330 research outputs found
NIPS - Not Even Wrong? A Systematic Review of Empirically Complete Demonstrations of Algorithmic Effectiveness in the Machine Learning and Artificial Intelligence Literature
Objective: To determine the completeness of argumentative steps necessary to
conclude effectiveness of an algorithm in a sample of current ML/AI supervised
learning literature.
Data Sources: Papers published in the Neural Information Processing Systems
(NeurIPS, n\'ee NIPS) journal where the official record showed a 2017 year of
publication.
Eligibility Criteria: Studies reporting a (semi-)supervised model, or
pre-processing fused with (semi-)supervised models for tabular data.
Study Appraisal: Three reviewers applied the assessment criteria to determine
argumentative completeness. The criteria were split into three groups,
including: experiments (e.g real and/or synthetic data), baselines (e.g
uninformed and/or state-of-art) and quantitative comparison (e.g. performance
quantifiers with confidence intervals and formal comparison of the algorithm
against baselines).
Results: Of the 121 eligible manuscripts (from the sample of 679 abstracts),
99\% used real-world data and 29\% used synthetic data. 91\% of manuscripts did
not report an uninformed baseline and 55\% reported a state-of-art baseline.
32\% reported confidence intervals for performance but none provided references
or exposition for how these were calculated. 3\% reported formal comparisons.
Limitations: The use of one journal as the primary information source may not
be representative of all ML/AI literature. However, the NeurIPS conference is
recognised to be amongst the top tier concerning ML/AI studies, so it is
reasonable to consider its corpus to be representative of high-quality
research.
Conclusion: Using the 2017 sample of the NeurIPS supervised learning corpus
as an indicator for the quality and trustworthiness of current ML/AI research,
it appears that complete argumentative chains in demonstrations of algorithmic
effectiveness are rare
Scoring rules in survival analysis
Scoring rules promote rational and good decision making and predictions by
models, this is increasingly important for automated procedures of `auto-ML'.
The Brier score and Log loss are well-established scoring rules for
classification and regression and possess the `strict properness' property that
encourages optimal predictions. In this paper we survey proposed scoring rules
for survival analysis, establish the first clear definition of `(strict)
properness' for survival scoring rules, and determine which losses are proper
and improper. We prove that commonly utilised scoring rules that are claimed to
be proper are in fact improper. We further prove that under a strict set of
assumptions a class of scoring rules is strictly proper for, what we term,
`approximate' survival losses. We hope these findings encourage further
research into robust validation of survival models and promote honest
evaluation
Use of Best Practice Alerts to Improve Adherence to Evidence-Based Screening in Pediatric Diabetes Care
Background: Youth with type 1 diabetes (T1D) are at increased risk for comorbid autoimmune conditions and long-term complications. To help with early identification of these complications, the American Diabetes Association (ADA) has published evidence-based screening guidelines. The aim of our quality improvement intervention was to improve and sustain adherence to the ADA recommended screening guidelines to \u3e90% for youth with T1D in the Texas Children’s Hospital (TCH) Diabetes Center by utilizing best practice alerts (BPA) within the electronic medical record (EMR).
Methods: In accordance with the ADA Standards of Care screening guidelines for youth with T1D, we analyzed the database of TCH patients to obtain the following baseline percentages: 1) urine microalbumin-to-creatinine ratio, 2) thyroid function screen, 3) lipid panel, and 4) retinopathy screen. In the TCH EMR, we developed BPAs to alert providers and provide decision support on ADA-based screening recommendations at each clinic encounter. Comparisons were made to screening rates for each category pre- and post-intervention.
Results: In the four years following the BPA build, the screening percentage for each category improved from a baseline of 90%, which has been maintained for three consecutive fiscal years.
Conclusions: The use of EMR-based BPAs to alert providers of the need for evidenced-based screening is effective in increasing adherence to standard of care guidelines. With this quality improvement intervention, we achieved our goal of \u3e90% for each category. Similar tools for decision support may be effectively utilized for evidence-based screening in other disease states
A theoretical and methodological framework for machine learning in survival analysis: Enabling transparent and accessible predictive modelling on right-censored time-to-event data
Survival analysis is an important field of Statistics concerned with mak- ing time-to-event predictions with ‘censored’ data. Machine learning, specifically supervised learning, is the field of Statistics concerned with using state-of-the-art algorithms in order to make predictions on unseen data. This thesis looks at unifying these two fields as current research into the two is still disjoint, with ‘classical survival’ on one side and su- pervised learning (primarily classification and regression) on the other. This PhD aims to improve the quality of machine learning research in survival analysis by focusing on transparency, accessibility, and predic- tive performance in model building and evaluation. This is achieved by examining historic and current proposals and implementations for models and measures (both classical and machine learning) in survival analysis and making novel contributions. In particular this includes: i) a survey of survival models including a crit- ical and technical survey of almost all supervised learning model classes currently utilised in survival, as well as novel adaptations; ii) a survey of evaluation measures for survival models, including key definitions, proofs and theorems for survival scoring rules that had previously been missing from the literature; iii) introduction and formalisation of composition and reduction in survival analysis, with a view on increasing transparency of modelling strategies and improving predictive performance; iv) imple- mentation of several R software packages, in particular mlr3proba for machine learning in survival analysis; and v) the first large-scale bench- mark experiment on right-censored time-to-event data with 24 survival models and 66 datasets. Survival analysis has many important applications in medical statistics, engineering and finance, and as such requires the same level of rigour as other machine learning fields such as regression and classification; this thesis aims to make this clear by describing a framework from prediction and evaluation to implementation
distr6: R6 Object-Oriented Probability Distributions Interface in R
distr6 is an object-oriented (OO) probability distributions interface
leveraging the extensibility and scalability of R6, and the speed and
efficiency of Rcpp. Over 50 probability distributions are currently implemented
in the package with `core' methods including density, distribution, and
generating functions, and more `exotic' ones including hazards and distribution
function anti-derivatives. In addition to simple distributions, distr6 supports
compositions such as truncation, mixtures, and product distributions. This
paper presents the core functionality of the package and demonstrates examples
for key use-cases. In addition this paper provides a critical review of the
object-oriented programming paradigms in R and describes some novel
implementations for design patterns and core object-oriented features
introduced by the package for supporting distr6 components.Comment: Accepted in The R Journa
Deep Learning for Survival Analysis: A Review
The influx of deep learning (DL) techniques into the field of survival
analysis in recent years, coupled with the increasing availability of
high-dimensional omics data and unstructured data like images or text, has led
to substantial methodological progress; for instance, learning from such
high-dimensional or unstructured data. Numerous modern DL-based survival
methods have been developed since the mid-2010s; however, they often address
only a small subset of scenarios in the time-to-event data setting - e.g.,
single-risk right-censored survival tasks - and neglect to incorporate more
complex (and common) settings. Partially, this is due to a lack of exchange
between experts in the respective fields.
In this work, we provide a comprehensive systematic review of DL-based
methods for time-to-event analysis, characterizing them according to both
survival- and DL-related attributes. In doing so, we hope to provide a helpful
overview to practitioners who are interested in DL techniques applicable to
their specific use case as well as to enable researchers from both fields to
identify directions for future investigation. We provide a detailed
characterization of the methods included in this review as an open-source,
interactive table: https://survival-org.github.io/DL4Survival. As this research
area is advancing rapidly, we encourage the research community to contribute to
keeping the information up to date.Comment: 24 pages, 6 figures, 2 tables, 1 interactive tabl
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