More Like this

1. Performance of empirical and model-based classifiers for detecting sucrase-isomaltase inhibition using the ¹³ C-sucrose breath test

   https://doi.org/10.1088/1752-7163/ad748d  

   Van_Wyk, Hannah; Lee, Gwenyth_O; Schillinger, Robert_J; Edwards, Christine_A; Morrison, Douglas_J; Brouwer, Andrew_F (September 2024, Journal of Breath Research)

   Abstract The¹³C-sucrose breath test (¹³C-SBT) has been proposed to estimate sucrase-isomaltase (SIM) activity and is a promising test for SIM deficiency, which can cause gastrointestinal symptoms, and for intestinal mucosal damage caused by gut dysfunction or chemotherapy. We previously showed how various summary measures of the¹³C-SBT breath curve reflect SIM inhibition. However, it is uncertain how the performance of these classifiers is affected by test duration. We leveraged¹³C-SBT data from a cross-over study in 16 adults who received 0, 100, and 750 mg of Reducose, an SIM inhibitor. We evaluated the performance of a pharmacokinetic-model-based classifier, $\rho$, and three empirical classifiers (cumulative percent dose recovered at 90 min (cPDR90), time to 50% dose recovered, and time to peak dose recovery rate), as a function of test duration using receiver operating characteristic (ROC) curves. We also assessed the sensitivity, specificity, and accuracy of consensus classifiers. Test durations of less than 2 h generally failed to accurately predict later breath curve dynamics. The cPDR90 classifier had the highest ROC area-under-the-curve and, by design, was robust to shorter test durations. For detecting mild SIM inhibition, $\rho$had a higher sensitivity. We recommend¹³C-SBT tests run for at least a 2 h duration. Although cPDR90 was the classifier with highest accuracy and robustness to test duration in this application, concerns remain about its sensitivity to misspecification of the CO₂production rate. More research is needed to assess these classifiers in target populations. 

   more » « less
2. Scheduling IDK classifiers with arbitrary dependences to minimize the expected time to successful classification

   https://doi.org/10.1007/s11241-023-09395-0  

   Abdelzaher, Tarek; Agrawal, Kunal; Baruah, Sanjoy; Burns, Alan; Davis, Robert I.; Guo, Zhishan; Hu, Yigong (January 2023, Real-Time Systems)

   Abstract This paper introduces and evaluates a general construct for trading off accuracy and overall execution duration in classification-based machine perception problems—namely, the generalized IDK classifier cascade . The aim is to select the optimal sequence of classifiers required to minimize the expected (i.e. average) execution duration needed to achieve successful classification, subject to a constraint on quality, and optionally a latency constraint on the worst-case execution duration. An IDK classifier is a software component that attempts to categorize each input provided to it into one of a fixed set of classes, returning “I Don’t Know” (IDK) if it is unable to do so with the required level of confidence. An ensemble of several different IDK classifiers may be available for the same classification problem, offering different trade-offs between effectiveness (i.e. the probability of successful classification) and timeliness (i.e. execution duration). A model for representing such characteristics is defined, and a method is proposed for determining the values of the model parameters for a given ensemble of IDK classifiers. Optimal algorithms are developed for sequentially ordering IDK classifiers into an IDK cascade, such that the expected duration to successfully classify an input is minimized, optionally subject to a latency constraint on the worst-case overall execution duration of the IDK cascade. The entire methodology is applied to two real-world case studies. In contrast to prior work, the methodology developed in this paper caters for arbitrary dependences between the probabilities of successful classification for different IDK classifiers. Effective practical solutions are developed considering both single and multiple processors. 

   more » « less
3. Building a Novel Ensemble Learning – Based Prediction Framework for Diagnosis of Coronary Heart Disease

   Abdullah Algahtani, Hoda El-Sayed (September 2022, International journal of intelligent systems and applications in engineering)

   Abstract:The newer technologies such as data mining, machine learning, artificial intelligence and data analytics have revolutionized medical sector in terms of using the existing big data to predict the various patterns emerging from the datasets available inthe healthcare repositories. The predictions based on the existing datasets in the healthcare sector have rendered several benefits such as helping clinicians to make accurate and informed decisions while managing the patients’ health leading to better management of patients’ wellbeing and health-care coordination. The millions of people have been affected by the coronary artery disease (CAD). There are several machine learning including ensemble learning approach and deep neural networks-based algorithms have shown promising outcomes in improving prediction accuracy for early diagnosis of CAD. This paper analyses the deep neural network variant DRN, Rider Optimization Algorithm-Neural network (RideNN) and Deep Neural Network-Fuzzy Neural Network (DNFN) with application of ensemble learning method for improvement in the prediction accuracy of CAD. The experimental outcomes showed the proposed ensemble classifier achieved the highest accuracy compared to the other machine learning models. Keywords:Heart disease prediction, Deep Residual Network (DRN), Ensemble classifiers, coronary artery disease. 

   more » « less
4. Beyond Thresholds: A General Approach to Sensor Selection for Practical Deep Learning-based HAR

   https://doi.org/10.1109/IoTDI61053.2024.00005  

   Cooper, Geffen; Marculescu, Radu (May 2024, IEEE)

   In deep learning (DL) based human activity recognition (HAR), sensor selection seeks to balance prediction accuracy and sensor utilization (how often a sensor is used). With advances in on-device inference, sensors have become tightly integrated with DL, often restricting access to the underlying model used. Given only sensor predictions, how can we derive a selection policy which does efficient classification while maximizing accuracy? We propose a cascaded inference approach which, given the prediction of any one sensor, determines whether to query all other sensors. Typically, cascades use a sequence of classifiers which terminate once the confidence of a classifier exceeds a threshold. However, a threshold-based policy for sensor selection may be suboptimal; we define a more general class of policies which can surpass the threshold. We extend to settings where little or no labeled data is available for tuning the policy. Our analysis is validated on three HAR datasets by improving upon the F1-score of a threshold policy across several utilization budgets. Overall, our work enables practical analytics for HAR by relaxing the requirement of labeled data for sensor selection and reducing sensor utilization to directly extend a sensor system’s lifetime. 

   more » « less
5. Optimally ordering IDK classifiers subject to deadlines

   https://doi.org/10.1007/s11241-022-09383-w  

   Baruah, Sanjoy; Burns, Alan; Davis, Robert I.; Wu, Yue (May 2022, Real-Time Systems)

   Abstract A classifier is a software component, often based on Deep Learning, that categorizes each input provided to it into one of a fixed set of classes. An IDK classifier may additionally output “I Don’t Know” (IDK) for certain inputs. Multiple distinct IDK classifiers may be available for the same classification problem, offering different trade-offs between effectiveness, i.e. the probability of successful classification, and efficiency, i.e. execution time. Optimal offline algorithms are proposed for sequentially ordering IDK classifiers such that the expected duration to successfully classify an input is minimized, optionally subject to a hard deadline on the maximum time permitted for classification. Solutions are provided considering independent and dependent relationships between pairs of classifiers, as well as a mix of the two. 

   more » « less