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Adversarial attack, Human pose estimation, White-box attack, Imperceptibility, Local perturbation, Drawing. Design. Illustration, NC1-1940, Computer applications to medicine. Medical informatics, R858-859.7, Computer software, and QA76.75-76.765

Abstract Deep neural networks are vulnerable to attacks from adversarial inputs. Corresponding attack research on human pose estimation (HPE), particularly for body joint detection, has been largely unexplored. Transferring classification-based attack methods to body joint regression tasks is not straightforward. Another issue is that the attack effectiveness and imperceptibility contradict each other. To solve these issues, we propose local imperceptible attacks on HPE networks. In particular, we reformulate imperceptible attacks on body joint regression into a constrained maximum allowable attack. Furthermore, we approximate the solution using iterative gradient-based strength refinement and greedy-based pixel selection. Our method crafts effective perceptual adversarial attacks that consider both human perception and attack effectiveness. We conducted a series of imperceptible attacks against state-of-the-art HPE methods, including HigherHRNet, DEKR, and ViTPose. The experimental results demonstrate that the proposed method achieves excellent imperceptibility while maintaining attack effectiveness by significantly reducing the number of perturbed pixels. Approximately 4% of the pixels can achieve sufficient attacks on HPE.

Knowledge graph, Fact prediction, Reinforcement learning, Entity heterogeneity, Postwalking mechanism, Drawing. Design. Illustration, NC1-1940, Computer applications to medicine. Medical informatics, R858-859.7, Computer software, and QA76.75-76.765

Abstract Knowledge graph (KG) fact prediction aims to complete a KG by determining the truthfulness of predicted triples. Reinforcement learning (RL)-based approaches have been widely used for fact prediction. However, the existing approaches largely suffer from unreliable calculations on rule confidences owing to a limited number of obtained reasoning paths, thereby resulting in unreliable decisions on prediction triples. Hence, we propose a new RL-based approach named EvoPath in this study. EvoPath features a new reward mechanism based on entity heterogeneity, facilitating an agent to obtain effective reasoning paths during random walks. EvoPath also incorporates a new postwalking mechanism to leverage easily overlooked but valuable reasoning paths during RL. Both mechanisms provide sufficient reasoning paths to facilitate the reliable calculations of rule confidences, enabling EvoPath to make precise judgments about the truthfulness of prediction triples. Experiments demonstrate that EvoPath can achieve more accurate fact predictions than existing approaches.

Per-patient model, Interpretability, Quantization, Automatic preprocessing, Inflammatory bowel disease, Computer applications to medicine. Medical informatics, R858-859.7, Analysis, and QA299.6-433

Abstract Background Discrimination between patients affected by inflammatory bowel diseases and healthy controls on the basis of endoscopic imaging is an challenging problem for machine learning models. Such task is used here as the testbed for a novel deep learning classification pipeline, powered by a set of solutions enhancing characterising elements such as reproducibility, interpretability, reduced computational workload, bias-free modeling and careful image preprocessing. Results First, an automatic preprocessing procedure is devised, aimed to remove artifacts from clinical data, feeding then the resulting images to an aggregated per-patient model to mimic the clinicians decision process. The predictions are based on multiple snapshots obtained through resampling, reducing the risk of misleading outcomes by removing the low confidence predictions. Each patient’s outcome is explained by returning the images the prediction is based upon, supporting clinicians in verifying diagnoses without the need for evaluating the full set of endoscopic images. As a major theoretical contribution, quantization is employed to reduce the complexity and the computational cost of the model, allowing its deployment on small power devices with an almost negligible 3% performance degradation. Such quantization procedure holds relevance not only in the context of per-patient models but also for assessing its feasibility in providing real-time support to clinicians even in low-resources environments. The pipeline is demonstrated on a private dataset of endoscopic images of 758 IBD patients and 601 healthy controls, achieving Matthews Correlation Coefficient 0.9 as top performance on test set. Conclusion We highlighted how a comprehensive pre-processing pipeline plays a crucial role in identifying and removing artifacts from data, solving one of the principal challenges encountered when working with clinical data. Furthermore, we constructively showed how it is possible to emulate clinicians decision process and how it offers significant advantages, particularly in terms of explainability and trust within the healthcare context. Last but not least, we proved that quantization can be a useful tool to reduce the time and resources consumption with an acceptable degradation of the model performs. The quantization study proposed in this work points up the potential development of real-time quantized algorithms as valuable tools to support clinicians during endoscopy procedures.

Social network analysis, Ballet collaboration, Collaboration network, Gender imbalance, Perception error, Computer applications to medicine. Medical informatics, and R858-859.7

Abstract Ballet, a mainstream performing art predominantly associated with women, exhibits significant gender imbalances in leading positions. However, the collaboration’s structural composition vis-à-vis gender representation in the field remains unexplored. Our study investigates the gendered labor force composition and collaboration patterns in ballet creations. Our findings reveal gender disparities in ballet creations aligned with gendered collaboration patterns and women’s occupation of more peripheral network positions than men. Productivity disparities show women accessing 20–25% of ballet creations compared to men. Mathematically derived perception errors show the underestimation of women artists’ representation within ballet collaboration networks, potentially impacting women’s careers in the field. Our study highlights the structural imbalances that women face in ballet creations and emphasizes the need for a more inclusive and equal professional environment in the ballet industry. These insights contribute to a broader understanding of structural gender imbalances in artistic domains and can inform cultural organizations about potential affirmative actions toward a better representation of women leaders in ballet.

Telehealth, Intelligent triage, Character embedding, Bidirectional LSTM, Computer applications to medicine. Medical informatics, and R858-859.7

Abstract Background The widespread adoption of telehealth services necessitates accurate online department selection based on patient medical records, a task requiring significant medical knowledge. Incorrect triage results in considerable time wastage for both patients and medical professionals. To address this, we propose an intelligent triage model based on a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network with character embedding to enhance the efficiency and capacity of telehealth services. Methods We gathered a 1.3 GB medical dataset comprising 200,000 records, each including medical history, physical examination data, and other pertinent information found on the electronic medical record homepage. Following data preprocessing, a clinical corpus was established to train character embeddings with a medical context. These character embeddings were then utilized to extract features from patient chief complaints, and a 2-layer Bi-LSTM neural network was trained to categorize these complaints, enabling intelligent triage for telehealth services. Results 60,000 chief complaint-department data pairs were extracted from clinical corpus and divided into the training, validation, and test sets of 42,000, 9,000, and 9,000, respectively. The character embedding based Bi-LSTM neural network achieved a macro-precision of 85.50% and an F1 score of 85.45%. Conclusion The telehealth triage model developed in this study demonstrates strong implementation outcomes and significantly improves the efficiency and capacity of telehealth services. Character embedding outperforms word embedding, and future work will incorporate additional features such as patient age and gender into the chief complaint feature to future enhance model performance.

Chronic heart failure, Mortality, Machine learning, Random forest, Permutation importance, SHAP value, Computer applications to medicine. Medical informatics, and R858-859.7

Abstract Background The goal of this study was to assess the effectiveness of machine learning models and create an interpretable machine learning model that adequately explained 3-year all-cause mortality in patients with chronic heart failure. Methods The data in this paper were selected from patients with chronic heart failure who were hospitalized at the First Affiliated Hospital of Kunming Medical University, from 2017 to 2019 with cardiac function class III-IV. The dataset was explored using six different machine learning models, including logistic regression, naive Bayes, random forest classifier, extreme gradient boost, K-nearest neighbor, and decision tree. Finally, interpretable methods based on machine learning, such as SHAP value, permutation importance, and partial dependence plots, were used to estimate the 3-year all-cause mortality risk and produce individual interpretations of the model's conclusions. Result In this paper, random forest was identified as the optimal aools lgorithm for this dataset. We also incorporated relevant machine learning interpretable tand techniques to improve disease prognosis, including permutation importance, PDP plots and SHAP values for analysis. From this study, we can see that the number of hospitalizations, age, glomerular filtration rate, BNP, NYHA cardiac function classification, lymphocyte absolute value, serum albumin, hemoglobin, total cholesterol, pulmonary artery systolic pressure and so on were important for providing an optimal risk assessment and were important predictive factors of chronic heart failure. Conclusion The machine learning-based cardiovascular risk models could be used to accurately assess and stratify the 3-year risk of all-cause mortality among CHF patients. Machine learning in combination with permutation importance, PDP plots, and the SHAP value could offer a clear explanation of individual risk prediction and give doctors an intuitive knowledge of the functions of important model components.

Hip replacement, Home-based rehabilitation, Machine learning, Older individual, Self-reported outcome, Computer applications to medicine. Medical informatics, and R858-859.7

Abstract Background With the aging of the population, the number of total hip replacement surgeries is increasing globally. Hip replacement has undergone revolutionary advancements in surgical methods and materials. Due to the short length of hospitalization, rehabilitation care is mainly home-based. The needs and concerns about such home-based rehabilitation are constantly changing, requiring continuous attention. Objective To explore effective methods for comprehensively identifying older patients’ self-reported outcomes after home-based rehabilitation for hip replacement, in order to develop appropriate intervention strategies for patient rehabilitation care in the future. Methods This study constructed a corpus of patients’ self-reported rehabilitation care problems after hip replacement, based on the Omaha classification system. This study used the Python development language and implemented artificial intelligence to match the corpus data on the cooperation platform, to identify the main health-related problems reported by the patients, and to perform statistical analyses. Results Most patients had physical health-related problems. More than 80% of these problems were related to neuromusculoskeletal function, interpersonal relationships, pain, health care supervision, physical activity, vision, nutrition, and residential environment. The most common period in which patients’ self-reported problems arose was 6 months post-surgery. The relevant labels that were moderately related to these problems were: Physiology-Speech and Language and Physiology-Mind (r = 0.45), Health-Related Behaviors-Nutrition and Health-Related Behaviors-Compliance with Doctors’ Prescription (r = 0.40). Conclusion Physiological issues remain the main health-related issues for home-based rehabilitation after hip replacement in older patients. Precision care has become an important principle of rehabilitation care. This study used a machine learning method to obtain the largest quantitative network data possible. The artificial intelligence capture was fully automated, which greatly improved efficiency, as compared to manual data entering.

Acute kidney injuries, Prediction model, Machine learning, Coronary artery bypass grafting, Computer applications to medicine. Medical informatics, and R858-859.7

Abstract Background Acute kidney injury (AKI) after coronary artery bypass grafting (CABG) surgery is associated with poor outcomes. The objective of this study was to apply a new machine learning (ML) method to establish prediction models of AKI after CABG. Methods A total of 2,780 patients from two medical centers in East China who underwent primary isolated CABG were enrolled. The dataset was randomly divided for model training (80%) and model testing (20%). Four ML models based on LightGBM, Support vector machine (SVM), Softmax and random forest (RF) algorithms respectively were established in Python. A total of 2,051 patients from two other medical centers were assigned to an external validation group to verify the performances of the ML prediction models. The models were evaluated using the area under the receiver operating characteristics curve (AUC), Hosmer-Lemeshow goodness-of-fit statistic, Bland-Altman plots, and decision curve analysis. The outcome of the LightGBM model was interpreted using SHapley Additive exPlanations (SHAP). Results The incidence of postoperative AKI in the modeling group was 13.4%. Similarly, the incidence of postoperative AKI of the two medical centers in the external validation group was 8.2% and 13.6% respectively. LightGBM performed the best in predicting, with an AUC of 0.8027 in internal validation group and 0.8798 and 0.7801 in the external validation group. The SHAP revealed the top 20 predictors of postoperative AKI ranked according to the importance, and the top three features on prediction were the serum creatinine in the first 24 h after operation, the last preoperative Scr level, and body surface area. Conclusion This study provides a LightGBM predictive model that can make accurate predictions for AKI after CABG surgery. The LightGBM model shows good predictive ability in both internal and external validation. It can help cardiac surgeons identify high-risk patients who may experience AKI after CABG surgery.

Gastric cancer, Epidemiology, Social and environmental factors, Risk areas, Spatial analysis, Computer applications to medicine. Medical informatics, and R858-859.7

Abstract Background Both incidence and mortality of gastric cancer in Gansu rank first in china, this study aimed to describe the recent prevalence of gastric cancer and explore the social and environmental determinants of gastric cancer in Gansu Province. Methods The incidence of gastric cancer in each city of Gansu Province was calculated by utilizing clinical data from patients with gastric cancer (2013–2021) sourced from the medical big data platform of the Gansu Province Health Commission, and demographic data provided by the Gansu Province Bureau of Statistics. Subsequently, we conducted joinpoint regression analysis, spatial auto-correlation analysis, space–time scanning analysis, as well as an exploration into the correlation between social and environmental factors and GC incidence in Gansu Province with Joinpoint_5.0, ArcGIS_10.8, GeoDa, SaTScanTM_10.1.1 and GeoDetector_2018. Results A total of 75,522 cases of gastric cancer were included in this study. Our findings suggested a significant upward trend in the incidence of gastric cancer over the past nine years. Notably, Wuwei, Zhangye and Jinchang had the highest incidence rates while Longnan, Qingyang and Jiayuguan had the lowest. In spatial analysis, we have identified significant high-high cluster areas and delineated two high-risk regions as well as one low-risk region for gastric cancer in Gansu. Furthermore, our findings suggested that several social and environmental determinants such as medical resource allocation, regional economic development and climate conditions exerted significant influence on the incidence of gastric cancer. Conclusions Gastric cancer remains an enormous threat to people in Gansu Province, the significant risk areas, social and environmental determinants were observed in this study, which may improve our understanding of gastric cancer epidemiology and help guide public health interventions in Gansu Province.

Autism spectrum disorder, Telehealth, Early screening, Diagnosis, Snack time, Computer applications to medicine. Medical informatics, and R858-859.7

Abstract Background The COVID-19 pandemic has caused an unprecedented need for accessible health care services and significantly accelerated the development processes of telehealth tools for autism spectrum disorder (ASD) early screening and diagnosis. This study aimed to examine the feasibility and utility of a time-efficient telehealth tool combining a structured snack time assessment activity and a novel behaviour coding scheme for identifying ASD. Methods A total of 134 1–6-year-old individuals with ASD (age in months: mean = 51.3, SD = 13.1) and 134 age- and sex-matched typically developing individuals (TD) (age in months: mean = 54, SD = 9.44) completed a 1-min snack time interaction assessment with examiners. The recorded videos were then coded by trained coders for 17 ASD-related behaviours; the beginning and end points and the form and function of each behaviour were recorded, which took 10–15 min. Coded details were transformed into 62 indicators representing the count, duration, rate, and proportion of those behaviours. Results Twenty indicators with good reliability were selected for group difference, univariate and multivariate analyses. Fifteen behaviour indicators differed significantly between the ASD and TD groups and remained significant after Bonferroni correction, including the children’s response to the examiner’s initiation, eye gaze, pointing, facial expressions, vocalization and verbalization, and giving behaviours. Five indicators were included in the final prediction model: total counts of eye gaze, counts of standard pointing divided by the total counts of pointing, counts of appropriate facial expressions, counts of socially oriented vocalizations and verbalizations divided by the total counts of vocalizations and verbalizations, and counts of children using giving behaviours to respond to the examiner's initiations divided by the total counts of the examiner's initiation of snack requisitions. The ROC curve revealed a good prediction performance with an area under the curve (AUC) of 0.955, a sensitivity of 92.5% and a specificity of 84.3%. Conclusion Our results suggest that the snack activity-based ASD telehealth approach shows promise in primary health care settings for early ASD screening.

PROMIS, Psychometric, Sleep, Menopause, Vasomotor symptoms, Computer applications to medicine. Medical informatics, and R858-859.7

Abstract Background Women with vasomotor symptoms (VMS) due to menopause frequently experience poor sleep quality. The Patient-Reported Outcomes Measurement Information System Sleep Disturbance – Short Form 8b (PROMIS SD-SF-8b) has been developed to assess sleep disturbance. The study objective was to use data from the fezolinetant SKYLIGHT 1 and 2 studies in individuals with VMS to assess the psychometric properties of the PROMIS SD-SF-8b. Methods Individuals (aged ≥ 40–≤65 years) with moderate-to-severe VMS (≥ 7 hot flashes/day) were enrolled. Besides PROMIS SD-SF-8b, eight other patient-reported outcome (PRO) measures were used for the psychometric evaluation. All the PRO assessments were completed at weeks 4 and 12 during the treatment period and most were completed at baseline. Psychometric analyses included factor analysis and reliability, construct validity, and sensitivity to change assessments. The within-patient threshold for a clinically meaningful change in sleep disturbance was derived. Results Overall, 1022 individuals were included from the SKYLIGHT 1 and 2 studies. Mean PROMIS SD-SF-8b total score at baseline was 26.80, which decreased to 22.68 at week 12, reflecting improved sleep disturbance. The confirmatory factor analysis supported the proposed PROMIS SD-SF-8b domain structure. Internal consistency was excellent, with Cronbach’s alpha values of 0.915 and 0.935 and a McDonald’s omega of 0.917. Item-to-item and item-total correlations were sufficient and moderate test-retest reliability was noted. The construct validity assessments showed that moderate Spearman rank correlations (r: 0.608 to 0.651) were observed between PROMIS SD-SF-8b total scores and measures of sleep disturbance and sleep-related impairment, and that significant differences were noted in the total scores across PRO categories. The responsiveness of PROMIS SD-SF-8b total scores was supported by the results from the correlations in change scores and comparisons of mean change scores by PRO categories. Statistically significant differences in mean scores were observed between responder and non-responder PRO groups. A PROMIS SD-SF-8b total score of 8 points was identified as the within-patient threshold to use to confirm a meaningful change in sleep disturbance. Conclusions The psychometric properties of the PROMIS SD-SF-8b support its use to measure sleep disturbance in women with VMS due to menopause. Trial registration ClinicalTrials.gov numbers: NCT04003155 and NCT04003142.

Smartphone app, mHealth intervention, Engagement, Usage data, Log-data, Future Self-Identification, Computer applications to medicine. Medical informatics, and R858-859.7

Abstract Background Engagement with smartphone-based interventions stimulates adherence and improves the likelihood of gaining benefits from intervention content. Research often relies on system usage data to capture engagement. However, to what extent usage data reflect engagement is still an open empirical question. We studied how usage data relate to engagement, and how both relate to intervention outcomes. Methods We drew data from a randomized controlled trial (RCT) (N = 86) evaluating a smartphone-based intervention that aims to stimulate future self-identification (i.e., future self vividness, valence, relatedness). General app engagement and feature-specific engagement were retrospectively measured. Usage data (i.e., duration, number of logins, number of days used, exposure to intervention content) were unobtrusively registered. Results Engagement and usage data were not correlated. Multiple linear regression analyses revealed that general app engagement predicted future self vividness (p = .042) and relatedness (p = .004). Furthermore, engagement with several specific features also predicted aspects of future self-identification (p = .005 – .032). For usage data, the number of logins predicted future self vividness (p = .042) and exposure to intervention content predicted future self valence (p = .002). Conclusions Usage data did not reflect engagement and the latter was the better predictor of intervention outcomes. Thus, the relation between usage data and engagement is likely to be intervention-specific and the unqualified use of the former as an indicator of the latter may result in measurement error. We provide recommendations on how to capture engagement and app use in more valid ways.

Patient reported outcome measures, Hip pain, International hip outcome tool 12 (iHOT12), Validity, Reliability, Hebrew, Computer applications to medicine. Medical informatics, and R858-859.7

Abstract Background The “International Hip Outcome Tool 12” (iHOT12) is a self-administered patient-reported outcome tool for measuring health-related quality of life and physical functioning in young and active patients with hip pathology. Since the iHOT12 has become widely used, we sought to translate and validate it for Hebrew-speaking populations. The aims of this study were: (1) To translate and culturally adapt the iHOT12 into Hebrew using established guidelines. (2) To test the new Hebrew version for validity, and (3) reliability. Methods The iHOT12 was translated and culturally adapted from English to Hebrew (iHOT12-H) according to the COSAMIN guidelines. For validity, the iHOT12-H and Western Ontario and McMaster universities osteoarthritis index (WOMAC) were completed by 200 patients with hip pathology. Exploratory factor analysis was used to assess structural validity. Subsequently, 51 patients repeated the iHOT12-H within a 2-week interval. Intraclass Correlation Coefficient (ICC), Cronbach alpha, and Standard Error of Measurement (SEM) were calculated to assess reliability. Results Construct validity: iHOT12-H correlated strongly to the WOMAC scores (r = -0.82, P

Computer applications to medicine. Medical informatics and R858-859.7

The integration of Quality Management System (QMS) principles into the life cycle of development, deployment, and utilization of machine learning (ML) and artificial intelligence (AI) technologies within healthcare settings holds the potential to close the AI translation gap by establishing a robust framework that accelerates the safe, ethical, and effective delivery of AI/ML in day-to-day patient care. Healthcare organizations (HCOs) can implement these principles effectively by embracing an enterprise QMS analogous to those in regulated industries. By establishing a QMS explicitly tailored to health AI technologies, HCOs can comply with evolving regulations and minimize redundancy and rework while aligning their internal governance practices with their steadfast commitment to scientific rigor and medical excellence.

Computer applications to medicine. Medical informatics and R858-859.7

Abstract Patients experiencing mental health crises often seek help through messaging-based platforms, but may face long wait times due to limited message triage capacity. Here we build and deploy a machine-learning-enabled system to improve response times to crisis messages in a large, national telehealth provider network. We train a two-stage natural language processing (NLP) system with key word filtering followed by logistic regression on 721 electronic medical record chat messages, of which 32% are potential crises (suicidal/homicidal ideation, domestic violence, or non-suicidal self-injury). Model performance is evaluated on a retrospective test set (4/1/21–4/1/22, N = 481) and a prospective test set (10/1/22–10/31/22, N = 102,471). In the retrospective test set, the model has an AUC of 0.82 (95% CI: 0.78–0.86), sensitivity of 0.99 (95% CI: 0.96–1.00), and PPV of 0.35 (95% CI: 0.309–0.4). In the prospective test set, the model has an AUC of 0.98 (95% CI: 0.966–0.984), sensitivity of 0.98 (95% CI: 0.96–0.99), and PPV of 0.66 (95% CI: 0.626–0.692). The daily median time from message receipt to crisis specialist triage ranges from 8 to 13 min, compared to 9 h before the deployment of the system. We demonstrate that a NLP-based machine learning model can reliably identify potential crisis chat messages in a telehealth setting. Our system integrates into existing clinical workflows, suggesting that with appropriate training, humans can successfully leverage ML systems to facilitate triage of crisis messages.

Computer applications to medicine. Medical informatics and R858-859.7

Abstract Digital health technologies will play an ever-increasing role in the future of healthcare. It is crucial that the people who will help make that transformation possible have the evidence-based and hands-on training necessary to address the many challenges ahead. To better prepare the future health workforce with the knowledge necessary to support the re-engineering of healthcare in an equitable, person-centric manner, we developed an experiential learning course—Wearables in Healthcare—for advanced undergraduate and graduate university students. Here we describe the components of that course and the lessons learned to help guide others interested in developing similar courses.

Computer applications to medicine. Medical informatics and R858-859.7

Abstract Randomized clinical trials (RCT) represent the cornerstone of evidence-based medicine but are resource-intensive. We propose and evaluate a machine learning (ML) strategy of adaptive predictive enrichment through computational trial phenomaps to optimize RCT enrollment. In simulated group sequential analyses of two large cardiovascular outcomes RCTs of (1) a therapeutic drug (pioglitazone versus placebo; Insulin Resistance Intervention after Stroke (IRIS) trial), and (2) a disease management strategy (intensive versus standard systolic blood pressure reduction in the Systolic Blood Pressure Intervention Trial (SPRINT)), we constructed dynamic phenotypic representations to infer response profiles during interim analyses and examined their association with study outcomes. Across three interim timepoints, our strategy learned dynamic phenotypic signatures predictive of individualized cardiovascular benefit. By conditioning a prospective candidate’s probability of enrollment on their predicted benefit, we estimate that our approach would have enabled a reduction in the final trial size across ten simulations (IRIS: −14.8% ± 3.1%, p one-sample t-test = 0.001; SPRINT: −17.6% ± 3.6%, p one-sample t-test

Computer applications to medicine. Medical informatics and R858-859.7

Abstract Predicting in-hospital cardiac arrest in patients admitted to an intensive care unit (ICU) allows prompt interventions to improve patient outcomes. We developed and validated a machine learning-based real-time model for in-hospital cardiac arrest predictions using electrocardiogram (ECG)-based heart rate variability (HRV) measures. The HRV measures, including time/frequency domains and nonlinear measures, were calculated from 5 min epochs of ECG signals from ICU patients. A light gradient boosting machine (LGBM) algorithm was used to develop the proposed model for predicting in-hospital cardiac arrest within 0.5–24 h. The LGBM model using 33 HRV measures achieved an area under the receiver operating characteristic curve of 0.881 (95% CI: 0.875–0.887) and an area under the precision-recall curve of 0.104 (95% CI: 0.093–0.116). The most important feature was the baseline width of the triangular interpolation of the RR interval histogram. As our model uses only ECG data, it can be easily applied in clinical practice.

Computer applications to medicine. Medical informatics and R858-859.7

Abstract The effectiveness of lifestyle interventions in reducing caloric intake and increasing physical activity for preventing Type 2 Diabetes (T2D) has been previously demonstrated. The use of modern technologies can potentially further improve the success of these interventions, promote metabolic health, and prevent T2D at scale. To test this concept, we built a remote program that uses continuous glucose monitoring (CGM) and wearables to make lifestyle recommendations that improve health. We enrolled 2,217 participants with varying degrees of glucose levels (normal range, and prediabetes and T2D ranges), using continuous glucose monitoring (CGM) over 28 days to capture glucose patterns. Participants logged food intake, physical activity, and body weight via a smartphone app that integrated wearables data and provided daily insights, including overlaying glucose patterns with activity and food intake, macronutrient breakdown, glycemic index (GI), glycemic load (GL), and activity measures. The app furthermore provided personalized recommendations based on users’ preferences, goals, and observed glycemic patterns. Users could interact with the app for an additional 2 months without CGM. Here we report significant improvements in hyperglycemia, glucose variability, and hypoglycemia, particularly in those who were not diabetic at baseline. Body weight decreased in all groups, especially those who were overweight or obese. Healthy eating habits improved significantly, with reduced daily caloric intake and carbohydrate-to-calorie ratio and increased intake of protein, fiber, and healthy fats relative to calories. These findings suggest that lifestyle recommendations, in addition to behavior logging and CGM data integration within a mobile app, can enhance the metabolic health of both nondiabetic and T2D individuals, leading to healthier lifestyle choices. This technology can be a valuable tool for T2D prevention and treatment.

Metschnikowia pulcherrima, sMOMENT, Genome-scale models, Electron transport chain, Complex I, Yeast, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), and QH301-705.5

Abstract Background Use of alternative non-Saccharomyces yeasts in wine and beer brewing has gained more attention the recent years. This is both due to the desire to obtain a wider variety of flavours in the product and to reduce the final alcohol content. Given the metabolic differences between the yeast species, we wanted to account for some of the differences by using in silico models. Results We created and studied genome-scale metabolic models of five different non-Saccharomyces species using an automated processes. These were: Metschnikowia pulcherrima, Lachancea thermotolerans, Hanseniaspora osmophila, Torulaspora delbrueckii and Kluyveromyces lactis. Using the models, we predicted that M. pulcherrima, when compared to the other species, conducts more respiration and thus produces less fermentation products, a finding which agrees with experimental data. Complex I of the electron transport chain was to be present in M. pulcherrima, but absent in the others. The predicted importance of Complex I was diminished when we incorporated constraints on the amount of enzymatic protein, as this shifts the metabolism towards fermentation. Conclusions Our results suggest that Complex I in the electron transport chain is a key differentiator between Metschnikowia pulcherrima and the other yeasts considered. Yet, more annotations and experimental data have the potential to improve model quality in order to increase fidelity and confidence in these results. Further experiments should be conducted to confirm the in vivo effect of Complex I in M. pulcherrima and its respiratory metabolism.