Reliable Decision-Making with Imprecise Models

The rapid growth in the deployment of autonomous systems across various sectors has generated considerable interest in how these systems can operate reliably in large, stochastic, and unstructured environments. Despite recent advances in artificial intelligence and machine learning, it is challenging to assure that autonomous systems will operate reliably in the open world. One of the causes of unreliable behavior is the impreciseness of the model used for decision-making. Due to the practical challenges in data collection and precise model specification, autonomous systems often operate based on models that do not represent all the details in the environment. Even if the system has access to a comprehensive decision-making model that accounts for all the details in the environment and all possible scenarios the agent may encounter, it may be intractable to solve this complex model optimally. Consequently, this complex, high fidelity model may be simplified to accelerate planning, introducing imprecision. Reasoning with such imprecise models affects the reliability of autonomous systems. A system\u27s actions may sometimes produce unexpected, undesirable consequences, which are often identified after deployment. How can we design autonomous systems that can operate reliably in the presence of uncertainty and model imprecision? This dissertation presents solutions to address three classes of model imprecision in a Markov decision process, along with an analysis of the conditions under which bounded-performance can be guaranteed. First, an adaptive outcome selection approach is introduced to devise risk-aware reduced models of the environment that efficiently balance the trade-off between model simplicity and fidelity, to accelerate planning in resource-constrained settings. Second, a framework that extends stochastic shortest path framework to problems with imperfect information about the goal state during planning is introduced, along with two solution approaches to solve this problem. Finally, two complementary solution approaches are presented to minimize the negative side effects of agent actions. The techniques presented in this dissertation enable an autonomous system to detect and mitigate undesirable behavior, without redesigning the model entirely

Survey of polypharmacy prescription in a tertiary care hospital, belagavi

  Polypharmacy is the use of four or more medications in one prescription or implies the prescription of too many medications for an individual. Concerns about polypharmacy include increase adverse drug reactions, drug interactions, prescribing cascade and higher cost. Objectives: To conduct a prescription survey of polypharmacy in tertiary care hospital at Belagavi. Methodology: The study was conducted in the Medicine outpatient department of a tertiary care hospital, Belagavi, after obtaining approval and clearance from the Institutional Ethics Committee. Total 83 patients were selected by Simple Random Sampling and the data were collected prospectively by direct observation in specially designed proforma containing relevant patient details like registration number, age, gender and diagnosis, disease data and drug data. Results: Out of the total sample population (N=83), 56.62% had prescriptions falling under major polypharmacy (>6 drugs), 43.37% had prescriptions categorized as minor polypharmacy (3-5 drugs). The most common age group of patients receiving prescriptions with polypharmacy was between 41 to 60 years accounting for 38.55%. Majority of the patients receiving prescriptions with polypharmacy in our study were females (59.03%) as compared to males (40.96%). Major polypharmacy is more prevalent in patients receiving treatment for Hypertension (60.24%) followed by patients with diabetes (23.67%). Conclusion: Our prescription survey portrays polypharmacy to be widely prevalent in a tertiary care setting. Specific treatment goals with certainty are the essential need for curing diseases rather than polypharmacy, which could be a possible threat of more harm than good

Risk based Optimization for Improving Emergency Medical Systems

In emergency medical systems, arriving at the incident locationa few seconds early can save a human life. Thus, this paper is motivated by the need to reduce the response time– time taken to arrive at the incident location after receivingthe emergency call — of Emergency Response Vehicles, ERVs(ex: ambulances, fire rescue vehicles) for as many requests as possible. We expect to achieve this primarily by positioning the ”right” number of ERVs at the ”right” places and at the ”right” times. Given the exponentially large action space(with respect to number of ERVs and their placement) and the stochasticity in location and timing of emergency incidents,this problem is computationally challenging. To that end, ourcontributions building on existing data-driven approaches are three fold:1. Based on real world evaluation metrics, we provide a riskbased optimization criterion to learn from past incident data. Instead of minimizing expected response time, we minimize the largest value of response time such that the risk of finding requests that have a higher value is bounded(ex: Only 10% of requests should have a response time greater than 8 minutes).2. We develop a mixed integer linear optimization formulation to learn and compute an allocation from a set of inputrequests while considering the risk criterion.3. To allow for ”live” reallocation of ambulances, we provide a decomposition method based on Lagrangian Relaxation to significantly reduce the run-time of the optimization formulation.Finally, we provide an exhaustive evaluation on real-world datasets from two asian cities that demonstrates the improvement provided by our approach over current practice and the best known approach from literature

Ursolic acid inhibits colistin efflux and curtails colistin resistant Enterobacteriaceae

Abstract Colistin resistance in Enterobacteriaceae especially Klebsiella pneumoniae and Escherichia coli is driving the evolution of pan drug resistant strains. Screening a library of 13 plant nutraceuticals led to the identification of acetyl shikonin and ursolic acid, which exhibited synergy with colistin against extremely drug resistant (XDR) clinical strains of E. coli (U3790) and K. pneumoniae (BC936). Ursolic acid caused a significant colistin MIC reversal of 16-fold in U3790 and 4-fold in BC936 strains. Ursolic acid also potentiated the bactericidal effect of colistin against both U3790 and BC936 by causing ~ 4 to 4.5 log fold decline in CFU of both clinical isolates in a time kill assay. At 2× minimum effective concentration, ursolic acid was non-toxic to zebrafish as evidenced by brain and liver enzyme profiles and by histopathology studies. In combination with colistin, ursolic acid reduced bacterial bioburden of U3790/BC936 by 1–1.58 log fold from the infected muscle tissue of zebrafish. Mechanistic explorations via studies on real time efflux, membrane potential and intracellular accumulation of dansyl chloride tagged colistin revealed that colistin efflux is inhibited by ursolic acid. In addition, ursolic acid also enhanced outer membrane permeability which probably facilitates colistin’s attack on outer and inner membranes. Our study shows that ursolic acid synergizes with colistin by inhibiting colistin efflux in Enterobacteriaceae that helps to curtail colistin resistant Enterobacteriaceae.https://deepblue.lib.umich.edu/bitstream/2027.42/148135/1/13568_2019_Article_750.pd