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CHIL '20: Proceedings of the ACM Conference on Health, Inference, and Learning
ACM2020 Proceeding
Publisher:
  • Association for Computing Machinery
  • New York
  • NY
  • United States
Conference:
ACM CHIL '20: ACM Conference on Health, Inference, and Learning Toronto Ontario Canada April 2 - 4, 2020
ISBN:
978-1-4503-7046-2
Published:
02 April 2020
Sponsors:
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Abstract

These proceedings are being made available in advance of the conference on April 2, 2020, the original date of ACM CHIL 2020 that was impacted by COVID-19. Research in machine learning and health requires a cross-disciplinary representation of clinicians and researchers in machine learning, health policy, causality, fairness, and other related areas. The goal of the ACM CHIL conference is to foster excellent research that addresses the unique challenges and opportunities that arise at the intersection of machine learning and health.

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research-article
Open Access
Defining admissible rewards for high-confidence policy evaluation in batch reinforcement learning

A key impediment to reinforcement learning (RL) in real applications with limited, batch data is in defining a reward function that reflects what we implicitly know about reasonable behaviour for a task and allows for robust off-policy evaluation. In ...

research-article
Open Access
Variational learning of individual survival distributions

The abundance of modern health data provides many opportunities for the use of machine learning techniques to build better statistical models to improve clinical decision making. Predicting time-to-event distributions, also known as survival analysis, ...

research-article
Open Access
Interpretable subgroup discovery in treatment effect estimation with application to opioid prescribing guidelines

The dearth of prescribing guidelines for physicians is one key driver of the current opioid epidemic in the United States. In this work, we analyze medical and pharmaceutical claims data to draw insights on characteristics of patients who are more prone ...

research-article
Open Access
Adverse drug reaction discovery from electronic health records with deep neural networks

Adverse drug reactions (ADRs) are detrimental and unexpected clinical incidents caused by drug intake. The increasing availability of massive quantities of longitudinal event data such as electronic health records (EHRs) has redefined ADR discovery as a ...

research-article
Open Access
CaliForest: calibrated random forest for health data

Real-world predictive models in healthcare should be evaluated in terms of discrimination, the ability to differentiate between high and low risk events, and calibration, or the accuracy of the risk estimates. Unfortunately, calibration is often ...

research-article
Open Access
BMM-Net: automatic segmentation of edema in optical coherence tomography based on boundary detection and multi-scale network

Retinal effusions and cysts caused by the leakage of damaged macular vessels and choroid neovascularization are symptoms of many ophthalmic diseases. Optical coherence tomography (OCT), which provides clear 10-layer cross-sectional images of the retina, ...

research-article
Open Access
Survival cluster analysis

Conventional survival analysis approaches estimate risk scores or individualized time-to-event distributions conditioned on covariates. In practice, there is often great population-level phenotypic heterogeneity, resulting from (unknown) subpopulations ...

research-article
Open Access
An adversarial approach for the robust classification of pneumonia from chest radiographs

While deep learning has shown promise in the domain of disease classification from medical images, models based on state-of-the-art convolutional neural network architectures often exhibit performance loss due to dataset shift. Models trained using data ...

research-article
Open Access
Explaining an increase in predicted risk for clinical alerts

Much work aims to explain a model's prediction on a static input. We consider explanations in a temporal setting where a stateful dynamical model produces a sequence of risk estimates given an input at each time step. When the estimated risk increases, ...

research-article
Open Access
Fast learning-based registration of sparse 3D clinical images

We introduce SparseVM, a method that registers clinical-quality 3D MR scans both faster and more accurately than previously possible. Deformable alignment, or registration, of clinical scans is a fundamental task for many clinical neuroscience studies. ...

research-article
Open Access
Multiple instance learning for predicting necrotizing enterocolitis in premature infants using microbiome data

Necrotizing enterocolitis (NEC) is a life-threatening intestinal disease that primarily affects preterm infants during their first weeks after birth. Mortality rates associated with NEC are 15-30%, and surviving infants are susceptible to multiple ...

research-article
Open Access
Hurtful words: quantifying biases in clinical contextual word embeddings

In this work, we examine the extent to which embeddings may encode marginalized populations differently, and how this may lead to a perpetuation of biases and worsened performance on clinical tasks. We pretrain deep embedding models (BERT) on medical ...

research-article
Open Access
Disease state prediction from single-cell data using graph attention networks

Single-cell RNA sequencing (scRNA-seq) has revolutionized bio-logical discovery, providing an unbiased picture of cellular heterogeneity in tissues. While scRNA-seq has been used extensively to provide insight into health and disease, it has not been ...

research-article
Open Access
Using SNOMED to automate clinical concept mapping

The International Classification of Disease (ICD) is a widely used diagnostic ontology for the classification of health disorders and a valuable resource for healthcare analytics. However, ICD is an evolving ontology and subject to periodic revisions (...

research-article
Open Access
MMiDaS-AE: multi-modal missing data aware stacked autoencoder for biomedical abstract screening

Systematic review (SR) is an essential process to identify, evaluate, and summarize the findings of all relevant individual studies concerning health-related questions. However, conducting a SR is labor-intensive, as identifying relevant studies is a ...

research-article
Open Access
Hidden stratification causes clinically meaningful failures in machine learning for medical imaging

Machine learning models for medical image analysis often suffer from poor performance on important subsets of a population that are not identified during training or testing. For example, overall performance of a cancer detection model may be high, but ...

research-article
Open Access
Interactive hybrid approach to combine machine and human intelligence for personalized rehabilitation assessment

Automated assessment of rehabilitation exercises using machine learning has a potential to improve current rehabilitation practices. However, it is challenging to completely replicate therapist's decision making on the assessment of patients with ...

research-article
Open Access
Extracting medical entities from social media

Accurately extracting medical entities from social media is challenging because people use informal language with different expressions for the same concept, and they also make spelling mistakes. Previous work either focused on specific diseases (e.g., ...

research-article
Open Access
Population-aware hierarchical bayesian domain adaptation via multi-component invariant learning

While machine learning is rapidly being developed and deployed in health settings such as influenza prediction, there are critical challenges in using data from one environment to predict in another due to variability in features. Even within disease ...

research-article
Open Access
TASTE: temporal and static tensor factorization for phenotyping electronic health records

Phenotyping electronic health records (EHR)focuses on defining meaningful patient groups (e.g., heart failure group and diabetes group) and identifying the temporal evolution of patients in those groups. Tensor factorization has been an effective tool ...

research-article
Open Access
Analyzing the role of model uncertainty for electronic health records

In medicine, both ethical and monetary costs of incorrect predictions can be significant, and the complexity of the problems often necessitates increasingly complex models. Recent work has shown that changing just the random seed is enough for otherwise ...

research-article
Open Access
Deidentification of free-text medical records using pre-trained bidirectional transformers

The ability of caregivers and investigators to share patient data is fundamental to many areas of clinical practice and biomedical research. Prior to sharing, it is often necessary to remove identifiers such as names, contact details, and dates in order ...

research-article
Open Access
MIMIC-Extract: a data extraction, preprocessing, and representation pipeline for MIMIC-III

Machine learning for healthcare researchers face challenges to progress and reproducibility due to a lack of standardized processing frameworks for public datasets. We present MIMIC-Extract, an open source pipeline for transforming the raw electronic ...

Contributors
  • MIT Computer Science & Artificial Intelligence Laboratory

Index Terms

  1. Proceedings of the ACM Conference on Health, Inference, and Learning
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    Acceptance Rates

    Overall Acceptance Rate 27 of 110 submissions, 25%
    YearSubmittedAcceptedRate
    CHIL '211102725%
    Overall1102725%