafted feature set. Moreover, we combine the proposed feature selection method with the classical random forest (RF) in the training stage to take full advantage of its intrinsic property (i.e., feature importance measure). Results To evaluate the segmentation performance, we verify our method on the T1-weighted MRI images of 18 NPC patients. The experimental results demonstrate that the proposed MRF-RFS method outperforms the baseline methods and deep learning methods on the task of segmenting NPC images. Conclusion The proposed method could be effective in NPC diagnosis and useful for guiding radiation therapy.

Detection and validation of the chest X-ray view position with use of convolutional neural networks to improve meta-information for data cleaning within a hospital data infrastructure. Within this paper we developed a convolutional neural network which automatically detects the anteroposterior and posteroanterior view position of a chest radiograph. We trained two different network architectures (VGG variant and ResNet-34) with data published by the RSNA (26 684 radiographs, class distribution 46 % AP, 54 % PA) and validated these on a self-compiled dataset with data from the University Hospital Essen (4507, radiographs, class distribution 55 % PA, 45 % AP) labeled by a human reader. For visualization and better understanding of the network predictions, a Grad-CAM was generated for each network decision. The network results were evaluated based on the accuracy, the area under the curve (AUC), and the F1-score against the human reader labels. Also a final performance comparison between model predictions and DICOM labels was performed. The ensemble models reached accuracy and F1-scores greater than 95 %. The AUC reaches more than 0.99 for the ensemble models. The Grad-CAMs provide insight as to which anatomical structures contributed to a decision by the networks which are comparable with the ones a radiologist would use. Furthermore, the trained models were able to generalize over mislabeled examples, which was found by comparing the human reader labels to the predicted labels as well as the DICOM labels. The results show that certain incorrectly entered meta-information of radiological images can be effectively corrected by deep learning in order to increase data quality in clinical application as well as in research. · The predictions for both view positions are accurate with respect to external validation data.. · The networks based their decisions on anatomical structures and key points that were in-line with prior knowledge and human understanding.. · Final models were able to detect labeling errors within the test dataset..· Hosch R, Kroll L, Nensa F et al. Differentiation Between Anteroposterior and Posteroanterior Chest X-Ray View Position With Convolutional Neural Networks. Fortschr Röntgenstr 2021; 193: 168 - 176.ZIEL: Detektion der Röntgen-Thorax-Aufnahmeposition anhand von Convolutional Neural Networks zur Verbesserung und Bereinigung von Metainformationen innerhalb der Dateninfrastruktur eines Krankenhauses. Innerhalb dieser Studie wurde ein Convolutional Neural Network entwickelt, das automatisch die verwendete Anterior-posterior- bzw. Posterior-anterior-Aufnahmeprojektion einer Röntgen-Thoraxaufnahme erkennt. Es wurden 2 unterschiedliche Netzwerkarchitekturen (VGG Variante und ResNet-34) auf Basis von Daten der RSNA (26 684 Röntgenaufnahmen, Klassenverteilung: 46 % AP, 54 % PA) trainiert und anschließend auf einem zusammengestellten hauseigenen Datensatz (Verwendung von manuellen Labeln) aus dem Datenbestand des Universitätsklinikums Essen (4507 Röntgenaufnahmen, Klassenverteilung: 55 % PA, 45 % AP) getestet. Für eine bessere Nachvollziehbarkeit der getätigten Vorhersagen der Modelle wurde zudem für jede Vorhersage eine Grad-CAM generiert. Die Resultate der Modelle wurden anhand der Accuracy, der Area under the Curve (AUC) und dem F1-Score berechnet auf Basis des Abgleichs der manuellen Label. Abschließend wurde zudem die Genauigkeit der Modellvorhersagen und der DICOM-Label anhand des Vergleichs mit den manuellen Labeln berechnet. Die zusammengefassten Modelle erreichten Accuracy- und F1-Score-Werte von mehr als 95 %. Alle Modelle erreichten eine AUC von über 0,99. Die generierten Grad-CAMSs zeigen, dass die Modelle relevante anatomische Referenzpunkte für ihre Vorhersage nutzen, die auch ein Radiologe für eine Unterscheidung heranziehen würde. Zudem zeigen die antrainierten Modelle die Fähigkeit zur Generalisierung, da diese auch falsch gekennzeichnete Röntgenbilder richtig einordnen können, was durch den Vergleich der manuellen Label mit den jeweiligen Modellvorhersagen und den DICOM-Labeln ersichtlich wurde. Die Resultate zeigen, dass falsch eingetragene Metainformationen innerhalb der radiologischen Bildgebung effektiv durch den Einsatz von Deep Learning korrigiert und somit die Datenqualität sowohl für die klinische Anwendung als auch für die Forschung erhöht werden können. · Die trainierten Modelle erzielen akkurate Vorhersagen auf externen Validierungsdaten.. · Die Netzwerke treffen ihre Vorhersagen basierend auf anatomischen Strukturen und Referenzpunkten, die mit dem menschlichen Fachwissen übereinstimmen.. · Die finalen Modelle konnten Label-Fehler in dem Testdatensatz finden..

SummaryObjectives: This paper addresses the problem of decision-making in relation to the administration of noninvasive mechanical ventila tion (NIMV) in intensive care units.Methods: Data mining methods were employed to find out the factors influencing the success/failure of NIMV and to predict its results in future patients. These artificial intelligence-based methods have not been applied in this field in spite of the good results obtained in other medical areas.Results: Feature selection methods provided the most influential variables in the success/ failure of NIMV, such as NIMV hours, PaCO2 at the start, PaO2 / FiO2 ratio at the start, hematocrit at the start or PaO2 / FiO2 ratio after two hours. These methods were also used in the preprocessing step with the aim of improving the results of the classifiers. The algorithms provided the best results when the dataset used as input was the one containing the attributes selected with the CFS method. Conclusions: Data mining methods can be successfully applied to determine the most influential factors in the success/failure of NIMV and also to predict NIMV results in future patients. The results provided by classifiers can be improved by preprocessing the data with feature selection techniques.

Different ensemble strategies were compared in online near-infrared models for monitoring active pharmaceutical ingredients of Traditional Chinese Medicine. Bagging partial least square regression and boosting partial least square regression were adopted to near-infrared models, to determine hesperidin and nobiletin content during the extraction process of Pericarpium Citri Reticulatae in a pilot scale system. Different pretreatment methods were investigated, including Savitzky-Golay smoothing, derivatives, multiplicative scatter correction, standard normal variate, normalize, and combinations of them. Two different variable selection methods, including synergy interval partial least squares and backward interval partial least squares algorithms, were performed. Based on the result of the synergy interval partial least squares algorithm, bagging partial least square regression and boosting partial least square regression were adopted into the quantitative analysis. The results demonstrated that the established approach could be applied for rapid determination and real-time monitoring of hesperidin and nobiletin in Pericarpium Citri Reticulatae (Citrus reticulata) during the extraction process. Comparing the results, the boosting partial least square regression provided a slightly better accuracy than the bagging partial least square regression. Finally, this paper provides a promising ensemble strategy on online near-infrared models in Chinese medicine.

SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Biosignal Interpretation: Advanced Methods for Neural Signals and Images”.Background: Dynamic Causal Modelling (DCM) is a generic formalism to study effective brain connectivity based on neuroimaging data, particularly functional Magnetic Resonance Imaging (fMRI). Recently, there have been attempts at modifying this model to allow for stochastic disturbances in the states of the model.Objectives: This paper proposes the Multiple- Model Kalman Filtering (MMKF) technique as a stochastic identification model discriminating among different hypothetical connectivity structures in the DCM framework; moreover, the performance compared to a similar de terministic identification model is assessed.Methods: The integration of the stochastic DCM equations is first presented, and a MMKF algorithm is then developed to perform model selection based on these equations. Monte Carlo simulations are performed in order to investigate the ability of MMKF to distinguish between different connectivity structures and to estimate hidden states under both deterministic and stochastic DCM.Results: The simulations show that the proposed MMKF algorithm was able to successfully select the correct connectivity model structure from a set of pre-specified plausible alternatives. Moreover, the stochastic approach by MMKF was more effective compared to its deterministic counterpart, both in the selection of the correct connectivity structure and in the estimation of the hidden states.Conclusions: These results demonstrate the applicability of a MMKF approach to the study of effective brain connectivity using DCM, particularly when a stochastic formulation is desirable.

SummaryObjectives: To develop an adaptive approach to mine frequent semantic tags (FSTs) from heterogeneous clinical research texts.Methods: We develop a “plug-n-play” framework that integrates replaceable un-supervised kernel algorithms with formatting, functional, and utility wrappers for FST mining. Temporal information identification and semantic equivalence detection were two example functional wrappers. We first compared this approach’s recall and efficiency for mining FSTs from ClinicalTrials.gov to that of a recently published tag-mining algorithm. Then we assessed this approach’s adaptability to two other types of clinical research texts: clinical data requests and clinical trial protocols, by comparing the prevalence trends of FSTs across three texts.Results: Our approach increased the average recall and speed by 12.8% and 47.02% respectively upon the baseline when mining FSTs from ClinicalTrials.gov, and maintained an overlap in relevant FSTs with the baseline ranging between 76.9% and 100% for varying FST frequency thresholds. The FSTs saturated when the data size reached 200 documents. Consistent trends in the prevalence of FST were observed across the three texts as the data size or frequency threshold changed.Conclusions: This paper contributes an adaptive tag-mining framework that is scalable and adaptable without sacrificing its recall. This component-based architectural design can be potentially generalizable to improve the adaptability of other clinical text mining methods.

SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Big Data and Analytics in Healthcare”.Objectives: This paper describes the scale-up efforts at the VA Salt Lake City Health Care System to address processing large corpora of clinical notes through a natural language processing (NLP) pipeline. The use case described is a current project focused on detecting the presence of an indwelling uri-nary catheter in hospitalized patients and subsequent catheter-associated urinary tract infections.Methods: An NLP algorithm using v3NLP was developed to detect the presence of an indwelling urinary catheter in hospitalized patients. The algorithm was tested on a small corpus of notes on patients for whom the presence or absence of a catheter was already known (reference standard). In planning for a scale-up, we estimated that the original algorithm would have taken 2.4 days to run on a larger corpus of notes for this project (550,000 notes), and 27 days for a corpus of 6 million records representative of a national sample of notes. We approached scaling-up NLP pipelines through three techniques: pipeline replication via multi-threading, intra-annotator threading for tasks that can be further decomposed, and remote annotator services which enable annotator scale-out.Results: The scale-up resulted in reducing the average time to process a record from 206 milliseconds to 17 milliseconds or a 12-fold increase in performance when applied to a corpus of 550,000 notes.Conclusions: Purposely simplistic in nature, these scale-up efforts are the straight forward evolution from small scale NLP processing to larger scale extraction without incurring associated complexities that are inherited by the use of the underlying UIMA framework. These efforts represent generalizable and widely applicable techniques that will aid other computationally complex NLP pipelines that are of need to be scaled out for processing and analyzing big data.

SummaryBackground: An aging population and people’s higher survival to diseases and traumas that leave physical consequences are challenging aspects in the context of an efficient health management. This is why telerehabilitation systems are being developed, to allow monitoring and support of physiotherapy sessions at home, which could reduce healthcare costs while also improving the quality of life of the users.Objectives: Our goal is the development of a Kinect-based algorithm that provides a very accurate real-time monitoring of physical rehabilitation exercises and that also provides a friendly interface oriented both to users and physiotherapists.Methods: The two main constituents of our algorithm are the posture classification method and the exercises recognition method. The exercises consist of series of movements. Each movement is composed of an initial posture, a final posture and the angular trajectories of the limbs involved in the movement. The algorithm was designed and tested with datasets of real movements performed by volunteers. We also explain in the paper how we obtained the optimal values for the trade-off values for posture and trajectory recognition.Results: Two relevant aspects of the algorithm were evaluated in our tests, classification accuracy and real-time data processing. We achieved 91.9% accuracy in posture classification and 93.75% accuracy in trajectory recognition. We also checked whether the algorithm was able to process the data in real-time. We found that our algorithm could process more than 20,000 postures per second and all the required trajectory data-series in real-time, which in practice guarantees no perceptible delays. Later on, we carried out two clinical trials with real patients that suffered shoulder disorders. We obtained an exercise monitoring accuracy of 95.16%.Conclusions: We present an exercise recognition algorithm that handles the data provided by Kinect efficiently. The algorithm has been validated in a real scenario where we have verified its suitability. Moreover, we have received a positive feedback from both users and the physiotherapists who took part in the tests.