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Diabetes is a large healthcare burden worldwide. There is substantial evidence that lifestyle modifications and drug intervention can prevent diabetes, therefore, an early identification of high risk individuals is important to design targeted prevention strategies. In this paper, we present an automatic tool that uses machine learning techniques to predict the development of type 2 diabetes mellitus (T2DM). Data generated from an oral glucose tolerance test (OGTT) was used to develop a predictive model based on the support vector machine (SVM). We trained and validated the models using the OGTT and demographic data of 1,492 healthy individuals collected during the San Antonio Heart Study. This study collected plasma glucose and insulin concentrations before glucose intake and at three time-points thereafter (30, 60 and 120 min). Furthermore, personal information such as age, ethnicity and body-mass index was also a part of the data-set. Using 11 OGTT measurements, we have deduced 61 features, which are then assigned a rank and the top ten features are shortlisted using minimum redundancy maximum relevance feature selection algorithm. All possible combinations of the 10 best ranked features were used to generate SVM based prediction models. This research shows that an individual's plasma glucose levels, and the information derived therefrom have the strongest predictive performance for the future development of T2DM. Significantly, insulin and demographic features do not provide additional performance improvement for diabetes prediction. The results of this work identify the parsimonious clinical data needed to be collected for an efficient prediction of T2DM. Our approach shows an average accuracy of 96.80% and a sensitivity of 80.09% obtained on a holdout set., Competing Interests: The authors have declared that no competing interests exist.

• Electrocardiogram (ECG) signals can be a potential alternative to continuous glucose monitoring (CGM) devices for detecting glycemic excursions. This includes hypoglycemia as well as hyperglycemia. • A combination of intra-beat (morphological features) and inter-beat (heart-rate variability features) information improves prediction performance compared to their independent use. • Changes in morphological patterns occur at varying glucose levels for different patients and at different times. • A personalized fusion-based approach combining morphological patterns at varying glucose levels leads to improved prediction performance over baseline models (trained only at conventional excursions thresholds). Monitoring glucose levels is critical for effective diabetes management. Continuous glucose monitoring devices estimate interstitial glucose levels and provide alerts for glycemic excursions. However, they are expensive and invasive. Therefore, low-cost, noninvasive alternatives are useful for patients with diabetes. In this article, we explore electrocardiogram signals as a potential alternative to detecting glycemic excursions by extracting intra-beat (beat-morphology) and inter-beat (heart rate variability) information. Unlike prior methods that focused only on the standard clinical excursion thresholds (70 mg/dL for hypoglycemia, 180 mg/dL for hyperglycemia), our proposed approach trains independent machine learning models at various excursion thresholds, aggregating their outputs for a final prediction. This allows learning morphological patterns in the neighborhood of the standard excursion thresholds. Our personalized fusion models achieve an AUC of 75 % for hypoglycemia and 78 % for hyperglycemia detection across patients, resulting in an average improvement of 4 % compared to the baseline models (trained using only standard clinical thresholds) for detecting glycemic excursions. We also find that combining morphology and HRV information outperforms using them individually (5 % for hypoglycemia and 6 % for hyperglycemia). The data used in this article was collected from 12 patients with type-1 diabetes, each monitored over a 14-day period at Texas Children's Hospital, Houston. The results indicate that a combination of morphological and HRV features is essential for noninvasive detection of glycemic excursions. Also, morphological changes can happen at varying glucose levels for different patients and capturing these changes provide valuable information that leads to improved prediction performance for detecting glycemic excursions. [ABSTRACT FROM AUTHOR]

The impact of infectious disease on human populations is a function of many factors including environmental conditions, vector dynamics, transmission mechanics, social and cultural behaviors, and public policy. A comprehensive framework for disease management must fully connect the complete disease lifecycle, including emergence from reservoir populations, zoonotic vector transmission, and impact on human societies. The Framework for Infectious Disease Analysis is a software environment and conceptual architecture for data integration, situational awareness, visualization, prediction, and intervention assessment. Framework for Infectious Disease Analysis automatically collects biosurveillance data using natural language processing, integrates structured and unstructured data from multiple sources, applies advanced machine learning, and uses multi-modeling for analyzing disease dynamics and testing interventions in complex, heterogeneous populations. In the illustrative case studies, natural language processing from social media, news feeds, and websites was used for information extraction, biosurveillance, and situation awareness. Classification machine learning algorithms (support vector machines, random forests, and boosting) were used for disease predictions. [ABSTRACT FROM AUTHOR]

Fatigue is defined as "a loss of force-generating capacity" in a muscle that can intensify tremor. Tremor quantification can facilitate early detection of fatigue onset so that preventative or corrective controls can be taken to minimize work-related injuries and improve the performance of tasks that require high-levels of accuracy. We focused on developing a system that recognizes and classifies voluntary effort and detects phases of fatigue. The experiment was designed to extract and evaluate hand-tremor data during the performance of both rest and effort tasks. The data were collected from the wrist and finger of the participant's dominant hand. To investigate tremor, time, frequency domain features were extracted from the accelerometer signal for segments of 45 and 90 samples/window. Analysis using advanced signal processing and machine-learning techniques such as decision tree, k-nearest neighbor, support vector machine, and ensemble classifiers were applied to discover models to classify rest and effort tasks and the phases of fatigue. Evaluation of the classifier's performance was assessed based on various metrics using 5-fold cross-validation. The recognition of rest and effort tasks using an ensemble classifier based on the random subspace and window length of 45 samples was deemed to be the most accurate (96.1%). The highest accuracy (~98%) that distinguished between early and late fatigue phases was achieved using the same classifier and window length. [ABSTRACT FROM AUTHOR]

Most incidents in complex systems such as process plants are not-chance events and weak signals emerge for a long time before incidents occur. It is necessary to identify and respond to the weak signals as early as possible for preventing incidents. However, in the era of Industry 4.0, recognizing weak signals from the abundance of data is challenging. Since the terminology "weak signal" was not precisely defined, the study first proposed a formal definition of "weak signal" and discussed its characteristics. Additionally, a framework was developed to address the challenges of observing, evaluating, and responding to weak signals in complex systems. The framework first utilized Function Resonance Analysis Method (FRAM) to determine the information to be collected for observing weak signals. Then, the relevance of weak signals and the corresponding responses were evaluated by utilizing Balanced Random Forest (BRF), Weighted Random Forest (WRF) and Decision Tree (DT) classification models. The case study of a hypothetical batch process showed great potentials for applying the framework in real operations. Based on the potential weak signals that were indicated by FRAM, probabilities of temperature deviations in the process were predicted with high accuracy by the optimal BRF model. Underlying weak signals and the corresponding responses to reduce the probabilities were identified from the DT. [ABSTRACT FROM AUTHOR]

Most incidents in complex systems such as process plants are not-chance events and weak signals emerge for a long time before incidents occur. It is necessary to identify and respond to the weak signals as early as possible for preventing incidents. However, in the era of Industry 4.0, recognizing weak signals from the abundance of data is challenging. Since the terminology "weak signal" was not precisely defined, the study first proposed a formal definition of "weak signal" and discussed its characteristics. Additionally, a framework was developed to address the challenges of observing, evaluating, and responding to weak signals in complex systems. The framework first utilized Function Resonance Analysis Method (FRAM) to determine the information to be collected for observing weak signals. Then, the relevance of weak signals and the corresponding responses were evaluated by utilizing Balanced Random Forest (BRF), Weighted Random Forest (WRF) and Decision Tree (DT) classification models. The case study of a hypothetical batch process showed great potentials for applying the framework in real operations. Based on the potential weak signals that were indicated by FRAM, probabilities of temperature deviations in the process were predicted with high accuracy by the optimal BRF model. Underlying weak signals and the corresponding responses to reduce the probabilities were identified from the DT. [ABSTRACT FROM AUTHOR]