2,644 research outputs found
Multimodal Machine Learning for Automated ICD Coding
This study presents a multimodal machine learning model to predict ICD-10
diagnostic codes. We developed separate machine learning models that can handle
data from different modalities, including unstructured text, semi-structured
text and structured tabular data. We further employed an ensemble method to
integrate all modality-specific models to generate ICD-10 codes. Key evidence
was also extracted to make our prediction more convincing and explainable. We
used the Medical Information Mart for Intensive Care III (MIMIC -III) dataset
to validate our approach. For ICD code prediction, our best-performing model
(micro-F1 = 0.7633, micro-AUC = 0.9541) significantly outperforms other
baseline models including TF-IDF (micro-F1 = 0.6721, micro-AUC = 0.7879) and
Text-CNN model (micro-F1 = 0.6569, micro-AUC = 0.9235). For interpretability,
our approach achieves a Jaccard Similarity Coefficient (JSC) of 0.1806 on text
data and 0.3105 on tabular data, where well-trained physicians achieve 0.2780
and 0.5002 respectively.Comment: Machine Learning for Healthcare 201
ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network
Automated ICD coding, which assigns the International Classification of
Disease codes to patient visits, has attracted much research attention since it
can save time and labor for billing. The previous state-of-the-art model
utilized one convolutional layer to build document representations for
predicting ICD codes. However, the lengths and grammar of text fragments, which
are closely related to ICD coding, vary a lot in different documents.
Therefore, a flat and fixed-length convolutional architecture may not be
capable of learning good document representations. In this paper, we proposed a
Multi-Filter Residual Convolutional Neural Network (MultiResCNN) for ICD
coding. The innovations of our model are two-folds: it utilizes a multi-filter
convolutional layer to capture various text patterns with different lengths and
a residual convolutional layer to enlarge the receptive field. We evaluated the
effectiveness of our model on the widely-used MIMIC dataset. On the full code
set of MIMIC-III, our model outperformed the state-of-the-art model in 4 out of
6 evaluation metrics. On the top-50 code set of MIMIC-III and the full code set
of MIMIC-II, our model outperformed all the existing and state-of-the-art
models in all evaluation metrics. The code is available at
https://github.com/foxlf823/Multi-Filter-Residual-Convolutional-Neural-Network
Deepr: A Convolutional Net for Medical Records
Feature engineering remains a major bottleneck when creating predictive
systems from electronic medical records. At present, an important missing
element is detecting predictive regular clinical motifs from irregular episodic
records. We present Deepr (short for Deep record), a new end-to-end deep
learning system that learns to extract features from medical records and
predicts future risk automatically. Deepr transforms a record into a sequence
of discrete elements separated by coded time gaps and hospital transfers. On
top of the sequence is a convolutional neural net that detects and combines
predictive local clinical motifs to stratify the risk. Deepr permits
transparent inspection and visualization of its inner working. We validate
Deepr on hospital data to predict unplanned readmission after discharge. Deepr
achieves superior accuracy compared to traditional techniques, detects
meaningful clinical motifs, and uncovers the underlying structure of the
disease and intervention space
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