Acute respiratory distress syndrome (ARDS) is a devastating critical care syndrome with significant morbidity and mortality. The objective of this study was to evaluate the predictive values of dynamic clinical indices by developing machine-learning (ML) models for early and accurate clinical assessment of the disease prognosis of ARDS. We conducted a retrospective observational study by applying dynamic clinical data collected in the ARDSNet FACTT Trial (n = 1000) to ML-based algorithms for predicting mortality. In order to compare the significance of clinical features dynamically, we further applied the random forest (RF) model to nine selected clinical parameters acquired at baseline and day 3 independently. An RF model trained using clinical data collected at day 3 showed improved performance and prognostication efficacy (area under the curve [AUC]: 0.84, 95% CI: 0.78–0.89) compared to baseline with an AUC value of 0.72 (95% CI: 0.65–0.78). Mean airway pressure (MAP), bicarbonate, age, platelet count, albumin, heart rate, and glucose were the most significant clinical indicators associated with mortality at day 3. Thus, clinical features collected early (day 3) improved performance of integrative ML models with better prognostication for mortality. Among these, MAP represented the most important feature for ARDS patients' early risk stratification.
Noninvasive behavioral tracking of animals during experiments is crucial to many scientific pursuits. Extracting the poses of animals without using markers is often essential for measuring behavioral effects in biomechanics, genetics, ethology & neuroscience. Yet, extracting detailed poses without markers in dynamically changing backgrounds has been challenging. We recently introduced an open source toolbox called DeepLabCut that builds on a state-of-the-art human pose estimation algorithm to allow a user to train a deep neural network using limited training data to precisely track user-defined features that matches human labeling accuracy. Here, with this paper we provide an updated toolbox that is self contained within a Python package that includes new features such as graphical user interfaces and active-learning based network refinement. Lastly, we provide a step-by-step guide for using DeepLabCut.
The nature of the relationship between serum thyrotropin (TSH) levels and higher cognitive abilities is unclear, especially within the normal reference range and in the younger population.To assess the relationship between serum TSH levels and mental health and sleep quality parameters (fluid intelligence [Gf], MMSE (Mini-Mental State Examination), depression scores, and, finally, Pittsburgh Sleep Quality Index (PSQI) scores (working memory, processing speed, and executive function) in young adults.This was a retrospective analysis of the data from the Human Connectome Project (HCP). The HCP consortium is seeking to map human brain circuits systematically and identify their relationship to behavior in healthy adults. Included were 391 female and 412 male healthy participants aged 22-35 years at the time of the screening interview. We excluded persons with serum TSH levels outside the reference range (0.4-4.5 mU/L). TSH was transformed logarithmically (log TSH). All the key variables were normalized and then linear regression analysis was performed to assess the relationship between log TSH as a cofactor and Gf as the dependent variable. Finally, a machine learning method, random forest regression, predicted Gf from the dependent variables (including alcohol and tobacco use). The main outcome was normalized Gf (nGf) and Gf scores.Log TSH was a significant co-predictor of nGF in females (β = 0.31(±0.1), P < .01) but not in males. Random forest analysis showed that the model(s) had a better predictive value for females (r = 0.39, mean absolute error [MAE] = 0.81) than males (r = 0.24, MAE = 0.77).Higher serum TSH levels might be associated with higher Gf scores in young women.
Waist circumference (cm) Arm circumference (cm) Thigh circumference (cm) Subscapular skin thickness (mm) Triceps skin (mm) Weight (in kg) BMI (kg/m2) Pulse rate (/min) Head fat (gm) Head lean mass(gm) Head fat % Left arm fat % Left leg fat % Right arm fat ( gm) Right leg fat % Thoracic BMD (bone mineral density) Lumbar BMD (gm/cm2) Pelvis BMD (gm/cm2) Truncal fat mass (gm) Truncal lean mass (gm) Total truncal mass (gm) Truncal fat % Subtotal lean mass (gm) Subtotal mass (gm) Subtotal fat % Total BMD (gm/cm2) Total fat mass (gm) Total fat % Total lean mass (gm) Age (in years) Gender The findings show us that though age is the most important factor in the determination of BP, body composition metrics are superior to BMI and weight as features of importance. The results of our study are shown in Figure 1. Stacking regression achieved the highest R2 value and lowest MAE (Mean Absolute Error) for predicting the average systolic and diastolic blood pressure. Our study did not include categorical variables like history of smoking, presence of other factors like anti-hypertensive drug use, ethnicity, etc that might limit the overall significance. Certain important determinants of cardiovascular risk like pulse pressure could not be used, since they are derived from systolic and diastolic blood pressure. Our study, however, is able to show that DXA measured body composition is a relatively inexpensive tool, associated with a low level of radiation exposure, that might be useful in identifying population at risk for developing hypertension, especially when traditional markers like BMI and waist circumference might not be adequate in capturing the risk. None.
Mediation analysis is used to investigate the role of intermediate variables (mediators) that lie in the path between an exposure and an outcome variable. While significant research has focused on developing methods for assessing the influence of mediators on the exposure-outcome relationship, current approaches do not easily extend to settings where the mediator is high-dimensional. These situations are becoming increasingly common with the rapid increase of new applications measuring massive numbers of variables, including brain imaging, genomics, and metabolomics. In this work, we introduce a novel machine learning based method for identifying high dimensional mediators. The proposed algorithm iterates between using a machine learning model to map the high-dimensional mediators onto a lower-dimensional space, and using the predicted values as input in a standard three-variable mediation model. Hence, the machine learning model is trained to maximize the likelihood of the mediation model. Importantly, the proposed algorithm is agnostic to the machine learning model that is used, providing significant flexibility in the types of situations where it can be used. We illustrate the proposed methodology using data from two functional Magnetic Resonance Imaging (fMRI) studies. First, using data from a task-based fMRI study of thermal pain, we combine the proposed algorithm with a deep learning model to detect distributed, network-level brain patterns mediating the relationship between stimulus intensity (temperature) and reported pain at the single trial level. Second, using resting-state fMRI data from the Human Connectome Project, we combine the proposed algorithm with a connectome-based predictive modeling approach to determine brain functional connectivity measures that mediate the relationship between fluid intelligence and working memory accuracy. In both cases, our multivariate mediation model links exposure variables (thermal pain or fluid intelligence), high dimensional brain measures (single-trial brain activation maps or resting-state brain connectivity) and behavioral outcomes (pain report or working memory accuracy) into a single unified model. Using the proposed approach, we are able to identify brain-based measures that simultaneously encode the exposure variable and correlate with the behavioral outcome. I. HIGHLIGHTS Current methods for assessing mediation do not easily extend to high dimensions We introduce a new approach for performing high-dimensional mediation analysis Links high-dimensional mediator to path analysis model via machine learning algorithm Method illustrated using data from two fMRI studies
Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles.
Diabetes mellitus is associated with increased cardiovascular disease (CVD) related morbidity, mortality and death. Exercise capacity in persons with type 2 diabetes has been shown to be predictive of cardiovascular events. In this study, we used the data from the prospective randomized LOOK AHEAD study and used machine learning algorithms to help predict exercise capacity (measured in Mets) from the baseline data that included cardiovascular history, medications, blood pressure, demographic information, anthropometric and Dual-energy X-Ray Absorptiometry (DXA) measured body composition metrics. We excluded variables with high collinearity and included DXA obtained Subtotal (total minus head) fat percentage and Subtotal lean mass (gms). Thereafter, we used different machine learning methods to predict maximum exercise capacity. The different machine learning models showed a strong predictive performance for both females and males. Our study shows that using baseline data from a large prospective cohort, we can predict maximum exercise capacity in persons with diabetes mellitus. We show that subtotal fat percentage is the most important feature for predicting the exercise capacity for males and females after accounting for other important variables. Until now, BMI and waist circumference were commonly used surrogates for adiposity and there was a relative under-appreciation of body composition metrics for understanding the pathophysiology of CVD. The recognition of body fat percentage as an important marker in determining CVD risk has prognostic implications with respect to cardiovascular morbidity and mortality.
