Prediction of gestational diabetes based on nationwide electronic health records

Identification of Diagnostic CpG Signatures in Patients with Gestational Diabetes Mellitus via Epigenome-Wide Association Study Integrated with Machine Learning

Background

Gestational diabetes mellitus (GDM) is the most prevalent metabolic disease during pregnancy, but the diagnosis is controversial and lagging partly due to the lack of useful biomarkers. CpG methylation is involved in the development of GDM. However, the specific CpG methylation sites serving as diagnostic biomarkers of GDM remain unclear. Here, we aimed to explore CpG signatures and establish the predicting model for the GDM diagnosis.

Methods

DNA methylation data of GSE88929 and GSE102177 were obtained from the GEO database, followed by the epigenome-wide association study (EWAS). GO and KEGG pathway analyses were performed by using the clusterProfiler package of R. The PPI network was constructed in the STRING database and Cytoscape software. The SVM model was established, in which the β-values of selected CpG sites were the predictor variable and the occurrence of GDM was the outcome variable.

Results

We identified 62 significant CpG methylation sites in the GDM samples compared with the control samples. GO and KEGG analyses based on the 62 CpG sites demonstrated that several essential cellular processes and signaling pathways were enriched in the system. A total of 12 hub genes related to the identified CpG sites were found in the PPI network. The SVM model based on the selected CpGs within the promoter region, including cg00922748, cg05216211, cg05376185, cg06617468, cg17097119, and cg22385669, was established, and the AUC values of the training set and testing set in the model were 0.8138 and 0.7576. The AUC value of the independent validation set of GSE102177 was 0.6667.

Conclusion

We identified potential diagnostic CpG signatures by EWAS integrated with the SVM model. The SVM model based on the identified 6 CpG sites reliably predicted the GDM occurrence, contributing to the diagnosis of GDM. Our finding provides new insights into the cross-application of EWAS and machine learning in GDM investigation.

Predicting optimal treatment regimens for patients with HR+/HER2- breast cancer using machine learning based on electronic health records

Aim: To predict optimal treatments maximizing overall survival (OS) and time to treatment discontinuation (TTD) for patients with metastatic breast cancer (MBC) using machine learning methods on electronic health records. Patients/methods: Adult females with HR+/HER2- MBC on first- or second-line systemic therapy were eligible. Random survival forest (RSF) models were used to predict optimal regimen classes for individual patients and each line of therapy based on baseline characteristics. Results: RSF models suggested greater use of CDK4 & 6 inhibitor-based therapies may maximize OS and TTD. RSF-predicted optimal treatments demonstrated longer OS and TTD compared with nonoptimal treatments across line of therapy (hazard ratios = 0.44∼0.79). Conclusion: RSF may help inform optimal treatment choices and improve outcomes for patients with HR+/HER2- MBC.

Gut microbiota, inflammation, and molecular signatures of host response to infection

Gut microbial dysbiosis has been linked to many noncommunicable diseases. However, little is known about specific gut microbiota composition and its correlated metabolites associated with molecular signatures underlying host response to infection. Here, we describe the construction of a proteomic risk score based on 20 blood proteomic biomarkers, which have recently been identified as molecular signatures predicting the progression of the COVID-19. We demonstrate that in our cohort of 990 healthy individuals without infection, this proteomic risk score is positively associated with proinflammatory cytokines mainly among older, but not younger, individuals. We further discover that a core set of gut microbiota can accurately predict the above proteomic biomarkers among 301 individuals using a machine learning model and that these gut microbiota features are highly correlated with proinflammatory cytokines in another independent set of 366 individuals. Fecal metabolomics analysis suggests potential amino acid-related pathways linking gut microbiota to host metabolism and inflammation. Overall, our multi-omics analyses suggest that gut microbiota composition and function are closely related to inflammation and molecular signatures of host response to infection among healthy individuals. These results may provide novel insights into the cross-talk between gut microbiota and host immune system.

Development and validation of a machine learning-based postpartum depression prediction model: A nationwide cohort study

BACKGROUND

Currently, postpartum depression (PPD) screening is mainly based on self-report symptom-based assessment, with lack of an objective, integrative tool which identifies women at increased risk, before the emergent of PPD. We developed and validated a machine learning-based PPD prediction model utilizing electronic health record (EHR) data, and identified novel PPD predictors.

METHODS

A nationwide longitudinal cohort that included 214,359 births between January 2008 and December 2015, divided into model training and validation sets, was constructed utilizing Israel largest health maintenance organization's EHR-database. PPD was defined as new diagnosis of a depressive episode or antidepressant prescription within the first year postpartum. A gradient-boosted decision tree algorithm was applied to EHR-derived sociodemographic, clinical, and obstetric features.

RESULTS

Among the birth cohort, 1.9% (n = 4104) met the case definition of new-onset PPD. In the validation set, the prediction model achieved an area under the curve (AUC) of 0.712 (95% confidence interval, 0.690-0.733), with a sensitivity of 0.349 and a specificity of 0.905 at the 90th percentile risk threshold, identifying PPDs at a rate more than three times higher than the overall set (positive and negative predictive values were 0.074 and 0.985, respectively). The model's strongest predictors included both well-recognized (e.g., past depression) and less-recognized (differing patterns of blood tests) PPD risk factors.

CONCLUSIONS

Machine learning-based models incorporating EHR-derived predictors, could augment symptom-based screening practice by identifying the high-risk population at greatest need for preventive intervention, before development of PPD.

Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation

Background

Pneumonia is the most frequently encountered postoperative pulmonary complications (PPC) after orthotopic liver transplantation (OLT), which cause high morbidity and mortality rates. We aimed to develop a model to predict postoperative pneumonia in OLT patients using machine learning (ML) methods.

Methods

Data of 786 adult patients underwent OLT at the Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2019 was retrospectively extracted from electronic medical records and randomly subdivided into a training set and a testing set. With the training set, six ML models including logistic regression (LR), support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost) and gradient boosting machine (GBM) were developed. These models were assessed by the area under curve (AUC) of receiver operating characteristic on the testing set. The related risk factors and outcomes of pneumonia were also probed based on the chosen model.

Results

591 OLT patients were eventually included and 253 (42.81%) were diagnosed with postoperative pneumonia, which was associated with increased postoperative hospitalization and mortality (P < 0.05). Among the six ML models, XGBoost model performed best. The AUC of XGBoost model on the testing set was 0.734 (sensitivity: 52.6%; specificity: 77.5%). Pneumonia was notably associated with 14 items features: INR, HCT, PLT, ALB, ALT, FIB, WBC, PT, serum Na⁺, TBIL, anesthesia time, preoperative length of stay, total fluid transfusion and operation time.

Conclusion

Our study firstly demonstrated that the XGBoost model with 14 common variables might predict postoperative pneumonia in OLT patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-021-01690-3.

Could Subtle Obstetrical Brachial Plexus Palsy Be Related to Unilateral B Glenoid Osteoarthritis?

Background: Several factors associated with B glenoid are also linked with obstetrical brachial plexus palsy (OBPP). The purpose of this observational study was to determine the incidence of OBPP risk factors in type B patients. Methods: A cohort of 154 patients (68% men, 187 shoulders) aged 63 ± 17 years with type B glenoids completed a questionnaire comprising history of perinatal characteristics related to OBPP. A literature review was performed following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) to estimate the incidence of OBPP risk factors in the general population. Results: Twenty-seven patients (18%) reported one or more perinatal OBPP risk factors, including shoulder dystocia (n = 4, 2.6%), macrosomia >4 kg (n = 5, 3.2%), breech delivery (n = 6, 3.9%), fetal distress (n = 8, 5.2%), maternal diabetes (n = 2, 1.3%), clavicular fracture (n = 2, 1.3%), and forceps delivery (n = 4, 2.6%). The comparison with the recent literature suggested that most perinatal OBPP risk factors were within the normal range, although the incidence of shoulder dystocia, forceps and vaginal breech deliveries exceeded the average rates. Conclusion: Perinatal factors related to OBPP did not occur in a higher frequency in patients with Walch type B OA compared to the general population, although some of them were in the high normal range.

Early Prediction of Gestational Diabetes Mellitus in the Chinese Population via Advanced Machine Learning

CONTEXT

Accurate methods for early gestational diabetes mellitus (GDM) (during the first trimester of pregnancy) prediction in Chinese and other populations are lacking.

OBJECTIVES

This work aimed to establish effective models to predict early GDM.

METHODS

Pregnancy data for 73 variables during the first trimester were extracted from the electronic medical record system. Based on a machine learning (ML)-driven feature selection method, 17 variables were selected for early GDM prediction. To facilitate clinical application, 7 variables were selected from the 17-variable panel. Advanced ML approaches were then employed using the 7-variable data set and the 73-variable data set to build models predicting early GDM for different situations, respectively.

RESULTS

A total of 16 819 and 14 992 cases were included in the training and testing sets, respectively. Using 73 variables, the deep neural network model achieved high discriminative power, with area under the curve (AUC) values of 0.80. The 7-variable logistic regression (LR) model also achieved effective discriminate power (AUC = 0.77). Low body mass index (BMI) (≤ 17) was related to an increased risk of GDM, compared to a BMI in the range of 17 to 18 (minimum risk interval) (11.8% vs 8.7%, P = .09). Total 3,3,5'-triiodothyronine (T3) and total thyroxin (T4) were superior to free T3 and free T4 in predicting GDM. Lipoprotein(a) was demonstrated a promising predictive value (AUC = 0.66).

CONCLUSIONS

We employed ML models that achieved high accuracy in predicting GDM in early pregnancy. A clinically cost-effective 7-variable LR model was simultaneously developed. The relationship of GDM with thyroxine and BMI was investigated in the Chinese population.

Data-Driven Modeling of Pregnancy-Related Complications

A healthy pregnancy depends on complex interrelated biological adaptations involving placentation, maternal immune responses, and hormonal homeostasis. Recent advances in high-throughput technologies have provided access to multiomics biological data that, combined with clinical and social data, can provide a deeper understanding of normal and abnormal pregnancies. Integration of these heterogeneous datasets using state-of-the-art machine-learning methods can enable the prediction of short- and long-term health trajectories for a mother and offspring and the development of treatments to prevent or minimize complications. We review advanced machine-learning methods that could: provide deeper biological insights into a pregnancy not yet unveiled by current methodologies; clarify the etiologies and heterogeneity of pathologies that affect a pregnancy; and suggest the best approaches to address disparities in outcomes affecting vulnerable populations.

The oral microbiome of pregnant women facilitates gestational diabetes discrimination

The oral microbiota plays an important role in the development of various diseases, whereas its association with gestational diabetes mellitus (GDM) remains largely unclear. The aim of this study is to identify biomarkers from the oral microbiota of GDM patients by analyzing the microbiome of the saliva and dental plaque samples of 111 pregnant women. We find that the microbiota of both types of oral samples in GDM patients exhibits differences and significantly varies from that of patients with periodontitis or dental caries. Using bacterial biomarkers from the oral microbiota, GDM classification models based on support vector machine and random forest algorithms are constructed. The area under curve (AUC) value of the classification model constructed by combination of Lautropia and Neisseria in dental plaque and Streptococcus in saliva reaches 0.83, and the value achieves a maximum value of 0.89 by adding clinical features. These findings suggest that certain bacteria in either saliva or dental plaque can effectively distinguish women with GDM from healthy pregnant women, which provides evidence of oral microbiome as an informative source for developing noninvasive biomarkers of GDM.

Clinical practice recommendations for the detection and management of hyperglycemia in pregnancy from South Asia, Africa and Mexico during COVID-19 pandemic

The human coronavirus disease 2019 (COVID-19) pandemic has affected overall healthcare delivery, including prenatal, antenatal and postnatal care. Hyperglycemia in pregnancy (HIP) is the most common medical condition encountered during pregnancy. There is little guidance for primary care physicians for providing delivery of optimal perinatal care while minimizing the risk of COVID-19 infection in pregnant women. This review aims to describe pragmatic modifications in the screening, detection and management of HIP during the COVID- 19 pandemic. In this review, articles published up to June 2021 were searched on multiple databases, including PubMed, Medline, EMBASE and ScienceDirect. Direct online searches were conducted to identify national and international guidelines. Search criteria included terms to extract articles describing HIP with and/or without COVID-19 between 1^st March 2020 and 15^th June 2021. Fasting plasma glucose, glycosylated hemoglobin (HbA1c) and random plasma glucose could be alternative screening strategies for gestational diabetes mellitus screening (at 24–28 weeks of gestation), instead of the traditional 2 h oral glucose tolerance test. The use of telemedicine for the management of HIP is recommended. Hospital visits should be scheduled to coincide with obstetric and ultrasound visits. COVID-19 infected pregnant women with HIP need enhanced maternal and fetal vigilance, optimal diabetes care and psychological support in addition to supportive measures. This article presents pragmatic options and approaches for primary care physicians, diabetes care providers and obstetricians for GDM screening, diagnosis and management during the pandemic, to be used in conjunction with routine antenatal care.

Machine Learning Prediction Models for Gestational Diabetes Mellitus: A meta- analysis (Preprint)

Background

Gestational diabetes mellitus (GDM) is a common endocrine metabolic disease, involving a carbohydrate intolerance of variable severity during pregnancy. The incidence of GDM-related complications and adverse pregnancy outcomes has declined, in part, due to early screening. Machine learning (ML) models are increasingly used to identify risk factors and enable the early prediction of GDM.

Objective

The aim of this study was to perform a meta-analysis and comparison of published prognostic models for predicting the risk of GDM and identify predictors applicable to the models.

Methods

Four reliable electronic databases were searched for studies that developed ML prediction models for GDM in the general population instead of among high-risk groups only. The novel Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias of the ML models. The Meta-DiSc software program (version 1.4) was used to perform the meta-analysis and determination of heterogeneity. To limit the influence of heterogeneity, we also performed sensitivity analyses, a meta-regression, and subgroup analysis.

Results

A total of 25 studies that included women older than 18 years without a history of vital disease were analyzed. The pooled area under the receiver operating characteristic curve (AUROC) for ML models predicting GDM was 0.8492; the pooled sensitivity was 0.69 (95% CI 0.68-0.69; P<.001; I²=99.6%) and the pooled specificity was 0.75 (95% CI 0.75-0.75; P<.001; I²=100%). As one of the most commonly employed ML methods, logistic regression achieved an overall pooled AUROC of 0.8151, while non–logistic regression models performed better, with an overall pooled AUROC of 0.8891. Additionally, maternal age, family history of diabetes, BMI, and fasting blood glucose were the four most commonly used features of models established by the various feature selection methods.

Conclusions

Compared to current screening strategies, ML methods are attractive for predicting GDM. To expand their use, the importance of quality assessments and unified diagnostic criteria should be further emphasized.

Gestational Diabetes Mellitus-Innovative Approach to Prediction, Diagnosis, Management, and Prevention of Future NCD-Mother and Offspring

Gestational diabetes mellitus (GDM) is the commonest medical complication of pregnancy. The association of GDM with immediate pregnancy complications including excess fetal growth and adiposity with subsequent risk of birth trauma and with hypertensive disorders of pregnancy is well recognized. However, the associations with wide ranges of longer-term health outcomes for mother and baby, including the lifetime risks of obesity, pre-diabetes, and diabetes and cardiovascular disease have received less attention and few health systems address these important issues in a systematic way. This article reviews historical and recent data regarding prediction of GDM using demographic, clinical, and biochemical parameters. We evaluate current and potential future diagnostic approaches designed to most effectively identify GDM and extend this analysis into a critical evaluation of lifestyle and nutritional/pharmacologic interventions designed to prevent the development of GDM. The general approach to management of GDM during pregnancy is then discussed and the major final focus of the article revolves around the importance of a GDM diagnosis as a future marker of the risk of non-communicable disease (NCD), in particular pre-diabetes, diabetes, and cardiovascular disease, both in mother and offspring.

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

BACKGROUND

Predictions in pregnancy care are complex because of interactions among multiple factors. Hence, pregnancy outcomes are not easily predicted by a single predictor using only one algorithm or modeling method.

OBJECTIVE

This study aims to review and compare the predictive performances between logistic regression (LR) and other machine learning algorithms for developing or validating a multivariable prognostic prediction model for pregnancy care to inform clinicians' decision making.

METHODS

Research articles from MEDLINE, Scopus, Web of Science, and Google Scholar were reviewed following several guidelines for a prognostic prediction study, including a risk of bias (ROB) assessment. We report the results based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were primarily framed as PICOTS (population, index, comparator, outcomes, timing, and setting): Population: men or women in procreative management, pregnant women, and fetuses or newborns; Index: multivariable prognostic prediction models using non-LR algorithms for risk classification to inform clinicians' decision making; Comparator: the models applying an LR; Outcomes: pregnancy-related outcomes of procreation or pregnancy outcomes for pregnant women and fetuses or newborns; Timing: pre-, inter-, and peripregnancy periods (predictors), at the pregnancy, delivery, and either puerperal or neonatal period (outcome), and either short- or long-term prognoses (time interval); and Setting: primary care or hospital. The results were synthesized by reporting study characteristics and ROBs and by random effects modeling of the difference of the logit area under the receiver operating characteristic curve of each non-LR model compared with the LR model for the same pregnancy outcomes. We also reported between-study heterogeneity by using τ2 and I2.

RESULTS

Of the 2093 records, we included 142 studies for the systematic review and 62 studies for a meta-analysis. Most prediction models used LR (92/142, 64.8%) and artificial neural networks (20/142, 14.1%) among non-LR algorithms. Only 16.9% (24/142) of studies had a low ROB. A total of 2 non-LR algorithms from low ROB studies significantly outperformed LR. The first algorithm was a random forest for preterm delivery (logit AUROC 2.51, 95% CI 1.49-3.53; I2=86%; τ2=0.77) and pre-eclampsia (logit AUROC 1.2, 95% CI 0.72-1.67; I2=75%; τ2=0.09). The second algorithm was gradient boosting for cesarean section (logit AUROC 2.26, 95% CI 1.39-3.13; I2=75%; τ2=0.43) and gestational diabetes (logit AUROC 1.03, 95% CI 0.69-1.37; I2=83%; τ2=0.07).

CONCLUSIONS

Prediction models with the best performances across studies were not necessarily those that used LR but also used random forest and gradient boosting that also performed well. We recommend a reanalysis of existing LR models for several pregnancy outcomes by comparing them with those algorithms that apply standard guidelines.

TRIAL REGISTRATION

PROSPERO (International Prospective Register of Systematic Reviews) CRD42019136106; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=136106.

Maternal thyroid profile in first and second trimester of pregnancy is correlated with gestational diabetes mellitus through machine learning

There is evidence about a possible relationship between thyroid abnormalities and gestational diabetes mellitus (GDM). However, there is still no conclusive data on this dependence, since no strong correlation has been proved. In this work, we used machine learning to determine whether there is a correlation between maternal thyroid profile in first and second trimester of pregnancy and GDM. Using principal component analysis, it was possible to find an evident correlation between both, which could be used as a complement for a more sensitive GDM diagnosis.

Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology

Artificial intelligence (AI) uses data and algorithms to aim to draw conclusions that are as good as, or even better than, those drawn by humans. AI is already part of our daily life; it is behind face recognition technology, speech recognition in virtual assistants (such as Amazon Alexa, Apple's Siri, Google Assistant and Microsoft Cortana) and self-driving cars. AI software has been able to beat world champions in chess, Go and recently even Poker. Relevant to our community, it is a prominent source of innovation in healthcare, already helping to develop new drugs, support clinical decisions and provide quality assurance in radiology. The list of medical image-analysis AI applications with USA Food and Drug Administration or European Union (soon to fall under European Union Medical Device Regulation) approval is growing rapidly and covers diverse clinical needs, such as detection of arrhythmia using a smartwatch or automatic triage of critical imaging studies to the top of the radiologist's worklist. Deep learning, a leading tool of AI, performs particularly well in image pattern recognition and, therefore, can be of great benefit to doctors who rely heavily on images, such as sonologists, radiographers and pathologists. Although obstetric and gynecological ultrasound are two of the most commonly performed imaging studies, AI has had little impact on this field so far. Nevertheless, there is huge potential for AI to assist in repetitive ultrasound tasks, such as automatically identifying good-quality acquisitions and providing instant quality assurance. For this potential to thrive, interdisciplinary communication between AI developers and ultrasound professionals is necessary. In this article, we explore the fundamentals of medical imaging AI, from theory to applicability, and introduce some key terms to medical professionals in the field of ultrasound. We believe that wider knowledge of AI will help accelerate its integration into healthcare. © 2020 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology.

Development and Validation of the Quick COVID-19 Severity Index: A Prognostic Tool for Early Clinical Decompensation

STUDY OBJECTIVE

The goal of this study is to create a predictive, interpretable model of early hospital respiratory failure among emergency department (ED) patients admitted with coronavirus disease 2019 (COVID-19).

METHODS

This was an observational, retrospective, cohort study from a 9-ED health system of admitted adult patients with severe acute respiratory syndrome coronavirus 2 (COVID-19) and an oxygen requirement less than or equal to 6 L/min. We sought to predict respiratory failure within 24 hours of admission as defined by oxygen requirement of greater than 10 L/min by low-flow device, high-flow device, noninvasive or invasive ventilation, or death. Predictive models were compared with the Elixhauser Comorbidity Index, quick Sequential [Sepsis-related] Organ Failure Assessment, and the CURB-65 pneumonia severity score.

RESULTS

During the study period, from March 1 to April 27, 2020, 1,792 patients were admitted with COVID-19, 620 (35%) of whom had respiratory failure in the ED. Of the remaining 1,172 admitted patients, 144 (12.3%) met the composite endpoint within the first 24 hours of hospitalization. On the independent test cohort, both a novel bedside scoring system, the quick COVID-19 Severity Index (area under receiver operating characteristic curve mean 0.81 [95% confidence interval {CI} 0.73 to 0.89]), and a machine-learning model, the COVID-19 Severity Index (mean 0.76 [95% CI 0.65 to 0.86]), outperformed the Elixhauser mortality index (mean 0.61 [95% CI 0.51 to 0.70]), CURB-65 (0.50 [95% CI 0.40 to 0.60]), and quick Sequential [Sepsis-related] Organ Failure Assessment (0.59 [95% CI 0.50 to 0.68]). A low quick COVID-19 Severity Index score was associated with a less than 5% risk of respiratory decompensation in the validation cohort.

CONCLUSION

A significant proportion of admitted COVID-19 patients progress to respiratory failure within 24 hours of admission. These events are accurately predicted with bedside respiratory examination findings within a simple scoring system.

An Innovative Artificial Intelligence-Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study

Background

Gestational diabetes mellitus (GDM) can cause adverse consequences to both mothers and their newborns. However, pregnant women living in low- and middle-income areas or countries often fail to receive early clinical interventions at local medical facilities due to restricted availability of GDM diagnosis. The outstanding performance of artificial intelligence (AI) in disease diagnosis in previous studies demonstrates its promising applications in GDM diagnosis.

Objective

This study aims to investigate the implementation of a well-performing AI algorithm in GDM diagnosis in a setting, which requires fewer medical equipment and staff and to establish an app based on the AI algorithm. This study also explores possible progress if our app is widely used.

Methods

An AI model that included 9 algorithms was trained on 12,304 pregnant outpatients with their consent who received a test for GDM in the obstetrics and gynecology department of the First Affiliated Hospital of Jinan University, a local hospital in South China, between November 2010 and October 2017. GDM was diagnosed according to American Diabetes Association (ADA) 2011 diagnostic criteria. Age and fasting blood glucose were chosen as critical parameters. For validation, we performed k-fold cross-validation (k=5) for the internal dataset and an external validation dataset that included 1655 cases from the Prince of Wales Hospital, the affiliated teaching hospital of the Chinese University of Hong Kong, a non-local hospital. Accuracy, sensitivity, and other criteria were calculated for each algorithm.

Results

The areas under the receiver operating characteristic curve (AUROC) of external validation dataset for support vector machine (SVM), random forest, AdaBoost, k-nearest neighbors (kNN), naive Bayes (NB), decision tree, logistic regression (LR), eXtreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT) were 0.780, 0.657, 0.736, 0.669, 0.774, 0.614, 0.769, 0.742, and 0.757, respectively. SVM also retained high performance in other criteria. The specificity for SVM retained 100% in the external validation set with an accuracy of 88.7%.

Conclusions

Our prospective and multicenter study is the first clinical study that supports the GDM diagnosis for pregnant women in resource-limited areas, using only fasting blood glucose value, patients’ age, and a smartphone connected to the internet. Our study proved that SVM can achieve accurate diagnosis with less operation cost and higher efficacy. Our study (referred to as GDM-AI study, ie, the study of AI-based diagnosis of GDM) also shows our app has a promising future in improving the quality of maternal health for pregnant women, precision medicine, and long-distance medical care. We recommend future work should expand the dataset scope and replicate the process to validate the performance of the AI algorithms.

Gestational diabetes: opportunities for improving maternal and child health

Gestational diabetes, the most common medical disorder in pregnancy, is defined as glucose intolerance resulting in hyperglycaemia that begins or is first diagnosed in pregnancy. Gestational diabetes is associated with increased pregnancy complications and long-term metabolic risks for the woman and the offspring. However, the current diagnostic and management strategies recommended by national and international guidelines are mainly focused on short-term risks during pregnancy and delivery, except the Carpenter-Coustan criteria, which were based on the risk of future incidence of type 2 diabetes post-gestational diabetes. In this Personal View, first, we summarise the evidence for long-term risk in women with gestational diabetes and their offspring. Second, we suggest that a shift is needed in the thinking about gestational diabetes; moving from the perception of a short-term condition that confers increased risks of large babies to a potentially modifiable long-term condition that contributes to the growing burden of childhood obesity and cardiometabolic disorders in women and the future generation. Third, we propose how the current clinical practice might be improved. Finally, we outline and justify priorities for future research.

Interplay between oxidative damage, the redox status, and metabolic biomarkers during long-term fasting

Obesity and its related metabolic disorders, as well as infectious diseases like covid-19, are important health risks nowadays. It was recently documented that long-term fasting improves metabolic health and enhanced the total antioxidant capacity. The present study investigated the influence of a 10-day fasting on markers of the redox status in 109 subjects. Reducing power, 2,2’-Azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt radical cation(ABTS) radical scavenging capacity, and hydroxyl radical scavenging capacity increased significantly, and indicated an increase of circulating antioxidant levels. No differences were detected in superoxide scavenging capacity, protein carbonyls, and superoxide dismutase when measured at baseline and after 10 days of fasting. These findings were concomitant to a decrease in blood glucose, insulin, glycated hemoglobin (HbA1c), total cholesterol, low-density lipoprotein (LDL) and triglycerides as well as an increase in total cholesterol/high-density lipoprotein (HDL) ratio. In addition, the well-being index as well as the subjective energy levels increased, documenting a good tolerability. There was an interplay between redox and metabolic parameters since lipid peroxidation baseline levels (thiobarbituric acid reactive substances [TBARS]) affected the ability of long-term fasting to normalize lipid levels. A machine learning model showed that a combination of antioxidant parameters measured at baseline predicted the efficiency of the fasting regimen to decrease LDL levels. In conclusion, it was demonstrated that long-term fasting enhanced the endogenous production of antioxidant molecules, that act protectively against free radicals, and in parallel improved the metabolic health status. Our results suggest that the outcome of long-term fasting strategies could be depending on the baseline values of the antioxidative and metabolic status of subjects.

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Long-term fasting increases the antioxidant capacity and decreases oxidative damage.

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It improves the metabolic health status.

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High TBARS levels at baseline limit the LDL reduction during long-term fasting.

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The antioxidant status is related with the lipid lowering effect of long-term fasting.

Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database

The application of machine learning (ML) for use in generating insights and making predictions on new records continues to expand within the medical community. Despite this progress to date, the application of time series analysis has remained underexplored due to complexity of the underlying techniques. In this study, we have deployed a novel ML, called automated time series (AutoTS) machine learning, to automate data processing and the application of a multitude of models to assess which best forecasts future values. This rapid experimentation allows for and enables the selection of the most accurate model in order to perform time series predictions. By using the nation-wide ICD-10 (International Classification of Diseases, Tenth Revision) dataset of hospitalized patients of Romania, we have generated time series datasets over the period of 2008–2018 and performed highly accurate AutoTS predictions for the ten deadliest diseases. Forecast results for the years 2019 and 2020 were generated on a NUTS 2 (Nomenclature of Territorial Units for Statistics) regional level. This is the first study to our knowledge to perform time series forecasting of multiple diseases at a regional level using automated time series machine learning on a national ICD-10 dataset. The deployment of AutoTS technology can help decision makers in implementing targeted national health policies more efficiently.

Electronic Health Records to Predict Gestational Diabetes Risk

Gestational diabetes mellitus is a common pregnancy complication associated with significant adverse health outcomes for both women and infants. Effective screening and early prediction tools as part of routine clinical care are needed to reduce the impact of the disease on the baby and mother. Using large-scale electronic health records, Artzi and colleagues developed and evaluated a machine learning driven tool to identify women at high and low risk of GDM. Their findings showcase how artificial intelligence approaches can potentially be embedded in clinical care to enable accurate and rapid risk stratification.

How Machine Learning Will Transform Biomedicine

This Perspective explores the application of machine learning toward improved diagnosis and treatment. We outline a vision for how machine learning can transform three broad areas of biomedicine: clinical diagnostics, precision treatments, and health monitoring, where the goal is to maintain health through a range of diseases and the normal aging process. For each area, early instances of successful machine learning applications are discussed, as well as opportunities and challenges for machine learning. When these challenges are met, machine learning promises a future of rigorous, outcomes-based medicine with detection, diagnosis, and treatment strategies that are continuously adapted to individual and environmental differences.

Machine Learning Applications in Endocrinology and Metabolism Research: An Overview

Machine learning (ML) applications have received extensive attention in endocrinology research during the last decade. This review summarizes the basic concepts of ML and certain research topics in endocrinology and metabolism where ML principles have been actively deployed. Relevant studies are discussed to provide an overview of the methodology, main findings, and limitations of ML, with the goal of stimulating insights into future research directions. Clear, testable study hypotheses stem from unmet clinical needs, and the management of data quality (beyond a focus on quantity alone), open collaboration between clinical experts and ML engineers, the development of interpretable high-performance ML models beyond the black-box nature of some algorithms, and a creative environment are the core prerequisites for the foreseeable changes expected to be brought about by ML and artificial intelligence in the field of endocrinology and metabolism, with actual improvements in clinical practice beyond hype. Of note, endocrinologists will continue to play a central role in these developments as domain experts who can properly generate, refine, analyze, and interpret data with a combination of clinical expertise and scientific rigor.

Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study

BACKGROUND

Gestational diabetes mellitus (GDM) contributes to adverse pregnancy and birth outcomes. In recent decades, extensive research has been devoted to the early prediction of GDM by various methods. Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression.

OBJECTIVE

The purpose of this study was to use machine learning methods to predict GDM and compare their performance with that of logistic regressions.

METHODS

We performed a retrospective, observational study including women who attended their routine first hospital visits during early pregnancy and had Down's syndrome screening at 16-20 gestational weeks in a tertiary maternity hospital in China from 2013.1.1 to 2017.12.31. A total of 22,242 singleton pregnancies were included, and 3182 (14.31%) women developed GDM. Candidate predictors included maternal demographic characteristics and medical history (maternal factors) and laboratory values at early pregnancy. The models were derived from the first 70% of the data and then validated with the next 30%. Variables were trained in different machine learning models and traditional logistic regression models. Eight common machine learning methods (GDBT, AdaBoost, LGB, Logistic, Vote, XGB, Decision Tree, and Random Forest) and two common regressions (stepwise logistic regression and logistic regression with RCS) were implemented to predict the occurrence of GDM. Models were compared on discrimination and calibration metrics.

RESULTS

In the validation dataset, the machine learning and logistic regression models performed moderately (AUC 0.59-0.74). Overall, the GBDT model performed best (AUC 0.74, 95% CI 0.71-0.76) among the machine learning methods, with negligible differences between them. Fasting blood glucose, HbA1c, triglycerides, and BMI strongly contributed to GDM. A cutoff point for the predictive value at 0.3 in the GBDT model had a negative predictive value of 74.1% (95% CI 69.5%-78.2%) and a sensitivity of 90% (95% CI 88.0%-91.7%), and the cutoff point at 0.7 had a positive predictive value of 93.2% (95% CI 88.2%-96.1%) and a specificity of 99% (95% CI 98.2%-99.4%).

CONCLUSION

In this study, we found that several machine learning methods did not outperform logistic regression in predicting GDM. We developed a model with cutoff points for risk stratification of GDM.