Runtime Decision Making Under Uncertainty in Autonomous Vehicles

Autonomous vehicles (AV) have the potential of not only increasing the safety, comfort and fuel efficiency in a vehicle but also utilising the road bandwidth more efficiently. This, however, will require us to build an AV control software, capable of coping with multiple sources of uncertainty that are either preexisting or introduced as a result of processing. Such uncertainty can come from many sources like a local or a distant source, for example, the uncertainty about the actual observation of the sensors of the AV or the uncertainty in the environment scenario communicated by peer vehicles respectively. For AV to function safely, this uncertainty needs to be taken into account during the decision making process. In this paper, we provide a generalised method for making safe decisions by estimating and integrating the Model and the Data uncertainties

Robust Intent Classification using Bayesian LSTM for Clinical Conversational Agents (CAs)

Conversational Agents (CAs) are software programs that replicate hu-man conversations using machine learning (ML) and natural language processing (NLP). CAs are currently being utilised for diverse clinical applications such as symptom checking, health monitoring, medical triage and diagnosis. Intent clas-sification (IC) is an essential task of understanding user utterance in CAs which makes use of modern deep learning (DL) methods. Because of the inherent model uncertainty associated with those methods, accuracy alone cannot be relied upon in clinical applications where certain errors may compromise patient safety. In this work, we employ Bayesian Long Short-Term Memory Networks (LSTMs) to calculate model uncertainty for IC, with a specific emphasis on symptom checker CAs. This method provides a certainty measure with IC prediction that can be utilised in assuring safe response from CAs. We evaluated our method on in-distribution (ID) and out-of-distribution (OOD) data and found mean uncer-tainty to be much higher for OOD data. These findings suggest that our method is robust to OOD utterances and can detect non-understanding errors in CAs

Translation, cultural adaptation and validation of the Hill Bone Compliance to High Blood Pressure Therapy Scale to Nepalese language

Background: Control of high blood pressure and prevention of cardiovascular complications among hypertensive patients depends on patients’ adherence to therapy. The Hill–Bone Compliance to High Blood Pressure Therapy Scale (HBCTS) is one of the most popular scale to assess hypertensive patients’ adherence behaviour. Unfortunately, no questionnaire in the Nepalese language is available to date to assess adherence to anti-hypertensive therapy. Aim: To translate, culturally adapt and validate the English original version of the HBCTS into Nepalese language to measure treatment adherence of Nepalese hypertensive patients. Methods: The cross-sectional study was conducted to translate, culturally adapt and validate the HBCTS into Nepalese version. The standard translation process was followed and was evaluated among 282 hypertensive patients visiting selected primary healthcare centers (PHCCs) of Kathmandu district, Nepal. Cronbach’s alpha was measured to assess the reliability of the tool. Exploratory factor analysis using principal component analysis with varimax rotation was used to evaluate structural validity. Results: The mean±SD age of 282 participants was 58.49± 12.44 years. Majority of participants were literate (75.2%), and consumed at least one anti-hypertensive medication per day (85.5%). Nearly half (42.2%) of the participants had a family history of hypertension, and almost half (48%) of them had comorbid conditions. Mean ±SD score for overall adherence was 17.85± 3.87 while those of medication taking, reduced salt taking, and appointment keeping subscales were 10.63± 2.55, 4.16± 1.12 and 3.06± 1.07, respectively. Kaiser Meyer Olkin (KMO) was found to be 0.877. Exploratory factor analysis revealed a three-component structure; however, the loading of components into medication adherence, reduced salt intake and appointment keeping constructs were not identical to the original tool. Cronbach’s alpha score for the entire HBCTS scale was 0.846. Conclusion: The translated Nepali version of the HBCTS demonstrated acceptable reliability and validity to measure adherence to antihypertensive therapy among hypertensive patients in clinical and community settings in Nepal