128 research outputs found
Modeling Rare Interactions in Time Series Data Through Qualitative Change: Application to Outcome Prediction in Intensive Care Units
Many areas of research are characterised by the deluge of large-scale
highly-dimensional time-series data. However, using the data available for
prediction and decision making is hampered by the current lag in our ability to
uncover and quantify true interactions that explain the outcomes.We are
interested in areas such as intensive care medicine, which are characterised by
i) continuous monitoring of multivariate variables and non-uniform sampling of
data streams, ii) the outcomes are generally governed by interactions between a
small set of rare events, iii) these interactions are not necessarily definable
by specific values (or value ranges) of a given group of variables, but rather,
by the deviations of these values from the normal state recorded over time, iv)
the need to explain the predictions made by the model. Here, while numerous
data mining models have been formulated for outcome prediction, they are unable
to explain their predictions.
We present a model for uncovering interactions with the highest likelihood of
generating the outcomes seen from highly-dimensional time series data.
Interactions among variables are represented by a relational graph structure,
which relies on qualitative abstractions to overcome non-uniform sampling and
to capture the semantics of the interactions corresponding to the changes and
deviations from normality of variables of interest over time. Using the
assumption that similar templates of small interactions are responsible for the
outcomes (as prevalent in the medical domains), we reformulate the discovery
task to retrieve the most-likely templates from the data.Comment: 8 pages, 3 figures. Accepted for publication in the European
Conference of Artificial Intelligence (ECAI 2020
Quantifying Health Inequalities Induced by Data and AI Models
AI technologies are being increasingly tested and
applied in critical environments including healthcare. Without an effective way to detect and mitigate AI induced inequalities, AI might do more
harm than good, potentially leading to the widening of underlying inequalities. This paper proposes
a generic allocation-deterioration framework for
detecting and quantifying AI induced inequality.
Specifically, AI induced inequalities are quantified
as the area between two allocation-deterioration
curves. To assess the framework’s performance, experiments were conducted on ten synthetic datasets
(N>33,000) generated from HiRID - a real-world
Intensive Care Unit (ICU) dataset, showing its ability to accurately detect and quantify inequality proportionally to controlled inequalities. Extensive
analyses were carried out to quantify health inequalities (a) embedded in two real-world ICU
datasets; (b) induced by AI models trained for two
resource allocation scenarios. Results showed that
compared to men, women had up to 33% poorer deterioration in markers of prognosis when admitted
to HiRID ICUs. All four AI models assessed were
shown to induce significant inequalities (2.45% to
43.2%) for non-White compared to White patients.
The models exacerbated data embedded inequalities significantly in 3 out of 8 assessments, one of
which was >9 times worse
Edinburgh_UCL_Health@ SMM4H'22:From Glove to Flair for handling imbalanced healthcare corpora related to Adverse Drug Events, Change in medication and self-reporting vaccination
This paper reports on the performance of Edin-burgh_UCL_Health’s models in the Social Media Mining for Health (SMM4H) 2022 shared tasks. Our team participated in the tasks related to the Identification of Adverse Drug Events (ADEs), the classification of change in medication (change-med) and the classification of selfreport of vaccination (self-vaccine). Our best performing models are based on DeepADEM-iner (with respective F1= 0.64, 0.62 and 0.39 for ADE identification), on a GloVe model trained on Twitter (with F1=0.11 for the changemed) and finally on a stack embedding including a layer of Glove embedding and two layers of Flair embedding (with F1= 0.77 for selfreport)
Explainable Automated Coding of Clinical Notes using Hierarchical Label-wise Attention Networks and Label Embedding Initialisation
Diagnostic or procedural coding of clinical notes aims to derive a coded
summary of disease-related information about patients. Such coding is usually
done manually in hospitals but could potentially be automated to improve the
efficiency and accuracy of medical coding. Recent studies on deep learning for
automated medical coding achieved promising performances. However, the
explainability of these models is usually poor, preventing them to be used
confidently in supporting clinical practice. Another limitation is that these
models mostly assume independence among labels, ignoring the complex
correlation among medical codes which can potentially be exploited to improve
the performance. We propose a Hierarchical Label-wise Attention Network (HLAN),
which aimed to interpret the model by quantifying importance (as attention
weights) of words and sentences related to each of the labels. Secondly, we
propose to enhance the major deep learning models with a label embedding (LE)
initialisation approach, which learns a dense, continuous vector representation
and then injects the representation into the final layers and the label-wise
attention layers in the models. We evaluated the methods using three settings
on the MIMIC-III discharge summaries: full codes, top-50 codes, and the UK NHS
COVID-19 shielding codes. Experiments were conducted to compare HLAN and LE
initialisation to the state-of-the-art neural network based methods. HLAN
achieved the best Micro-level AUC and on the top-50 code prediction and
comparable results on the NHS COVID-19 shielding code prediction to other
models. By highlighting the most salient words and sentences for each label,
HLAN showed more meaningful and comprehensive model interpretation compared to
its downgraded baselines and the CNN-based models. LE initialisation
consistently boosted most deep learning models for automated medical coding.Comment: Accepted to Journal of Biomedical Informatics, structured abstract in
full text, 21 pages, 5 figures, 4 supplementary materials (4 extra pages
Machine Learning to Classify Cardiotocography for Fetal Hypoxia Detection
Fetal hypoxia can cause damaging consequences on babies' such as stillbirth and cerebral palsy. Cardiotocography (CTG) has been used to detect intrapartum fetal hypoxia during labor. It is a non-invasive machine that measures the fetal heart rate and uterine contractions. Visual CTG suffers inconsistencies in interpretations among clinicians that can delay interventions. Machine learning (ML) showed potential in classifying abnormal CTG, allowing automatic interpretation. In the absence of a gold standard, researchers used various surrogate biomarkers to classify CTG, where some were clinically irrelevant. We proposed using Apgar scores as the surrogate benchmark of babies' ability to recover from birth. Apgar scores measure newborns' ability to recover from active uterine contraction, which measures appearance, pulse, grimace, activity and respiration. The higher the Apgar score, the healthier the baby is.We employ signal processing methods to pre-process and extract validated features of 552 raw CTG. We also included CTG-specific characteristics as outlined in the NICE guidelines. We employed ML techniques using 22 features and measured performances between ML classifiers. While we found that ML can distinguish CTG with low Apgar scores, results for the lowest Apgar scores, which are rare in the dataset we used, would benefit from more CTG data for better performance. We need an external dataset to validate our model for generalizability to ensure that it does not overfit a specific population.Clinical Relevance- This study demonstrated the potential of using a clinically relevant benchmark for classifying CTG to allow automatic early detection of hypoxia to reduce decision-making time in maternity units.</p
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