Symptoms are known by their companies: towards association guided disease diagnosis assistant.

Over the last few years, dozens of healthcare surveys have shown a shortage of doctors and an alarming doctor-population ratio. With the motivation of assisting doctors and utilizing their time efficiently, automatic disease diagnosis using artificial intelligence is experiencing an ever-growing demand and popularity. Humans are known by the company they keep; similarly, symptoms also exhibit the association property, i.e., one symptom may strongly suggest another symptom's existence/non-existence, and their association provides crucial information about the suffering condition. The work investigates the role of symptom association in symptom investigation and disease diagnosis process. We propose and build a virtual assistant called Association guided Symptom Investigation and Diagnosis Assistant (A-SIDA) using hierarchical reinforcement learning. The proposed A-SIDDA converses with patients and extracts signs and symptoms as per patients' chief complaints and ongoing dialogue context. We infused association-based recommendations and critic into the assistant, which reinforces the assistant for conducting context-aware, symptom-association guided symptom investigation. Following the symptom investigation, the assistant diagnoses a disease based on the extracted signs and symptoms. The assistant then diagnoses a disease based on the extracted signs and symptoms. In addition to diagnosis accuracy, the relevance of inspected symptoms is critical to the usefulness of a diagnosis framework. We also propose a novel evaluation metric called Investigation Relevance Score (IReS), which measures the relevance of symptoms inspected during symptom investigation. The obtained improvements (Diagnosis success rate-5.36%, Dialogue length-1.16, Match rate-2.19%, Disease classifier-6.36%, IReS-0.3501, and Human score-0.66) over state-of-the-art methods firmly establish the crucial role of symptom association that gets uncovered by the virtual agent. Furthermore, we found that the association guided symptom investigation greatly increases human satisfaction, owing to its seamless topic (symptom) transition.

Sustainable use of plastic-derived nanocarbons as a promising larvicidal and growth inhibitor agent towards control of mosquitoes.

Nanoscale carbon was obtained from six widely used plastics (PET, HDPE, PVC, LDPE, PP and PP) via thermal degradation (600 °C) under inert atmosphere. The thermally degraded products were processed through bath sonication followed by lyophilisation and the same was characterized through proximate analysis, UV-Vis spectroscopy, Scanning electron micrograph (SEM) with energy dispersive X-ray (EDX) analysis, Transmission electron micrograph (TEM), Dynamic light scattering (DLS) and Fourier transform infrared spectroscopy (FTIR). A series of aqueous solution of nanoscale carbon (5-30 mg/L) were prepared and same were used as both mosquito growth inhibitor and larvicidal agent against 3rd and 4th instar larvae of Culex pipiens. The significant percent mortality results were recorded for LDPE (p < 0.007) with average particle size of 3.01 nm and 62.95 W% of carbon and PS (p < 0.002) with average particle size of 12.80 nm and 58.73 W% of carbon against 3rd instar larvae, respectively. Similarly, for 4th instar larvae, both significant pupicidal and adulticidal activity were also recorded for PET (F = 24.0, p < 0.0001 and F = 5.73, p < 0.006), and HDPE (F = 26.0, p < 0.0001) and F = 5.30, p < 0.008). However, significant pupicidal activity were observed for PVC (F = 6.90, p < 0.003), and PS (F = 21.30, p < 0.0001). Histological, bio-chemical and microscopic studies were revealed that nanoscale carbon causes mild to severe damage of external and internal cellular integrity of larvae. However, nanoscale carbon does not exhibit any chromosomal abnormality and anatomical irregularities in Allium cepa and Cicer arietinum, respectively. Similarly, non-significant results with respect to blood cell deformation were also recorded from blood smear of Poecilia reticulata. Therefore, it can be concluded that plastic origin nanoscale carbon could be a viable sustainable nano-weapon towards control of insects.

A dynamic goal adapted task oriented dialogue agent.

PURPOSE: Existing virtual agents (VAs) present in dialogue systems are either information retrieval based or static goal-driven. However, in real-world situations, end-users might not have a known and fixed goal beforehand for the task, i.e., they may upgrade/downgrade/update their goal components in real-time to maximize their utility values. Existing VAs are unable to handle such dynamic goal-oriented situations. METHODOLOGY: Due to the absence of any related dialogue dataset where such choice deviations are present, we have created a conversational dataset called Deviation adapted Virtual Agent(DevVA), with the manual annotation of its corresponding intents, slots, and sentiment labels. A Dynamic Goal Driven Dialogue Agent (DGDVA) has been developed by incorporating a Dynamic Goal Driven Module (GDM) on top of a deep reinforcement learning based dialogue manager. In the course of a conversation, the user sentiment provides grounded feedback about agent behavior, including goal serving action. User sentiment appears to be an appropriate indicator for goal discrepancy that guides the agent to complete the user's desired task with gratification. The negative sentiment expressed by the user about an aspect of the provided choice is treated as a discrepancy that is being resolved by the GDM depending upon the observed discrepancy and current dialogue state. The goal update capability and the VA's interactiveness trait enable end-users to accomplish their desired task satisfactorily. FINDINGS: The obtained experimental results illustrate that DGDVA can handle dynamic goals with maximum user satisfaction and a significantly higher success rate. The interaction drives the user to decide its final goal through the latent specification of possible choices and information retrieved and provided by the dialogue agent. Through the experimental results (qualitative and quantitative), we firmly conclude that the proposed sentiment-aware VA adapts users' dynamic behavior for its goal setting with substantial efficacy in terms of primary objective i.e., task success rate (0.88). PRACTICAL IMPLICATIONS: In real world, it can be argued that many people do not have a predefined and fixed goal for tasks such as online shopping, movie booking & restaurant booking, etc. They tend to explore the available options first which are aligned with their minimum requirements and then decide one amongst them. The DGDVA provides maximum user satisfaction as it enables them to accomplish a dynamic goal that leads to additional utilities along with the essential ones. ORIGINALITY: To the best of our knowledge, this is the first effort towards the development of A Dynamic Goal Adapted Task-Oriented Dialogue Agent that can serve user goals dynamically until the user is satisfied.

Towards sentiment aided dialogue policy learning for multi-intent conversations using hierarchical reinforcement learning.

PURPOSE: Developing a Dialogue/Virtual Agent (VA) that can handle complex tasks (need) of the user pertaining to multiple intents of a domain is challenging as it requires the agent to simultaneously deal with multiple subtasks. However, majority of these end-to-end dialogue systems incorporate only user semantics as inputs in the learning process and ignore other useful user behavior and information. Sentiment of the user at the time of conversation plays an important role in securing maximum user gratification. So, incorporating sentiment of the user during the policy learning becomes even more crucial, more so when serving composite tasks of the user. METHODOLOGY: As a first step towards enabling the development of sentiment aided VA for multi-intent conversations, this paper proposes a new dataset, annotated with its corresponding intents, slot and sentiment (considering the entire dialogue history) labels, named SentiVA, collected from open-sourced dialogue datasets. In order to integrate these multiple aspects, a Hierarchical Reinforcement Learning (HRL) specifically options based VA is proposed to learn strategies for managing multi-intent conversations. Along with task success based immediate rewards, sentiment based immediate rewards are also incorporated in the hierarchical value functions to make the VA user adaptive. FINDINGS: Empirically, the paper shows that task based and sentiment based immediate rewards cumulatively are required to ensure successful task completion and attain maximum user satisfaction in a multi-intent scenario instead of any of these rewards alone. PRACTICAL IMPLICATIONS: The eventual evaluators and consumers of dialogue systems are users. Thus, to ensure a fulfilling conversational experience involving maximum user satisfaction requires VA to consider user sentiment at every time-step in its decision making policy. ORIGINALITY: This work is the first attempt in incorporating sentiment based rewards in the HRL framework.