1,224 research outputs found
MedLens: Improve mortality prediction via medical signs selecting and regression interpolation
Monitoring the health status of patients and predicting mortality in advance
is vital for providing patients with timely care and treatment. Massive medical
signs in electronic health records (EHR) are fitted into advanced machine
learning models to make predictions. However, the data-quality problem of
original clinical signs is less discussed in the literature. Based on an
in-depth measurement of the missing rate and correlation score across various
medical signs and a large amount of patient hospital admission records, we
discovered the comprehensive missing rate is extremely high, and a large number
of useless signs could hurt the performance of prediction models. Then we
concluded that only improving data-quality could improve the baseline accuracy
of different prediction algorithms. We designed MEDLENS, with an automatic
vital medical signs selection approach via statistics and a flexible
interpolation approach for high missing rate time series. After augmenting the
data-quality of original medical signs, MEDLENS applies ensemble classifiers to
boost the accuracy and reduce the computation overhead at the same time. It
achieves a very high accuracy performance of 0.96% AUC-ROC and 0.81% AUC-PR,
which exceeds the previous benchmark
CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms
How to optimally dispatch orders to vehicles and how to tradeoff between
immediate and future returns are fundamental questions for a typical
ride-hailing platform. We model ride-hailing as a large-scale parallel ranking
problem and study the joint decision-making task of order dispatching and fleet
management in online ride-hailing platforms. This task brings unique challenges
in the following four aspects. First, to facilitate a huge number of vehicles
to act and learn efficiently and robustly, we treat each region cell as an
agent and build a multi-agent reinforcement learning framework. Second, to
coordinate the agents from different regions to achieve long-term benefits, we
leverage the geographical hierarchy of the region grids to perform hierarchical
reinforcement learning. Third, to deal with the heterogeneous and variant
action space for joint order dispatching and fleet management, we design the
action as the ranking weight vector to rank and select the specific order or
the fleet management destination in a unified formulation. Fourth, to achieve
the multi-scale ride-hailing platform, we conduct the decision-making process
in a hierarchical way where a multi-head attention mechanism is utilized to
incorporate the impacts of neighbor agents and capture the key agent in each
scale. The whole novel framework is named as CoRide. Extensive experiments
based on multiple cities real-world data as well as analytic synthetic data
demonstrate that CoRide provides superior performance in terms of platform
revenue and user experience in the task of city-wide hybrid order dispatching
and fleet management over strong baselines.Comment: CIKM 201
Lending Interaction Wings to Recommender Systems with Conversational Agents
Recommender systems trained on offline historical user behaviors are
embracing conversational techniques to online query user preference. Unlike
prior conversational recommendation approaches that systemically combine
conversational and recommender parts through a reinforcement learning
framework, we propose CORE, a new offline-training and online-checking paradigm
that bridges a COnversational agent and REcommender systems via a unified
uncertainty minimization framework. It can benefit any recommendation platform
in a plug-and-play style. Here, CORE treats a recommender system as an offline
relevance score estimator to produce an estimated relevance score for each
item; while a conversational agent is regarded as an online relevance score
checker to check these estimated scores in each session. We define uncertainty
as the summation of unchecked relevance scores. In this regard, the
conversational agent acts to minimize uncertainty via querying either
attributes or items. Based on the uncertainty minimization framework, we derive
the expected certainty gain of querying each attribute and item, and develop a
novel online decision tree algorithm to decide what to query at each turn.
Experimental results on 8 industrial datasets show that CORE could be
seamlessly employed on 9 popular recommendation approaches. We further
demonstrate that our conversational agent could communicate as a human if
empowered by a pre-trained large language model.Comment: NeurIPS 202
Transgenically mediated shRNAs targeting conserved regions of foot-and-mouth disease virus provide heritable resistance in porcine cell lines and suckling mice
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