316,792 research outputs found
Multi-label Few-shot ICD Coding as Autoregressive Generation with Prompt
Automatic International Classification of Diseases (ICD) coding aims to
assign multiple ICD codes to a medical note with an average of 3,000+ tokens.
This task is challenging due to the high-dimensional space of multi-label
assignment (155,000+ ICD code candidates) and the long-tail challenge - Many
ICD codes are infrequently assigned yet infrequent ICD codes are important
clinically. This study addresses the long-tail challenge by transforming this
multi-label classification task into an autoregressive generation task.
Specifically, we first introduce a novel pretraining objective to generate free
text diagnoses and procedure using the SOAP structure, the medical logic
physicians use for note documentation. Second, instead of directly predicting
the high dimensional space of ICD codes, our model generates the lower
dimension of text descriptions, which then infer ICD codes. Third, we designed
a novel prompt template for multi-label classification. We evaluate our
Generation with Prompt model with the benchmark of all code assignment
(MIMIC-III-full) and few shot ICD code assignment evaluation benchmark
(MIMIC-III-few). Experiments on MIMIC-III-few show that our model performs with
a marco F1 30.2, which substantially outperforms the previous MIMIC-III-full
SOTA model (marco F1 4.3) and the model specifically designed for few/zero shot
setting (marco F1 18.7). Finally, we design a novel ensemble learner, a cross
attention reranker with prompts, to integrate previous SOTA and our best
few-shot coding predictions. Experiments on MIMIC-III-full show that our
ensemble learner substantially improves both macro and micro F1, from 10.4 to
14.6 and from 58.2 to 59.1, respectively.Comment: To be appear in AAAI202
The Complexity of Planning Problems With Simple Causal Graphs
We present three new complexity results for classes of planning problems with
simple causal graphs. First, we describe a polynomial-time algorithm that uses
macros to generate plans for the class 3S of planning problems with binary
state variables and acyclic causal graphs. This implies that plan generation
may be tractable even when a planning problem has an exponentially long minimal
solution. We also prove that the problem of plan existence for planning
problems with multi-valued variables and chain causal graphs is NP-hard.
Finally, we show that plan existence for planning problems with binary state
variables and polytree causal graphs is NP-complete
Evolving macro-actions for planning
Domain re-engineering through macro-actions (i.e. macros) provides one potential avenue for research into learning for planning. However, most existing work learns macros that are reusable plan fragments and so observable from planner behaviours online or plan characteristics offline. Also, there are learning methods that learn macros from domain analysis. Nevertheless, most of these methods explore restricted macro spaces and exploit specific features of planners or domains. But, the learning examples, especially that are used to acquire previous experiences, might not cover many aspects of the system, or might not always reflect that better choices have been made during the search. Moreover, any specific properties are not likely to be common with many planners or domains. This paper presents an offline evolutionary method that learns macros for arbitrary planners and domains. Our method explores a wider macro space and learns macros that are somehow not observable from the examples. Our method also represents a generalised macro learning framework as it does not discover or utilise any specific structural properties of planners or domains
Cooperation and Storage Tradeoffs in Power-Grids with Renewable Energy Resources
One of the most important challenges in smart grid systems is the integration
of renewable energy resources into its design. In this work, two different
techniques to mitigate the time varying and intermittent nature of renewable
energy generation are considered. The first one is the use of storage, which
smooths out the fluctuations in the renewable energy generation across time.
The second technique is the concept of distributed generation combined with
cooperation by exchanging energy among the distributed sources. This technique
averages out the variation in energy production across space. This paper
analyzes the trade-off between these two techniques. The problem is formulated
as a stochastic optimization problem with the objective of minimizing the time
average cost of energy exchange within the grid. First, an analytical model of
the optimal cost is provided by investigating the steady state of the system
for some specific scenarios. Then, an algorithm to solve the cost minimization
problem using the technique of Lyapunov optimization is developed and results
for the performance of the algorithm are provided. These results show that in
the presence of limited storage devices, the grid can benefit greatly from
cooperation, whereas in the presence of large storage capacity, cooperation
does not yield much benefit. Further, it is observed that most of the gains
from cooperation can be obtained by exchanging energy only among a few energy
harvesting sources
MC-TESTER: a universal tool for comparisons of Monte Carlo predictions for particle decays in high energy physics
Theoretical predictions in high energy physics are routinely provided in the
form of Monte Carlo generators. Comparisons of predictions from different
programs and/or different initialization set-ups are often necessary. MC-TESTER
can be used for such tests of decays of intermediate states (particles or
resonances) in a semi-automated way. Our test consists of two steps. Different
Monte Carlo programs are run; events with decays of a chosen particle are
searched, decay trees are analysed and appropriate information is stored. Then,
at the analysis step, a list of all found decay modes is defined and branching
ratios are calculated for both runs. Histograms of all scalar Lorentz-invariant
masses constructed from the decay products are plotted and compared for each
decay mode found in both runs. For each plot a measure of the difference of the
distributions is calculated and its maximal value over all histograms for each
decay channel is printed in a summary table. As an example of MC-TESTER
application, we include a test with the tau lepton decay Monte Carlo
generators, TAUOLA and PYTHIA. The HEPEVT (or LUJETS) common block is used as
exclusive source of information on the generated events.Comment: Version as published in Computer Physics Communications, 157(2004) 1,
pp 39-6
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