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https://ceur-ws.org/Vol-2429/preface.pdf
KDH-2019 preface
Preface
This volume contains the papers presented at KDH-2019: The 4th International Workshop on
Knowledge Discovery in Healthcare Data held on August 10-16, 2019 in Macao.
The Knowledge Discovery in Health care Data (KDH) workshop series was established
in 2016 to bring together AI and clinical researchers, fostering collaborative discussions and
presenting AI research efforts to solve pressing problems in health care. This is the workshop’s
fourth year; held along with IJCAI in Macao, China.
There were 17 submissions. Each submission received at least 1 review, and on the average
3, by program committee members. The committee decided to accept 10 papers. Papers are
ordered in the proceedings according to the workshop schedule.
The first session included two papers setting the scene on broad themes of frameworks and
repositories. Firstly exploring the validity and authenticity of using crowdsourced annotations
for healthcare data and the need for a framework that can automatically correct incorrectly
captured annotations of outcomes. The authors argue that such functionality is particularly
relevant for Evidence based Medicine (EBM) primarily focusing on health outcomes using a
rule based chunking algorithm to recognise and fix errors/flaws. The second paper is directed
at the idea of creating a national UK repository of phenotyping algorithms in such a way that
a common standard representation is adopted. The authors draw from 70 existing phenotyping
algorithms and set out to identify what the hallmarks of the envisioned standard representation
should be and argue that they should be based on five criteria: source, terminology, validation,
format and implementation.
In the machine learning and classification session, the focus was on explainability and clas-
sification as covered by four papers using data from time series to accelerometer to images and
noisy medical records. First of the explainability papers presented a framework for deep clas-
sification models that can learn prototypical representations during training with time series
data. The authors introduce a regularising mechanism to enable direct control over whether the
learned prototypes are few and diverse, or many and granular. The next paper provides a qual-
itative comparison of two Convolution Neural Net (CNN) based feature distillation techniques
on a Diabetic Retinopathy image dataset. For many medical fields, the distillation, and hence
the explainability of machine vision methods is of great importance. The authors use DenseNet
to classify images for identifying the stage of Diabetic Retinopathy and extract the feature
maps to identify the regions of focus for a given classification instance. Of the two classification
applications the first paper focuses on text classification on veterinary narratives to identify tick
parasitism using an ensemble architecture. Here the focus is to combine domain-specific and
general word embeddings to overcome challenges with textual data. The last paper explored
how mobile accelerometer data can be used to recognise heavy drinking episodes with a view
to providing just-in time adaptive interventions to promote healthy behaviors.
The afternoon session focused on KDH applications in clinical trials, medical negligence
claims and ICU mortality prediction. The first paper in this session introduces a multi arm
bandit technique for adaptive clinical trials with the idea of an explore/exploit approach to max-
imise patient gains while finding out about new treatments. The authors argue that traditional
randomised approaches to clinical trials are often too slow for a rapidly changing modern world.
They provide an optimisation to include variation in results as a measure of success, so that
treatments that provide consistent results are preferred over those that are inconsistent even if
the mean value may be poorer. Next the application of NLP to automatically identify infor-
mation necessary for medical negligence claims is presented as a means for quickly identifying
relevant information among a large volume of longitudinally collected electronic medical record
Copyright © 2019 for this paper by its authors. Use permitted under
Creative Commons License Attribution 4.0 International (CC BY 4.0). i
KDH-2019 preface
information. The third paper in this section presents a Bidirectional LSTM mechanism that
incorporates both prior medical knowledge and intensive Care Unit (ICU) data for mortality
prediction.
The final position paper presents a vision on how to create a decision process for Self-
Management and chronic patient support. Here, the authors argue that despite the growing
prevalence of multimorbidities, current digital self-management approaches still prioritise single
conditions. Specifically, a model-aware and data-agnostic platform is presented on the basis
of a tailored self-management plan with three integral concepts - Monitoring (M) multiple
information sources to empower Predictions (P) and trigger intelligent Interventions (I).
We very much appreciate the support of the workshop chair, David Sarne, Amal El Fallah
Seghrouchni, as well as this year’s conference chair Thomas Eiter, and program chair Sarit
Kraus. Further we would like to thank Zhiguo Gong for the local arrangements.
We sincerely hope that the participants enjoyed this year’s workshop program and that
this collection of papers will inspire and encourage more AI-related research for and within
healthcare in the future.
July 29, 2019 Nirmalie Wiratunga
Aberdeen Frans Coenen
Sadiq Sani
ii