Sequential Diagnosis Prediction with Transformer and Ontological Representation Article Swipe

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Xueping Peng , Guodong Long , Tao Shen , Sen Wang , Jing Jiang ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2109.03069

Sequential diagnosis prediction on the Electronic Health Record (EHR) has been proven crucial for predictive analytics in the medical domain. EHR data, sequential records of a patient's interactions with healthcare systems, has numerous inherent characteristics of temporality, irregularity and data insufficiency. Some recent works train healthcare predictive models by making use of sequential information in EHR data, but they are vulnerable to irregular, temporal EHR data with the states of admission/discharge from hospital, and insufficient data. To mitigate this, we propose an end-to-end robust transformer-based model called SETOR, which exploits neural ordinary differential equation to handle both irregular intervals between a patient's visits with admitted timestamps and length of stay in each visit, to alleviate the limitation of insufficient data by integrating medical ontology, and to capture the dependencies between the patient's visits by employing multi-layer transformer blocks. Experiments conducted on two real-world healthcare datasets show that, our sequential diagnoses prediction model SETOR not only achieves better predictive results than previous state-of-the-art approaches, irrespective of sufficient or insufficient training data, but also derives more interpretable embeddings of medical codes. The experimental codes are available at the GitHub repository (https://github.com/Xueping/SETOR).

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Artificial Intelligence

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Medical diagnosis Computer science Exploit Diagnosis code Timestamp Transformer Data mining Predictive analytics Health records Health care Machine learning Artificial intelligence Medicine Real-time computing Economics Environmental health Physics Population Computer security Pathology Economic growth Voltage Quantum mechanics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2109.03069
PDF: https://arxiv.org/pdf/2109.03069
OA Status: green
Cited By: 2
References: 35
Related Works: 10
OpenAlex ID: https://openalex.org/W3196962552

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DOI: https://doi.org/10.48550/arxiv.2109.03069

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Title: Sequential Diagnosis Prediction with Transformer and Ontological Representation

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Type: preprint

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Language: en

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Publication year: 2021

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Publication date: 2021-09-07

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Authors: Xueping Peng, Guodong Long, Tao Shen, Sen Wang, Jing Jiang

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Landing page: https://arxiv.org/abs/2109.03069

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Concepts: Medical diagnosis, Computer science, Exploit, Diagnosis code, Timestamp, Transformer, Data mining, Predictive analytics, Health records, Health care, Machine learning, Artificial intelligence, Medicine, Real-time computing, Economics, Environmental health, Physics, Population, Computer security, Pathology, Economic growth, Voltage, Quantum mechanics

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Cited by: 2

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abstract_inverted_index.visits	102, 132
abstract_inverted_index.between	99, 129
abstract_inverted_index.blocks.	137
abstract_inverted_index.capture	126
abstract_inverted_index.crucial	12
abstract_inverted_index.derives	172
abstract_inverted_index.domain.	19
abstract_inverted_index.medical	18, 122, 177
abstract_inverted_index.propose	80
abstract_inverted_index.records	23
abstract_inverted_index.results	158
abstract_inverted_index.achieves	155
abstract_inverted_index.admitted	104
abstract_inverted_index.datasets	144
abstract_inverted_index.equation	93
abstract_inverted_index.exploits	89
abstract_inverted_index.inherent	33
abstract_inverted_index.mitigate	77
abstract_inverted_index.numerous	32
abstract_inverted_index.ordinary	91
abstract_inverted_index.previous	160
abstract_inverted_index.systems,	30
abstract_inverted_index.temporal	63
abstract_inverted_index.training	168
abstract_inverted_index.alleviate	114
abstract_inverted_index.analytics	15
abstract_inverted_index.available	183
abstract_inverted_index.conducted	139
abstract_inverted_index.diagnoses	149
abstract_inverted_index.diagnosis	1
abstract_inverted_index.employing	134
abstract_inverted_index.hospital,	72
abstract_inverted_index.intervals	98
abstract_inverted_index.irregular	97
abstract_inverted_index.ontology,	123
abstract_inverted_index.patient's	26, 101, 131
abstract_inverted_index.Electronic	5
abstract_inverted_index.Sequential	0
abstract_inverted_index.embeddings	175
abstract_inverted_index.end-to-end	82
abstract_inverted_index.healthcare	29, 45, 143
abstract_inverted_index.irregular,	62
abstract_inverted_index.limitation	116
abstract_inverted_index.prediction	2, 150
abstract_inverted_index.predictive	14, 46, 157
abstract_inverted_index.real-world	142
abstract_inverted_index.repository	187
abstract_inverted_index.sequential	22, 52, 148
abstract_inverted_index.sufficient	165
abstract_inverted_index.timestamps	105
abstract_inverted_index.vulnerable	60
abstract_inverted_index.Experiments	138
abstract_inverted_index.approaches,	162
abstract_inverted_index.information	53
abstract_inverted_index.integrating	121
abstract_inverted_index.multi-layer	135
abstract_inverted_index.transformer	136
abstract_inverted_index.dependencies	128
abstract_inverted_index.differential	92
abstract_inverted_index.experimental	180
abstract_inverted_index.insufficient	74, 118, 167
abstract_inverted_index.interactions	27
abstract_inverted_index.irregularity	37
abstract_inverted_index.irrespective	163
abstract_inverted_index.temporality,	36
abstract_inverted_index.interpretable	174
abstract_inverted_index.insufficiency.	40
abstract_inverted_index.characteristics	34
abstract_inverted_index.state-of-the-art	161
abstract_inverted_index.transformer-based	84
abstract_inverted_index.admission/discharge	70
abstract_inverted_index.(https://github.com/Xueping/SETOR).	188
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	5
citation_normalized_percentile