OBJECTIVE: This study aimed to develop and evaluate a fully automated method for visualizing and measuring tooth wear progression using pairs of intraoral scans (IOSs) in comparison with a manual protocol.
METHODS: Eight patients with severe tooth wear progression were retrospectively included, with IOSs taken at baseline and 1-year, 3-year, and 5-year follow-ups. For alignment, the automated method segmented the arch into separate teeth in the IOSs. Tooth pair registration selected tooth surfaces that were likely unaffected by tooth wear and performed point set registration on the selected surfaces. Maximum tooth profile losses from baseline to each follow-up were determined based on signed distances using the manual 3D Wear Analysis (3DWA) protocol and the automated method. The automated method was evaluated against the 3DWA protocol by comparing tooth segmentations with the Dice-Sørensen coefficient (DSC) and intersection over union (IoU). The tooth profile loss measurements were compared with regression and Bland-Altman plots. Additionally, the relationship between the time interval and the measurement differences between the two methods was shown.
RESULTS: The automated method completed within two minutes. It was very effective for tooth instance segmentation (826 teeth, DSC = 0.947, IoU = 0.907), and a correlation of 0.932 was observed for agreement on tooth profile loss measurements (516 tooth pairs, mean difference = 0.021mm, 95% confidence interval = [-0.085, 0.138]). The variability in measurement differences increased for larger time intervals.
CONCLUSIONS: The proposed automated method for monitoring tooth wear progression was faster and not clinically significantly different in accuracy compared to a manual protocol for full-arch IOSs.
CONCLUSIONS: General practitioners and patients can benefit from the visualization of tooth wear, allowing quantifiable and standardized decisions concerning therapy requirements of worn teeth. The proposed method for tooth wear monitoring decreased the time required to less than two minutes compared with the manual approach, which took at least two hours.

UNASSIGNED: Tertiary and quaternary (TQ) care refers to complex cases requiring highly specialized health services. Our study aimed to compare the ability of a natural language processing (NLP) model to an existing human workflow in predictively identifying TQ cases for transfer requests to an academic health center.
UNASSIGNED: Data on interhospital transfers were queried from the electronic health record for the 6-month period from July 1, 2020 to December 31, 2020. The NLP model was allowed to generate predictions on the same cases as the human predictive workflow during the study period. These predictions were then retrospectively compared to the true TQ outcomes.
UNASSIGNED: There were 1895 transfer cases labeled by both the human predictive workflow and the NLP model, all of which had retrospective confirmation of the true TQ label. The NLP model receiver operating characteristic curve had an area under the curve of 0.91. Using a model probability threshold of ≥0.3 to be considered TQ positive, accuracy was 81.5% for the NLP model versus 80.3% for the human predictions (P = .198) while sensitivity was 83.6% versus 67.7% (P<.001).
UNASSIGNED: The NLP model was as accurate as the human workflow but significantly more sensitive. This translated to 15.9% more TQ cases identified by the NLP model.
UNASSIGNED: Integrating an NLP model into existing workflows as automated decision support could translate to more TQ cases identified at the onset of the transfer process.

The advance of high-performance computing (HPC), high-performance data analytics (HPDA) and AI and their synergetic integration into workflows has revolutionized numerous industries, amongst others the medical and pharmaceutical sectors. In this special section of Technology and Health Care, we delve into the remarkable advancements and potential of HPC, HPDA and AI (together termed HPC+) in driving innovation, improving patient outcomes, and accelerating drug discovery. The articles in this issue shed light onto the potential of HPC+ in addressing several critical areas, including medical imaging, personalized medicine, drug discovery, and clinical as well as political decision support.

The knowledge transformation process involves the guideline for the diagnosis and therapy of epilepsy to an executable and computable knowledge base that serves as the basis for a decision-support system. We present a transparent knowledge representation model which facilitates technical implementation and verification. Knowledge is represented in a plain table, used in the frontend code of the software where simple reasoning is performed. The simple structure is sufficient and comprehensible also for non-technical persons (i.e., clinicians).

UNASSIGNED: The main complications of polypharmacy, which is known as the simultaneous use of more than five drugs, are potentially inappropriate medicines(PIMs), drug-drug, and drug-disease interaction. It is aimed to prepare an auxiliary tool to reduce the complications of polypharmacy and to support rational drug use(RDU), by evaluating the patient with age, drugs, and chronic diseases in this study.
UNASSIGNED: In the first phase of this study, as methodological research, an up-to-date and comprehensive auxiliary tool as a reference method was generated with a database containing interaction information of 430 most commonly used drug agents and chronic diseases in geriatrics in the light of current and valid 6 PIM criteria for geriatric patients, and medication prospectuses, relevant current articles, and guidelines. Then, an artificial intelligence(AI) supported web application was designed and developed to facilitate the practical use of the tool. Afterward, the data of a cross-sectional observational single-center study were used for the rate and time of PIM and drug interaction detection with the web application. The proposed web application is publicly available at https://fastrational.com/.
UNASSIGNED: While the PIM coverage rate with the proposed tool was 75.3%, the PIM coverage rate of EU(7)-PIM, US-FORTA, TIME-to-STOPP, Beers 2019, STOPP, Priscus criteria in the web application database respectively(63.5%-19.5%) from the highest to the lowest. The proposed tool includes all PIMs, drug-drug, and drug-disease interaction information detected with other criteria. A general practitioner detects interactions for a patient without the web application in 2278 s on average, while the time with the web application is decreased to 33.8 s on average, and this situation is statistically significant.
UNASSIGNED: In the literature and this study, the PIM criteria alone are insufficient to include actively used medicines and it shows heterogeneity. In addition, many studies showed that the biggest obstacle to drug regulation in practice is \"time constraints.\" The proposed comprehensive auxiliary tool analyzes age, drugs, and diseases specifically for the patient 60 times faster than the manual method, and it provides quick access to the relevant references, and ultimately supports RDU for the clinician, with the first and only AI-supported web application.

Clinical practice guidelines are statements which are based on the best available evidence, and their goal is to improve the quality of patient care. Integrating clinical practice guidelines into computer systems can help physicians reduce medical errors and help them to have the best possible practice. Guideline-based clinical decision support systems play a significant role in supporting physicians in their decisions. Meantime, system errors are the most critical concerns in designing decision support systems that can affect their performance and efficacy. A well-developed ontology can be helpful in this matter. The proposed systematic review will specify the methods, components, language of rules, and evaluation methods of current ontology-driven guideline-based clinical decision support systems.
This review will identify literature through searching MEDLINE (via Ovid), PubMed, EMBASE, Cochrane Library, CINAHL, ScienceDirect, IEEEXplore, and ACM Digital Library. Gray literature, reference lists, and citing articles of the included studies will be searched. The quality of the included studies will be assessed by the mixed methods appraisal tool (MMAT-version 2018). At least two independent reviewers will perform the screening, quality assessment, and data extraction. A third reviewer will resolve any disagreements. Proper data analysis will be performed based on the type of system and ontology engineering evaluation data.
The study will provide evidence regarding applying ontologies in guideline-based clinical decision support systems. The findings of this systematic review will be a guide for decision support system designers and developers, technologists, system providers, policymakers, and stakeholders. Ontology builders can use the information in this review to build well-structured ontologies for personalized medicine.
PROSPERO CRD42018106501.

OBJECTIVE: The primary purpose of this study was to develop a new machine learning model for the surgery/non-surgery decision in class III patients and evaluate the validity and reliability of this model.
METHODS: The sample consisted of 196 skeletal class III patients. All the cases were allocated randomly, 136 to the training set and the remaining 60 to the test set. Using the test set, the success rate of the artificial neural network model was estimated, along with a 95% confidence interval. To predict surgical cases, we trained a binary classifier using two different methods: random forest (RF) and logistic regression (LR).
RESULTS: Both the RF and the LR model showed high separability when classifying each patient for surgical or non-surgical treatment. RF achieved an area under the curve (AUC) of 0.9395 on the test set. 95% confidence intervals were computed by bootstrap sampling as lower bound = 0.7908 and higher bound = 0.9799. On the other hand, LR achieved an AUC of 0.937 on the test set. 95% confidence intervals were computed by bootstrap sampling as lower bound = 0.8467 and higher bound = 0.9812.
CONCLUSIONS: RF and LR machine learning models can be used to generate accurate and reliable algorithms to successfully classify patients up to 90%. The features selected by the algorithms coincide with the clinical features that we as clinicians weigh heavily when determining a treatment plan. This study further supports that overjet, Wits appraisal, lower incisor angulation, and Holdaway H angle can be used as strong predictors in assessing a patient\'s surgical needs.
UNASSIGNED: ZIELSETZUNG: Primäres Ziel dieser Studie war es, ein neues Machine-Learning-Modell für die Entscheidung Operation vs. nichtoperative Behandlung bei Klasse-III-Patienten zu entwickeln und die Validität und Reliabilität dieses Modells zu bewerten.
METHODS: Die Stichprobe bestand aus 196 Patienten der skelettalen Klasse III. Alle Fälle wurden randomisiert einer Gruppe zugewiesen, 136 der Trainingsgruppe und die übrigen 60 der Testgruppe. Anhand des Testsatzes wurde die Erfolgsquote des künstlichen neuronalen Netzes mit einem Konfidenzintervall von 95% abgeschätzt. Zur Prädiktion chirurgischer Fälle wurde ein binärer Klassifikator mit 2 unterschiedlichen Methoden trainiert: Random Forest (RF) und logistische Regression (LR).
UNASSIGNED: Sowohl das RF- als auch das LR-Modell zeigten eine hohe Trennschärfe bei der Klassifizierung der einzelnen Patienten für eine chirurgische bzw. eine nichtchirurgische Behandlung. RF erreichte eine AUC („area under the curve“) von 0,9395 in der Testgruppe. Die 95%-Konfidenzintervalle wurden mittels Bootstrap-Stichproben als untere Grenze = 0,7908 und obere Grenze = 0,9799 berechnet. Andererseits erreichte LR eine AUC von 0,937 in der Testgruppe. Die 95%-Konfidenzintervalle wurden durch Bootstrap-Sampling als untere Grenze = 0,8467 und obere Grenze = 0,9812 berechnet.
UNASSIGNED: Mithilfe von RF- und LR-Modellen für maschinelles Lernen lassen sich genaue und zuverlässige Algorithmen erstellen, die Patienten in bis zu 90% der Fälle erfolgreich klassifizieren. Die von den Algorithmen ausgewählten Merkmale stimmen mit den klinischen Merkmalen überein, die wir als Kliniker bei der Festlegung eines Behandlungsplans stark gewichten. Diese Studie belegt außerdem, dass Overjet, Wits-Appraisal, die Angulation der unteren Inzisiven und der Holdaway-H-Winkel als starke Prädiktoren für die Beurteilung des Operationsbedarfs eines Patienten verwendet werden können.

OBJECTIVE: Rising patient numbers, with increasing complexity, challenge the sustainability of the current preoperative process. We evaluated whether an electronic screening application can distinguish patients that need a preoperative consultation from low-risk patients that can be first seen on the day of surgery.
METHODS: Prospective cohort study.
METHODS: Preoperative clinic of a tertiary academic hospital.
METHODS: 1395 adult patients scheduled for surgery or procedural sedation.
METHODS: We assessed a novel electronic preoperative screening application which consists of a questionnaire with a maximum of 185 questions regarding the patient\'s medical history and current state of health. The application provides an extensive health report, including an American Society of Anesthesiologists physical status (ASA-PS) classification and a recommendation for either consultation by an anesthesiologist at the preoperative clinic or approval for screening on the day of surgery.
METHODS: The recommendation of the electronic screening system was compared with the regular preoperative assessment using measures of diagnostic accuracy and agreement. Secondary outcomes included ASA-PS classification, patient satisfaction, and the anesthesiologists\' opinion on the completeness and quality of the screening report.
RESULTS: Sensitivity to detect patients who needed additional consultation was 97.5% (95%CI 91.2-99.7) and the negative likelihood ratio was 0.08 (95%CI 0.02-0.32). 407 (29.2%) patients were approved for surgery by both electronic screening and anesthesiologist. In 909 (65.2%) cases, the electronic screening system recommended further consultation while the anesthesiologist approved the patient (specificity 30.9% (95%CI 28.4-33.5); poor level of agreement (ĸ = 0.04)). Agreement regarding ASA-PS classification scores was weak (ĸ = 0.48). The majority of patients (78.0%) felt positive about electronic screening replacing the regular preoperative assessment.
CONCLUSIONS: Electronic screening can reliably identify patients who can have their first contact with an anesthesiologist on the day of surgery, potentially allowing a major proportion of patients to safely bypass the preoperative clinic.

Structured Abstract-Objective: Abnormal elevation of intracranial pressure (ICP) can cause dangerous or even fatal outcomes. The early detection of high intracranial pressure events can be crucial in saving lives in an intensive care unit (ICU). Despite many applications of machine learning (ML) techniques related to clinical diagnosis, ML applications for continuous ICP detection or short-term predictions have been rarely reported. This study proposes an efficient method of applying an artificial recurrent neural network on the early prediction of ICP evaluation continuously for TBI patients. Methods: After ICP data preprocessing, the learning model is generated for thirteen patients to continuously predict the ICP signal occurrence and classify events for the upcoming 10 minutes by inputting the previous 20-minutes of the ICP signal. Results: As the overall model performance, the average accuracy is 94.62%, the average sensitivity is 74.91%, the average specificity is 94.83%, and the average root mean square error is approximately 2.18 mmHg. Conclusion: This research addresses a significant clinical problem with the management of traumatic brain injury patients. The machine learning model data enables early prediction of ICP continuously in a real-time fashion, which is crucial for appropriate clinical interventions. The results show that our machine learning-based model has high adaptive performance, accuracy, and efficiency.

BACKGROUND: Clinical decision support systems often adopt and operationalize existing clinical practice guidelines leading to higher guideline availability, increased guideline adherence, and data integration. Most of these systems use an internal state-based model of a clinical practice guideline to derive recommendations but do not provide the user with comprehensive insight into the model.
OBJECTIVE: Here we present a novel approach based on dynamic guideline visualization that incorporates the individual patient\'s current treatment context.
METHODS: We derived multiple requirements to be fulfilled by such an enhanced guideline visualization. Using business process and model notation as the representation format for computer-interpretable guidelines, a combination of graph-based representation and logical inferences is adopted for guideline processing. A context-specific guideline visualization is inferred using a business rules engine.
RESULTS: We implemented and piloted an algorithmic approach for guideline interpretation and processing. As a result of this interpretation, a context-specific guideline is derived and visualized. Our implementation can be used as a software library but also provides a representational state transfer interface. Spring, Camunda, and Drools served as the main frameworks for implementation. A formative usability evaluation of a demonstrator tool that uses the visualization yielded high acceptance among clinicians.
CONCLUSIONS: The novel guideline processing and visualization concept proved to be technically feasible. The approach addresses known problems of guideline-based clinical decision support systems. Further research is necessary to evaluate the applicability of the approach in specific medical use cases.