Use of ivabradine in supraventricular tachycardia caused by refractory focal atrial tachycardia in neonates to avoid radiofrequency ablation

Supraventricular tachycardia (SVT) is a clinical condition caused by many arrhythmias and from different etiologies. Any arrhythmogenic focus above the ventricles due to reentrant or isolated ectopic focus can cause SVT. Neonates usually tolerate tachy- and bradyarrhythmias better than any other age groups. In SVT, the signs of cardiac failure appear after at least 36–48 hrs. We are here presenting a case report of SVT caused by unifocal atrial ectopic focus and treated by ivabradine as it was not responding to usual antiarrhythmic drugs. Literature showing the usage of ivabradine in SVT in pediatric age group is scarce; therefore, we are reporting this case

TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations

Machine Learning (ML) models are increasingly used to make critical decisions in real-world applications, yet they have become more complex, making them harder to understand. To this end, researchers have proposed several techniques to explain model predictions. However, practitioners struggle to use these explainability techniques because they often do not know which one to choose and how to interpret the results of the explanations. In this work, we address these challenges by introducing TalkToModel: an interactive dialogue system for explaining machine learning models through conversations. Specifically, TalkToModel comprises of three key components: 1) a natural language interface for engaging in conversations, making ML model explainability highly accessible, 2) a dialogue engine that adapts to any tabular model and dataset, interprets natural language, maps it to appropriate explanations, and generates text responses, and 3) an execution component that constructs the explanations. We carried out extensive quantitative and human subject evaluations of TalkToModel. Overall, we found the conversational system understands user inputs on novel datasets and models with high accuracy, demonstrating the system's capacity to generalize to new situations. In real-world evaluations with humans, 73% of healthcare workers (e.g., doctors and nurses) agreed they would use TalkToModel over baseline point-and-click systems for explainability in a disease prediction task, and 85% of ML professionals agreed TalkToModel was easier to use for computing explanations. Our findings demonstrate that TalkToModel is more effective for model explainability than existing systems, introducing a new category of explainability tools for practitioners. Code & demo released here: https://github.com/dylan-slack/TalkToModel.Comment: Pre-print; comments welcome! Reach out to [email protected] v3 update title and abstrac

Towards Robust Off-Policy Evaluation via Human Inputs

Off-policy Evaluation (OPE) methods are crucial tools for evaluating policies in high-stakes domains such as healthcare, where direct deployment is often infeasible, unethical, or expensive. When deployment environments are expected to undergo changes (that is, dataset shifts), it is important for OPE methods to perform robust evaluation of the policies amidst such changes. Existing approaches consider robustness against a large class of shifts that can arbitrarily change any observable property of the environment. This often results in highly pessimistic estimates of the utilities, thereby invalidating policies that might have been useful in deployment. In this work, we address the aforementioned problem by investigating how domain knowledge can help provide more realistic estimates of the utilities of policies. We leverage human inputs on which aspects of the environments may plausibly change, and adapt the OPE methods to only consider shifts on these aspects. Specifically, we propose a novel framework, Robust OPE (ROPE), which considers shifts on a subset of covariates in the data based on user inputs, and estimates worst-case utility under these shifts. We then develop computationally efficient algorithms for OPE that are robust to the aforementioned shifts for contextual bandits and Markov decision processes. We also theoretically analyze the sample complexity of these algorithms. Extensive experimentation with synthetic and real world datasets from the healthcare domain demonstrates that our approach not only captures realistic dataset shifts accurately, but also results in less pessimistic policy evaluations.Comment: 10 pages, 5 figures, 1 table. Appeared at AIES '22: Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society. Expanded version of arXiv:2103.1593

Congenital absence of the sternum in a neonate

Congenital absence of the sternum is a rare chest wall malformation resulting from the failure of midline fusion during embryonic development. It is a potentially life-threatening congenital midline defect. Only sporadic cases have been reported in literature. The abnormality can cause significant morbidity, and like other congenital anomalies can have associated defects. Repair of congenital absence of the sternum should ideally be undertaken in the neonatal period when the chest wall is highly compliant, and hence, primary closure can thus be achieved without significant cardiopulmonary compression. As the patient ages, chest wall compliance decreases and closure will become progressively difficult as venous return and lung compliance are increasingly compromised. We report a case of congenital absence of the sternum as it is very rare and because it was successfully operated in a neonate period

Congenital varicella syndrome in a neonate

Congenital varicella syndrome is an extremely rare disorder occurring in <2% of the babies born to women infected with varicella between 7 and 28 weeks of pregnancy. The characteristic symptoms consist of skin lesions in a dermatomal distribution, neurological defects, eye diseases, and skeletal anomalies. We present a case of a newborn male baby who was shifted to neonatal intensive care unit

Post Hoc Explanations of Language Models Can Improve Language Models

Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex tasks. Moreover, recent research has shown that incorporating human-annotated rationales (e.g., Chain-of- Thought prompting) during in-context learning can significantly enhance the performance of these models, particularly on tasks that require reasoning capabilities. However, incorporating such rationales poses challenges in terms of scalability as this requires a high degree of human involvement. In this work, we present a novel framework, Amplifying Model Performance by Leveraging In-Context Learning with Post Hoc Explanations (AMPLIFY), which addresses the aforementioned challenges by automating the process of rationale generation. To this end, we leverage post hoc explanation methods which output attribution scores (explanations) capturing the influence of each of the input features on model predictions. More specifically, we construct automated natural language rationales that embed insights from post hoc explanations to provide corrective signals to LLMs. Extensive experimentation with real-world datasets demonstrates that our framework, AMPLIFY, leads to prediction accuracy improvements of about 10-25% over a wide range of tasks, including those where prior approaches which rely on human-annotated rationales such as Chain-of-Thought prompting fall short. Our work makes one of the first attempts at highlighting the potential of post hoc explanations as valuable tools for enhancing the effectiveness of LLMs. Furthermore, we conduct additional empirical analyses and ablation studies to demonstrate the impact of each of the components of AMPLIFY, which, in turn, lead to critical insights for refining in-context learning

To study the impact of unilateral breast massage on milk volume among postnatal mothers - A quasi-experimental study

Background: Breast massage is known to increase the volume of breast milk. This is known to occur through stimulation of oxytocin and prolactin. None of the studies have been done which looked at the local effects of breast massage. Objective: The objective was to study the impact of unilateral breast massage on breast milk output among postnatal mothers. Materials and Methods: This quasi-experimental study was conducted in a tertiary health-care hospital, Telangana. Postnatal mothers who approached the center’s milk bank for expressing breast milk were included in the study. The enrolled mothers were shown video of breast massage and also demonstrated the technique of breast massage using breast module by the lactational counsellors of the milk bank, at the hospital in Hyderabad. All the mothers were asked to do massage to the left breast for 10 min and later were asked to express breast milk separately from both breast using electrical hospital grade breast pump. The volume of milk produced from both the breasts was recorded separately at the third session. Results: A total of 42 postnatal mothers were enrolled in the study. The median volume of breast milk expressed from the left breast after breast massage was 22.5 ml (10,30) and the median volume of breast milk expressed from the right breast without breast massage was 15 ml (10,25). The volume of breast milk produced from the side of breast massage was significantly higher when compared to unmassaged side with p<0.001. Conclusion: Breast massage increases the volume of breast milk production. If this increase in breast milk production is due to oxytocin and prolactin, then breast massage on one side should have its effect equally on both the breasts. However, in our study, the volume of milk produced on the massaged side was significantly higher than the unmassaged side. Hence, apart from oxytocin and prolactin, there may be some other local factors responsible for increased milk secretion which requires further research