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Title: Relational Multi-Instance Learning for Concept Annotation from Medical Time Series

Paper content:
Recent advances in computing technology and sensor design have made it easier to collect longitudinal or time series data from patients, resulting in a gigantic amount of available medical data.
Most of the medical time series lack annotations or even when the annotations are available they could be subjective and prone to human errors.
Earlier works have developed natural language processing techniques to extract concept annotations and/or clinical narratives from doctor notes.
However, these approaches are slow and do not use the accompanying medical time series data.
To address this issue, we introduce the problem of concept annotation for the medical time series data, i.e., the task of predicting and localizing medical concepts by using the time series data as input.
We propose Relational Multi-Instance Learning (RMIL) - a deep Multi Instance Learning framework based on recurrent neural networks, which uses pooling functions and attention mechanisms for the concept annotation tasks.
Empirical results on medical datasets show that our proposed models outperform various multi-instance learning models.
Clinicians have limited time (e.g., only a few minutes BID31 ) to study and treat each patient.
However, they are overloaded with a lot of patient data from multiple sources and in various formats, such as patient medical history and doctor's notes in free-flowing text, vitals and monitoring data which are captured as time series, and prescriptions and drugs which appear as medical codes including ICD-9 (Organization & Corporation, 1998) , LOINC codes BID21 , etc.
This rich information should be summarized and available to clinicians in easily digestible format for faster diagnosis and treatment.
Graphical visualizations BID54 are a popular approach to show patient data to doctors.
However, recent studies have shown that graphical visualisations are not always helpful for clinicians' decision-making BID46 BID63 .
Text summaries on the other hand are widely embraced and are usually adopted in practice BID58 .
Most existing systems use natural language processing techniques (Afantenos et al., 2005; BID23 to generate summaries from doctor notes which include test results, discharge reports, observational notes, etc.
While these systems are useful, they only use one source of data, i.e., doctor's notes which might have noisy and erroneous entries, for text summarization.
On the other hand, electronic health records have other sources of patient data such as vital signs, monitoring sensors, and lab results in the form of multivariate time-series, which can be more accurate and may contain rich information about patient's conditions.
Few existing patient summarization systems actually extract information directly from these time series for concept prediction and/or summarization.
Generating simple text summaries such as trends from time series has been investigated before BID61 but is marginally useful since these trends are not mapped to the medical concepts which clinicians can quickly comprehend.
Recent works BID52 BID16 BID48 BID14 have successfully shown that clinical events and outcomes can be predicted using medical codes or clinical time series data.
However, directly obtaining medical concept annotations and summaries from the time series data is still an open question.In this work, we introduce the concept annotation task as the problem of predicting and localizing the medical concepts by modeling the related medical time series data.
FIG0 illustrates a concept annotation example where medical time series data such as heart rate, pH and blood gas pressure are given, and the goal is to predict the time series of concepts such as intubation, extubation and resuscitate.
To solve concept annotation problem, we formulate it as a Multi-Instance Learning (MIL) problem BID20 and propose a deep learning based framework called Relational MultiInstance Learning (RMIL).
RMIL uses Recurrent Neural Networks (RNNs) to model multivariate time series data, leverages instance relations via attention mechanisms, and provides concept predictions using pooling functions.The main contributions of our work are the following.
We present a unified view of the MIL approaches for time series data using RNN models with different pooling functions and attention mechanisms.
We show that our RMIL model is capable of learning a good classifier for concept detection (bag label predictions) and concept localization tasks (instance label prediction), even though it is only trained using bag labels.
We demonstrate that RMIL obtains promising results on real-world medical datasets and outperforms popular MIL approaches.The rest of the paper is structured as follows.
In the following section, we briefly discuss the related works.
Afterwards, we describe MIL framework and describe how RNN can be combined with multi-instance learning framework to obtain our proposed RMIL.
In Sections 4 and 5, we present experimental results and conclusions respectively.
In the appendix, we demonstrate anomaly detection as another application of our RMIL framework.
We can study the interpretability of concept localization by looking at the localization results of our RMIL models, even though the model is trained without the labels for localization.
FIG3 shows the ground truth annotations of two respiratory concepts -intubation and extubation concepts, and the prediction probabilities of these concepts obtained by our RMIL attention-based LSTM models.
From figure 2(a
) we can make the following observations, (
i) intubation usually happens before extubation for the same patient,
(ii) intubation and extubation could happen on the same day, and
(iii) intubation and extubation occur commonly within the first 24 hours of admission.
From the figure 2(b), we see that our RMIL attention based LSTM predicts that the probability of intubation happening on the first day of admission is higher (draker gray means higher probability of concept occurrence) and the probability of extubation happening within first day is lower.
This indicates that the model has correctly learnt that intubation should appear before extubation.
This also implicitly implies that the RMIL attention-based LSTM models have correctly learnt the instance-level relationships from the medical time series data with only bag-level labels.

assistant: text: Proposes a novel Multiple Instance Learning (MIL) formulation called Relation MIL (RMIL), and discussed a number of its variants with LSTM, Bi-LSTM, S2S, etc. and explores integrating RMIL with various attention mechanisms, and demonstrates its usage on medical concept prediction from time series data.