pwc-archive/papers-with-abstracts huggingface dataset:
A Hybrid Citation Retrieval Algorithm for Evidence-based Clinical Knowledge Summarization: Combining Concept Extraction, Vector Similarity and Query Expansion for High Precision
Authors: Kalpana Raja, Andrew J Sauer, Ravi P Garg, Melanie R Klerer, Siddhartha R. Jonnalagadda
Date: 2016-09-06 00:00:00
Tasks: Clinical Knowledge, Information Retrieval, Retrieval

Novel information retrieval methods to identify citations relevant to a
clinical topic can overcome the knowledge gap existing between the primary
literature (MEDLINE) and online clinical knowledge resources such as UpToDate.
Searching the MEDLINE database directly or with query expansion methods returns
a large number of citations that are not relevant to the query. The current
study presents a citation retrieval system that retrieves citations for
evidence-based clinical knowledge summarization. This approach combines query
expansion, concept-based screening algorithm, and concept-based vector
similarity. We also propose an information extraction framework for automated
concept (Population, Intervention, Comparison, and Disease) extraction. We
evaluated our proposed system on all topics (as queries) available from
UpToDate for two diseases, heart failure (HF) and atrial fibrillation (AFib).
The system achieved an overall F-score of 41.2% on HF topics and 42.4% on AFib
topics on a gold standard of citations available in UpToDate. This is
significantly high when compared to a query-expansion based baseline (F-score
of 1.3% on HF and 2.2% on AFib) and a system that uses query expansion with
disease hyponyms and journal names, concept-based screening, and term-based
vector similarity system (F-score of 37.5% on HF and 39.5% on AFib). Evaluating
the system with top K relevant citations, where K is the number of citations in
the gold standard achieved a much higher overall F-score of 69.9% on HF topics
and 75.1% on AFib topics. In addition, the system retrieved up to 18 new
relevant citations per topic when tested on ten HF and six AFib clinical
topics.
An Information Extraction Approach to Prescreen Heart Failure Patients for Clinical Trials
Authors: Abhishek Kalyan Adupa, Ravi Prakash Garg, Jessica Corona-Cox, Sanjiv. J. Shah, Siddhartha R. Jonnalagadda
Date: 2016-09-06 00:00:00
Tasks: Decision Making

To reduce the large amount of time spent screening, identifying, and
recruiting patients into clinical trials, we need prescreening systems that are
able to automate the data extraction and decision-making tasks that are
typically relegated to clinical research study coordinators. However, a major
obstacle is the vast amount of patient data available as unstructured free-form
text in electronic health records. Here we propose an information
extraction-based approach that first automatically converts unstructured text
into a structured form. The structured data are then compared against a list of
eligibility criteria using a rule-based system to determine which patients
qualify for enrollment in a heart failure clinical trial. We show that we can
achieve highly accurate results, with recall and precision values of 0.95 and
0.86, respectively. Our system allowed us to significantly reduce the time
needed for prescreening patients from a few weeks to a few minutes. Our
open-source information extraction modules are available for researchers and
could be tested and validated in other cardiovascular trials. An approach such
as the one we demonstrate here may decrease costs and expedite clinical trials,
and could enhance the reproducibility of trials across institutions and
populations.
CRTS: A type system for representing clinical recommendations
Authors: Ravi P Garg, Kalpana Raja, Siddhartha R. Jonnalagadda
Date: 2016-09-06 00:00:00
Tasks: Articles, Clinical Knowledge, Retrieval, Vocal Bursts Type Prediction

Background: Clinical guidelines and recommendations are the driving wheels of
the evidence-based medicine (EBM) paradigm, but these are available primarily
as unstructured text and are generally highly heterogeneous in nature. This
significantly reduces the dissemination and automatic application of these
recommendations at the point of care. A comprehensive structured representation
of these recommendations is highly beneficial in this regard. Objective: The
objective of this paper to present Clinical Recommendation Type System (CRTS),
a common type system that can effectively represent a clinical recommendation
in a structured form. Methods: CRTS is built by analyzing 125 recommendations
and 195 research articles corresponding to 6 different diseases available from
UpToDate, a publicly available clinical knowledge system, and from the National
Guideline Clearinghouse, a public resource for evidence-based clinical practice
guidelines. Results: We show that CRTS not only covers the recommendations but
also is flexible to be extended to represent information from primary
literature. We also describe how our developed type system can be applied for
clinical decision support, medical knowledge summarization, and citation
retrieval. Conclusion: We showed that our proposed type system is precise and
comprehensive in representing a large sample of recommendations available for
various disorders. CRTS can now be used to build interoperable information
extraction systems that automatically extract clinical recommendations and
related data elements from clinical evidence resources, guidelines, systematic
reviews and primary publications.
  Keywords: guidelines and recommendations, type system, clinical decision
support, evidence-based medicine, information storage and retrieval
A Bootstrap Machine Learning Approach to Identify Rare Disease Patients from Electronic Health Records
Authors: Ravi Garg, Shu Dong, Sanjiv Shah, Siddhartha R. Jonnalagadda
Date: 2016-09-06 00:00:00
Tasks: BIG-bench Machine Learning

Rare diseases are very difficult to identify among large number of other
possible diagnoses. Better availability of patient data and improvement in
machine learning algorithms empower us to tackle this problem computationally.
In this paper, we target one such rare disease - cardiac amyloidosis. We aim to
automate the process of identifying potential cardiac amyloidosis patients with
the help of machine learning algorithms and also learn most predictive factors.
With the help of experienced cardiologists, we prepared a gold standard with 73
positive (cardiac amyloidosis) and 197 negative instances. We achieved high
average cross-validation F1 score of 0.98 using an ensemble machine learning
classifier. Some of the predictive variables were: Age and Diagnosis of cardiac
arrest, chest pain, congestive heart failure, hypertension, prim open angle
glaucoma, and shoulder arthritis. Further studies are needed to validate the
accuracy of the system across an entire health system and its generalizability
for other diseases.
Using Natural Language Processing to Screen Patients with Active Heart Failure: An Exploration for Hospital-wide Surveillance
Authors: Shu Dong, R Kannan Mutharasan, Siddhartha Jonnalagadda
Date: 2016-09-06 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we proposed two different approaches, a rule-based approach
and a machine-learning based approach, to identify active heart failure cases
automatically by analyzing electronic health records (EHR). For the rule-based
approach, we extracted cardiovascular data elements from clinical notes and
matched patients to different colors according their heart failure condition by
using rules provided by experts in heart failure. It achieved 69.4% accuracy
and 0.729 F1-Score. For the machine learning approach, with bigram of clinical
notes as features, we tried four different models while SVM with linear kernel
achieved the best performance with 87.5% accuracy and 0.86 F1-Score. Also, from
the classification comparison between the four different models, we believe
that linear models fit better for this problem. Once we combine the
machine-learning and rule-based algorithms, we will enable hospital-wide
surveillance of active heart failure through increased accuracy and
interpretability of the outputs.
Lie Access Neural Turing Machine
Authors: Greg Yang
Date: 2016-02-28 00:00:00

Following the recent trend in explicit neural memory structures, we present a
new design of an external memory, wherein memories are stored in an Euclidean
key space $\mathbb R^n$. An LSTM controller performs read and write via
specialized read and write heads. It can move a head by either providing a new
address in the key space (aka random access) or moving from its previous
position via a Lie group action (aka Lie access). In this way, the "L" and "R"
instructions of a traditional Turing Machine are generalized to arbitrary
elements of a fixed Lie group action. For this reason, we name this new model
the Lie Access Neural Turing Machine, or LANTM.
  We tested two different configurations of LANTM against an LSTM baseline in
several basic experiments. We found the right configuration of LANTM to
outperform the baseline in all of our experiments. In particular, we trained
LANTM on addition of $k$-digit numbers for $2 \le k \le 16$, but it was able to
generalize almost perfectly to $17 \le k \le 32$, all with the number of
parameters 2 orders of magnitude below the LSTM baseline.
Automatically extracting, ranking and visually summarizing the treatments for a disease
Authors: Prakash Reddy Putta, John J. Dzak III, Siddhartha R. Jonnalagadda
Date: 2016-09-06 00:00:00

Clinicians are expected to have up-to-date and broad knowledge of disease
treatment options for a patient. Online health knowledge resources contain a
wealth of information. However, because of the time investment needed to
disseminate and rank pertinent information, there is a need to summarize the
information in a more concise format. Our aim of the study is to provide
clinicians with a concise overview of popular treatments for a given disease
using information automatically computed from Medline abstracts. We analyzed
the treatments of two disorders - Atrial Fibrillation and Congestive Heart
Failure. We calculated the precision, recall, and f-scores of our two ranking
methods to measure the accuracy of the results. For Atrial Fibrillation
disorder, maximum f-score for the New Treatments weighing method is 0.611,
which occurs at 60 treatments. For Congestive Heart Failure disorder, maximum
f-score for the New Treatments weighing method is 0.503, which occurs at 80
treatments.
Best-Buddies Similarity - Robust Template Matching using Mutual Nearest Neighbors
Authors: Shaul Oron, Tali Dekel, Tianfan Xue, William T. Freeman, Shai Avidan
Date: 2016-09-06 00:00:00
Tasks: Template Matching

We propose a novel method for template matching in unconstrained
environments. Its essence is the Best-Buddies Similarity (BBS), a useful,
robust, and parameter-free similarity measure between two sets of points. BBS
is based on counting the number of Best-Buddies Pairs (BBPs)--pairs of points
in source and target sets, where each point is the nearest neighbor of the
other. BBS has several key features that make it robust against complex
geometric deformations and high levels of outliers, such as those arising from
background clutter and occlusions. We study these properties, provide a
statistical analysis that justifies them, and demonstrate the consistent
success of BBS on a challenging real-world dataset while using different types
of features.
Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation
Authors: Jianxu Chen, Lin Yang, Yizhe Zhang, Mark Alber, Danny Z. Chen
Date: 2016-09-05 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: 3D Medical Imaging Segmentation, Image Segmentation, Segmentation, Semantic Segmentation

Segmentation of 3D images is a fundamental problem in biomedical image
analysis. Deep learning (DL) approaches have achieved state-of-the-art
segmentation perfor- mance. To exploit the 3D contexts using neural networks,
known DL segmentation methods, including 3D convolution, 2D convolution on
planes orthogonal to 2D image slices, and LSTM in multiple directions, all
suffer incompatibility with the highly anisotropic dimensions in common 3D
biomedical images. In this paper, we propose a new DL framework for 3D image
segmentation, based on a com- bination of a fully convolutional network (FCN)
and a recurrent neural network (RNN), which are responsible for exploiting the
intra-slice and inter-slice contexts, respectively. To our best knowledge, this
is the first DL framework for 3D image segmentation that explicitly leverages
3D image anisotropism. Evaluating using a dataset from the ISBI Neuronal
Structure Segmentation Challenge and in-house image stacks for 3D fungus
segmentation, our approach achieves promising results comparing to the known
DL-based 3D segmentation approaches.
Confidence-aware Levenberg-Marquardt optimization for joint motion estimation and super-resolution
Authors: Cosmin Bercea, Andreas Maier, Thomas Köhler
Date: 2016-09-06 00:00:00
Tasks: Image Reconstruction, Motion Estimation, Multi-Frame Super-Resolution, Super-Resolution

Motion estimation across low-resolution frames and the reconstruction of
high-resolution images are two coupled subproblems of multi-frame
super-resolution. This paper introduces a new joint optimization approach for
motion estimation and image reconstruction to address this interdependence. Our
method is formulated via non-linear least squares optimization and combines two
principles of robust super-resolution. First, to enhance the robustness of the
joint estimation, we propose a confidence-aware energy minimization framework
augmented with sparse regularization. Second, we develop a tailor-made
Levenberg-Marquardt iteration scheme to jointly estimate motion parameters and
the high-resolution image along with the corresponding model confidence
parameters. Our experiments on simulated and real images confirm that the
proposed approach outperforms decoupled motion estimation and image
reconstruction as well as related state-of-the-art joint estimation algorithms.
Robust video object tracking via Bayesian model averaging based feature fusion
Authors: Yi Dai, Bin Liu
Date: 2016-04-02 00:00:00
Tasks: Object, Object Tracking, Video Object Tracking

In this article, we are concerned with tracking an object of interest in
video stream. We propose an algorithm that is robust against occlusion, the
presence of confusing colors, abrupt changes in the object feature space and
changes in object size. We develop the algorithm within a Bayesian modeling
framework. The state space model is used for capturing the temporal correlation
in the sequence of frame images by modeling the underlying dynamics of the
tracking system. The Bayesian model averaging (BMA) strategy is proposed for
fusing multi-clue information in the observations. Any number of object
features are allowed to be involved in the proposed framework. Every feature
represents one source of information to be fused and is associated with an
observation model. The state inference is performed by employing the particle
filter methods. In comparison with related approaches, the BMA based tracker is
shown to have robustness, expressivity, and comprehensibility.
Low-rank Bandits with Latent Mixtures
Authors: Aditya Gopalan, Odalric-Ambrym Maillard, Mohammadi Zaki
Date: 2016-09-06 00:00:00
Tasks: Recommendation Systems

We study the task of maximizing rewards from recommending items (actions) to
users sequentially interacting with a recommender system. Users are modeled as
latent mixtures of C many representative user classes, where each class
specifies a mean reward profile across actions. Both the user features (mixture
distribution over classes) and the item features (mean reward vector per class)
are unknown a priori. The user identity is the only contextual information
available to the learner while interacting. This induces a low-rank structure
on the matrix of expected rewards r a,b from recommending item a to user b. The
problem reduces to the well-known linear bandit when either user or item-side
features are perfectly known. In the setting where each user, with its
stochastically sampled taste profile, interacts only for a small number of
sessions, we develop a bandit algorithm for the two-sided uncertainty. It
combines the Robust Tensor Power Method of Anandkumar et al. (2014b) with the
OFUL linear bandit algorithm of Abbasi-Yadkori et al. (2011). We provide the
first rigorous regret analysis of this combination, showing that its regret
after T user interactions is $\tilde O(C\sqrt{BT})$, with B the number of
users. An ingredient towards this result is a novel robustness property of
OFUL, of independent interest.
Training LDCRF model on unsegmented sequences using Connectionist Temporal Classification
Authors: Amir Ahooye Atashin, Kamaledin Ghiasi-Shirazi, Ahad Harati
Date: 2016-06-26 00:00:00
Tasks: Classification, General Classification, Gesture Recognition, Handwriting Recognition, speech-recognition, Speech Recognition

Many machine learning problems such as speech recognition, gesture
recognition, and handwriting recognition are concerned with simultaneous
segmentation and labeling of sequence data. Latent-dynamic conditional random
field (LDCRF) is a well-known discriminative method that has been successfully
used for this task. However, LDCRF can only be trained with pre-segmented data
sequences in which the label of each frame is available apriori. In the realm
of neural networks, the invention of connectionist temporal classification
(CTC) made it possible to train recurrent neural networks on unsegmented
sequences with great success. In this paper, we use CTC to train an LDCRF model
on unsegmented sequences. Experimental results on two gesture recognition tasks
show that the proposed method outperforms LDCRFs, hidden Markov models, and
conditional random fields.
Depth Estimation Through a Generative Model of Light Field Synthesis
Authors: Mehdi S. M. Sajjadi, Rolf Köhler, Bernhard Schölkopf, Michael Hirsch
Date: 2016-09-06 00:00:00
Tasks: Depth Estimation

Light field photography captures rich structural information that may
facilitate a number of traditional image processing and computer vision tasks.
A crucial ingredient in such endeavors is accurate depth recovery. We present a
novel framework that allows the recovery of a high quality continuous depth map
from light field data. To this end we propose a generative model of a light
field that is fully parametrized by its corresponding depth map. The model
allows for the integration of powerful regularization techniques such as a
non-local means prior, facilitating accurate depth map estimation.
Towards Learning and Verifying Invariants of Cyber-Physical Systems by Code Mutation
Authors: Yuqi Chen, Christopher M. Poskitt, Jun Sun
Date: 2016-09-06 00:00:00

Cyber-physical systems (CPS), which integrate algorithmic control with
physical processes, often consist of physically distributed components
communicating over a network. A malfunctioning or compromised component in such
a CPS can lead to costly consequences, especially in the context of public
infrastructure. In this short paper, we argue for the importance of
constructing invariants (or models) of the physical behaviour exhibited by CPS,
motivated by their applications to the control, monitoring, and attestation of
components. To achieve this despite the inherent complexity of CPS, we propose
a new technique for learning invariants that combines machine learning with
ideas from mutation testing. We present a preliminary study on a water
treatment system that suggests the efficacy of this approach, propose
strategies for establishing confidence in the correctness of invariants, then
summarise some research questions and the steps we are taking to investigate
them.
Animal Classification System: A Block Based Approach
Authors: Y H Sharath Kumar, Manohar N, H K Chethan
Date: 2016-09-06 00:00:00
Tasks: Classification, General Classification

In this work, we propose a method for the classification of animal in images.
Initially, a graph cut based method is used to perform segmentation in order to
eliminate the background from the given image. The segmented animal images are
partitioned in to number of blocks and then the color texture moments are
extracted from different blocks. Probabilistic neural network and K-nearest
neighbors are considered here for classification. To corroborate the efficacy
of the proposed method, an experiment was conducted on our own data set of 25
classes of animals, which consisted of 4000 sample images. The experiment was
conducted by picking images randomly from the database to study the effect of
classification accuracy, and the results show that the K-nearest neighbors
classifier achieves good performance.
Automation of Pedestrian Tracking in a Crowded Situation
Authors: Saman Saadat, Kardi Teknomo
Date: 2016-09-06 00:00:00

Studies on microscopic pedestrian requires large amounts of trajectory data
from real-world pedestrian crowds. Such data collection, if done manually,
needs tremendous effort and is very time consuming. Though many studies have
asserted the possibility of automating this task using video cameras, we found
that only a few have demonstrated good performance in very crowded situations
or from a top-angled view scene. This paper deals with tracking pedestrian
crowd under heavy occlusions from an angular scene. Our automated tracking
system consists of two modules that perform sequentially. The first module
detects moving objects as blobs. The second module is a tracking system. We
employ probability distribution from the detection of each pedestrian and use
Bayesian update to track the next position. The result of such tracking is a
database of pedestrian trajectories over time and space. With certain prior
information, we showed that the system can track a large number of people under
occlusion and clutter scene.
Comparison of several short-term traffic speed forecasting models
Authors: John Boaz Lee, Kardi Teknomo
Date: 2016-09-06 00:00:00
Tasks: regression

The widespread adoption of smartphones in recent years has made it possible
for us to collect large amounts of traffic data. Special software installed on
the phones of drivers allow us to gather GPS trajectories of their vehicles on
the road network. In this paper, we simulate the trajectories of multiple
agents on a road network and use various models to forecast the short-term
traffic speed of various links. Our results show that traditional techniques
like multiple regression and artificial neural networks work well but simpler
adaptive models that do not require prior training also perform comparatively
well.
Multi Exit Configuration of Mesoscopic Pedestrian Simulation
Authors: Allan Lao, Kardi Teknomo
Date: 2016-09-06 00:00:00
Tasks: Q-Learning

A mesoscopic approach to modeling pedestrian simulation with multiple exits
is proposed in this paper. A floor field based on Qlearning Algorithm is used.
Attractiveness of exits to pedestrian typically is based on shortest path.
However, several factors may influence pedestrian choice of exits. Scenarios
with multiple exits are presented and effect of Q-learning rewards system on
navigation is investigated