Increasing Adoption of Deep Learning Models in Medicine and Circadian Omic Analyses through Interpretability and Data Availability

There are numerous applications for deep learning in a healthcare setting including: providing more accurate diagnoses, recommending treatment plans, predicting patient outcomes, tracking patient engagement and adherence, and reducing the burden of administrative tasks. This plethora of applications has resulted in the widespread publication of deep learning algorithms applied to healthcare data. Despite numerous publications showing deep learning to be very successful in retrospective healthcare studies, very few of these algorithms are then actually incorporated into clinical practice. While there are many factors influencing the lack of algorithm deployment, one of the major reasons is a lack of trust in deep learning. This lack of trust stems in part from a lack of model interpretability and an inability to independently verify published results due to a lack of data availability. In this work, we explore generalized additive models with neural networks (GAM-NNs) as a method of improving model interpretability and we propose MOVER: Medical Informatics Operating Room Vi- tals and Events Repository a publicly available repository of medical data designed to improve visibility into deep learning algorithms in healthcare.Similarly, deep learning can be used to analyze circadian omic (e.g. transcriptomic, metabolomic, proteomic) time series data. Several studies have shown that a disruption to circadian rhythms have been linked to health problems such as cancer, diabetes, obesity, and premature aging. In order to gain clinician trust in the conclusions drawn from circadian omic analyses we propose CircadiOmics: the largest annotated repository of circadian omic time series data analyzed using deep learning. Clinicians and researchers can use CircadiOmics to not only validate the findings of their circadian omic experiments, but also to analyze multiple circadian omic experiments in aggregate

Increasing Adoption of Deep Learning Models in Medicine and Circadian Omic Analyses through Interpretability and Data Availability

There are numerous applications for deep learning in a healthcare setting including: providing more accurate diagnoses, recommending treatment plans, predicting patient outcomes, tracking patient engagement and adherence, and reducing the burden of administrative tasks. This plethora of applications has resulted in the widespread publication of deep learning algorithms applied to healthcare data. Despite numerous publications showing deep learning to be very successful in retrospective healthcare studies, very few of these algorithms are then actually incorporated into clinical practice. While there are many factors influencing the lack of algorithm deployment, one of the major reasons is a lack of trust in deep learning. This lack of trust stems in part from a lack of model interpretability and an inability to independently verify published results due to a lack of data availability. In this work, we explore generalized additive models with neural networks (GAM-NNs) as a method of improving model interpretability and we propose MOVER: Medical Informatics Operating Room Vi- tals and Events Repository a publicly available repository of medical data designed to improve visibility into deep learning algorithms in healthcare.Similarly, deep learning can be used to analyze circadian omic (e.g. transcriptomic, metabolomic, proteomic) time series data. Several studies have shown that a disruption to circadian rhythms have been linked to health problems such as cancer, diabetes, obesity, and premature aging. In order to gain clinician trust in the conclusions drawn from circadian omic analyses we propose CircadiOmics: the largest annotated repository of circadian omic time series data analyzed using deep learning. Clinicians and researchers can use CircadiOmics to not only validate the findings of their circadian omic experiments, but also to analyze multiple circadian omic experiments in aggregate

CircadiOmics: circadian omic web portal.

Circadian rhythms are a foundational aspect of biology. These rhythms are found at the molecular level in every cell of every living organism and they play a fundamental role in homeostasis and a variety of physiological processes. As a result, biomedical research of circadian rhythms continues to expand at a rapid pace. To support this research, CircadiOmics (http://circadiomics.igb.uci.edu/) is the largest annotated repository and analytic web server for high-throughput omic (e.g. transcriptomic, metabolomic, proteomic) circadian time series experimental data. CircadiOmics contains over 290 experiments and over 100 million individual measurements, across >20 unique tissues/organs, and 11 different species. Users are able to visualize and mine these datasets by deriving and comparing periodicity statistics for oscillating molecular species including: period, amplitude, phase, P-value and q-value. These statistics are obtained from BIO_CYCLE and JTK_CYCLE and are intuitively aggregated and displayed for comparison. CircadiOmics is the most up-to-date and cutting-edge web portal for searching and analyzing circadian omic data and is used by researchers around the world

Development and validation of an interpretable neural network for prediction of postoperative in-hospital mortality.

While deep neural networks (DNNs) and other machine learning models often have higher accuracy than simpler models like logistic regression (LR), they are often considered to be "black box" models and this lack of interpretability and transparency is considered a challenge for clinical adoption. In healthcare, intelligible models not only help clinicians to understand the problem and create more targeted action plans, but also help to gain the clinicians' trust. One method of overcoming the limited interpretability of more complex models is to use Generalized Additive Models (GAMs). Standard GAMs simply model the target response as a sum of univariate models. Inspired by GAMs, the same idea can be applied to neural networks through an architecture referred to as Generalized Additive Models with Neural Networks (GAM-NNs). In this manuscript, we present the development and validation of a model applying the concept of GAM-NNs to allow for interpretability by visualizing the learned feature patterns related to risk of in-hospital mortality for patients undergoing surgery under general anesthesia. The data consists of 59,985 patients with a feature set of 46 features extracted at the end of surgery to which we added previously not included features: total anesthesia case time (1 feature); the time in minutes spent with mean arterial pressure (MAP) below 40, 45, 50, 55, 60, and 65 mmHg during surgery (6 features); and Healthcare Cost and Utilization Project (HCUP) Code Descriptions of the Primary current procedure terminology (CPT) codes (33 features) for a total of 86 features. All data were randomly split into 80% for training (n = 47,988) and 20% for testing (n = 11,997) prior to model development. Model performance was compared to a standard LR model using the same features as the GAM-NN. The data consisted of 59,985 surgical records, and the occurrence of in-hospital mortality was 0.81% in the training set and 0.72% in the testing set. The GAM-NN model with HCUP features had the highest area under the curve (AUC) 0.921 (0.895-0.95). Overall, both GAM-NN models had higher AUCs than LR models, however, had lower average precisions. The LR model without HCUP features had the highest average precision 0.217 (0.136-0.31). To assess the interpretability of the GAM-NNs, we then visualized the learned contributions of the GAM-NNs and compared against the learned contributions of the LRs for the models with HCUP features. Overall, we were able to demonstrate that our proposed generalized additive neural network (GAM-NN) architecture is able to (1) leverage a neural network's ability to learn nonlinear patterns in the data, which is more clinically intuitive, (2) be interpreted easily, making it more clinically useful, and (3) maintain model performance as compared to previously published DNNs

Medical Informatics Operating Room Vitals and Events Repository (MOVER): a public-access operating room database.

OBJECTIVES: Artificial intelligence (AI) holds great promise for transforming the healthcare industry. However, despite its potential, AI is yet to see widespread deployment in clinical settings in significant part due to the lack of publicly available clinical data and the lack of transparency in the published AI algorithms. There are few clinical data repositories publicly accessible to researchers to train and test AI algorithms, and even fewer that contain specialized data from the perioperative setting. To address this gap, we present and release the Medical Informatics Operating Room Vitals and Events Repository (MOVER). MATERIALS AND METHODS: This first release of MOVER includes adult patients who underwent surgery at the University of California, Irvine Medical Center from 2015 to 2022. Data for patients who underwent surgery were captured from 2 different sources: High-fidelity physiological waveforms from all of the operating rooms were captured in real time and matched with electronic medical record data. RESULTS: MOVER includes data from 58 799 unique patients and 83 468 surgeries. MOVER is available for download at https://doi.org/10.24432/C5VS5G, it can be downloaded by anyone who signs a data usage agreement (DUA), to restrict traffic to legitimate researchers. DISCUSSION: To the best of our knowledge MOVER is the only freely available public data repository that contains electronic health record and high-fidelity physiological waveforms data for patients undergoing surgery. CONCLUSION: MOVER is freely available to all researchers who sign a DUA, and we hope that it will accelerate the integration of AI into healthcare settings, ultimately leading to improved patient outcomes

The hidden link between circadian entropy and mental health disorders

Abstract The high overlapping nature of various features across multiple mental health disorders suggests the existence of common psychopathology factor(s) (p-factors) that mediate similar phenotypic presentations across distinct but relatable disorders. In this perspective, we argue that circadian rhythm disruption (CRD) is a common underlying p-factor that bridges across mental health disorders within their age and sex contexts. We present and analyze evidence from the literature for the critical roles circadian rhythmicity plays in regulating mental, emotional, and behavioral functions throughout the lifespan. A review of the literature shows that coarse CRD, such as sleep disruption, is prevalent in all mental health disorders at the level of etiological and pathophysiological mechanisms and clinical phenotypical manifestations. Finally, we discuss the subtle interplay of CRD with sex in relation to these disorders across different stages of life. Our perspective highlights the need to shift investigations towards molecular levels, for instance, by using spatiotemporal circadian “omic” studies in animal models to identify the complex and causal relationships between CRD and mental health disorders

Reshaping circadian metabolism in the suprachiasmatic nucleus and prefrontal cortex by nutritional challenge

Significance : Nutrition and the body clock are deeply intertwined, both impinging on our physiological health. Food composition can dramatically rewire peripheral clock metabolism; however, whether food challenges can impact circadian metabolism of the master clock in the suprachiasmatic nucleus (SCN) or other brain areas has not been fully explored. Here we analyzed the complete diurnal metabolome of the SCN and medial prefrontal cortex (mPFC) in mice fed a balanced diet or a high-fat diet (HFD). Strikingly, our data reveal unexpected daily rhythmicity in both SCN and mPFC metabolites that is significantly impacted by HFD in a region-specific manner. Our findings unveil an unsuspected sensitivity of brain clocks to nutrition. Abstract : Food is a powerful entrainment cue for circadian clocks in peripheral tissues, and changes in the composition of nutrients have been demonstrated to metabolically reprogram peripheral clocks. However, how food challenges may influence circadian metabolism of the master clock in the suprachiasmatic nucleus (SCN) or in other brain areas is poorly understood. Using high-throughput metabolomics, we studied the circadian metabolome profiles of the SCN and medial prefrontal cortex (mPFC) in lean mice compared with mice challenged with a high-fat diet (HFD). Both the mPFC and the SCN displayed a robust cyclic metabolism, with a strikingly high sensitivity to HFD perturbation in an area-specific manner. The phase and amplitude of oscillations were drastically different between the SCN and mPFC, and the metabolic pathways impacted by HFD were remarkably region-dependent. Furthermore, HFD induced a significant increase in the number of cycling metabolites exclusively in the SCN, revealing an unsuspected susceptibility of the master clock to food stress

Reshaping circadian metabolism in the suprachiasmatic nucleus and prefrontal cortex by nutritional challenge

Food is a powerful entrainment cue for circadian clocks in peripheral tissues, and changes in the composition of nutrients have been demonstrated to metabolically reprogram peripheral clocks. However, how food challenges may influence circadian metabolism of the master clock in the suprachiasmatic nucleus (SCN) or in other brain areas is poorly understood. Using high-throughput metabolomics, we studied the circadian metabolome profiles of the SCN and medial prefrontal cortex (mPFC) in lean mice compared with mice challenged with a high-fat diet (HFD). Both the mPFC and the SCN displayed a robust cyclic metabolism, with a strikingly high sensitivity to HFD perturbation in an area-specific manner. The phase and amplitude of oscillations were drastically different between the SCN and mPFC, and the metabolic pathways impacted by HFD were remarkably region-dependent. Furthermore, HFD induced a significant increase in the number of cycling metabolites exclusively in the SCN, revealing an unsuspected susceptibility of the master clock to food stress

Deep Learning Analysis of Vibrational Spectra of Bacterial Lysate for Rapid Antimicrobial Susceptibility Testing

Rapid antimicrobial susceptibility testing (AST) is an integral tool to mitigate the unnecessary use of powerful and broad-spectrum antibiotics that leads to the proliferation of multi-drug-resistant bacteria. Using a sensor platform composed of surface-enhanced Raman scattering (SERS) sensors with control of nanogap chemistry and machine learning algorithms for analysis of complex spectral data, bacteria metabolic profiles post antibiotic exposure are correlated with susceptibility. Deep neural network models are able to discriminate the responses of Escherichia coli and Pseudomonas aeruginosa to antibiotics from untreated cells in SERS data in 10 min after antibiotic exposure with greater than 99% accuracy. Deep learning analysis is also able to differentiate responses from untreated cells with antibiotic dosages up to 10-fold lower than the minimum inhibitory concentration observed in conventional growth assays. In addition, analysis of SERS data using a generative model, a variational autoencoder, identifies spectral features in the P. aeruginosa lysate data associated with antibiotic efficacy. From this insight, a combinatorial dataset of metabolites is selected to extend the latent space of the variational autoencoder. This culture-free dataset dramatically improves classification accuracy to select effective antibiotic treatment in 30 min. Unsupervised Bayesian Gaussian mixture analysis achieves 99.3% accuracy in discriminating between susceptible versus resistant to antibiotic cultures in SERS using the extended latent space. Discriminative and generative models rapidly provide high classification accuracy with small sets of labeled data, which enormously reduces the amount of time needed to validate phenotypic AST with conventional growth assays. Thus, this work outlines a promising approach toward practical rapid AST