78 research outputs found
A Coreset-based, Tempered Variational Posterior for Accurate and Scalable Stochastic Gaussian Process Inference
We present a novel stochastic variational Gaussian process ()
inference method, based on a posterior over a learnable set of weighted pseudo
input-output points (coresets). Instead of a free-form variational family, the
proposed coreset-based, variational tempered family for s (CVTGP)
is defined in terms of the prior and the data-likelihood; hence,
accommodating the modeling inductive biases. We derive CVTGP's lower bound for
the log-marginal likelihood via marginalization of the proposed posterior over
latent coreset variables, and show it is amenable to stochastic
optimization. CVTGP reduces the learnable parameter size to ,
enjoys numerical stability, and maintains time- and
space-complexity, by leveraging a coreset-based tempered
posterior that, in turn, provides sparse and explainable representations of the
data. Results on simulated and real-world regression problems with Gaussian
observation noise validate that CVTGP provides better evidence lower-bound
estimates and predictive root mean squared error than alternative stochastic
inference methods
Identifying and mitigating biases in EHR laboratory tests
AbstractElectronic health record (EHR) data show promise for deriving new ways of modeling human disease states. Although EHR researchers often use numerical values of laboratory tests as features in disease models, a great deal of information is contained in the context within which a laboratory test is taken. For example, the same numerical value of a creatinine test has different interpretation for a chronic kidney disease patient and a patient with acute kidney injury. We study whether EHR research studies are subject to biased results and interpretations if laboratory measurements taken in different contexts are not explicitly separated. We show that the context of a laboratory test measurement can often be captured by the way the test is measured through time.We perform three tasks to study the properties of these temporal measurement patterns. In the first task, we confirm that laboratory test measurement patterns provide additional information to the stand-alone numerical value. The second task identifies three measurement pattern motifs across a set of 70 laboratory tests performed for over 14,000 patients. Of these, one motif exhibits properties that can lead to biased research results. In the third task, we demonstrate the potential for biased results on a specific example. We conduct an association study of lipase test values to acute pancreatitis. We observe a diluted signal when using only a lipase value threshold, whereas the full association is recovered when properly accounting for lipase measurements in different contexts (leveraging the lipase measurement patterns to separate the contexts).Aggregating EHR data without separating distinct laboratory test measurement patterns can intermix patients with different diseases, leading to the confounding of signals in large-scale EHR analyses. This paper presents a methodology for leveraging measurement frequency to identify and reduce laboratory test biases
From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting
Selecting the ``right'' amount of information to include in a summary is a
difficult task. A good summary should be detailed and entity-centric without
being overly dense and hard to follow. To better understand this tradeoff, we
solicit increasingly dense GPT-4 summaries with what we refer to as a ``Chain
of Density'' (CoD) prompt. Specifically, GPT-4 generates an initial
entity-sparse summary before iteratively incorporating missing salient entities
without increasing the length. Summaries generated by CoD are more abstractive,
exhibit more fusion, and have less of a lead bias than GPT-4 summaries
generated by a vanilla prompt. We conduct a human preference study on 100 CNN
DailyMail articles and find that that humans prefer GPT-4 summaries that are
more dense than those generated by a vanilla prompt and almost as dense as
human written summaries. Qualitative analysis supports the notion that there
exists a tradeoff between informativeness and readability. 500 annotated CoD
summaries, as well as an extra 5,000 unannotated summaries, are freely
available on HuggingFace
(https://huggingface.co/datasets/griffin/chain_of_density).Comment: preprin
Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile health data
The menstrual cycle is a key indicator of overall health for women of
reproductive age. Previously, menstruation was primarily studied through survey
results; however, as menstrual tracking mobile apps become more widely adopted,
they provide an increasingly large, content-rich source of menstrual health
experiences and behaviors over time. By exploring a database of user-tracked
observations from the Clue app by BioWink of over 378,000 users and 4.9 million
natural cycles, we show that self-reported menstrual tracker data can reveal
statistically significant relationships between per-person cycle length
variability and self-reported qualitative symptoms. A concern for self-tracked
data is that they reflect not only physiological behaviors, but also the
engagement dynamics of app users. To mitigate such potential artifacts, we
develop a procedure to exclude cycles lacking user engagement, thereby allowing
us to better distinguish true menstrual patterns from tracking anomalies. We
uncover that women located at different ends of the menstrual variability
spectrum, based on the consistency of their cycle length statistics, exhibit
statistically significant differences in their cycle characteristics and
symptom tracking patterns. We also find that cycle and period length statistics
are stationary over the app usage timeline across the variability spectrum. The
symptoms that we identify as showing statistically significant association with
timing data can be useful to clinicians and users for predicting cycle
variability from symptoms or as potential health indicators for conditions like
endometriosis. Our findings showcase the potential of longitudinal,
high-resolution self-tracked data to improve understanding of menstruation and
women's health as a whole.Comment: The Supplementary Information for this work, as well as the code
required for data pre-processing and producing results is available in
https://github.com/iurteaga/menstrual_cycle_analysi
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