Towards dynamic multi-modal phenotyping using chest radiographs and physiological data Article Swipe

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Nasir Hayat , Krzysztof J. Geras , Farah E. Shamout ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2111.02710

The healthcare domain is characterized by heterogeneous data modalities, such as imaging and physiological data. In practice, the variety of medical data assists clinicians in decision-making. However, most of the current state-of-the-art deep learning models solely rely upon carefully curated data of a single modality. In this paper, we propose a dynamic training approach to learn modality-specific data representations and to integrate auxiliary features, instead of solely relying on a single modality. Our preliminary experiments results for a patient phenotyping task using physiological data in MIMIC-IV & chest radiographs in the MIMIC- CXR dataset show that our proposed approach achieves the highest area under the receiver operating characteristic curve (AUROC) (0.764 AUROC) compared to the performance of the benchmark method in previous work, which only used physiological data (0.740 AUROC). For a set of five recurring or chronic diseases with periodic acute episodes, including cardiac dysrhythmia, conduction disorders, and congestive heart failure, the AUROC improves from 0.747 to 0.798. This illustrates the benefit of leveraging the chest imaging modality in the phenotyping task and highlights the potential of multi-modal learning in medical applications.

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Benchmark (Surveying)

Receiver Operating Characteristic

Computer Science

Artificial Intelligence

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Modality (human–computer interaction) Modalities Benchmark (surveying) Task (project management) Receiver operating characteristic Computer science Artificial intelligence Set (abstract data type) Radiography Data set Machine learning Medicine Radiology Geography Economics Management Sociology Programming language Social science Geodesy

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2111.02710
PDF: https://arxiv.org/pdf/2111.02710
OA Status: green
References: 11
Related Works: 20
OpenAlex ID: https://openalex.org/W3208375333

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DOI: https://doi.org/10.48550/arxiv.2111.02710

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Title: Towards dynamic multi-modal phenotyping using chest radiographs and physiological data

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Language: en

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Publication year: 2021

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Publication date: 2021-11-04

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Authors: Nasir Hayat, Krzysztof J. Geras, Farah E. Shamout

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Landing page: https://arxiv.org/abs/2111.02710

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Concepts: Modality (human–computer interaction), Modalities, Benchmark (surveying), Task (project management), Receiver operating characteristic, Computer science, Artificial intelligence, Set (abstract data type), Radiography, Data set, Machine learning, Medicine, Radiology, Geography, Economics, Management, Sociology, Programming language, Social science, Geodesy

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abstract_inverted_index.under	103
abstract_inverted_index.using	81
abstract_inverted_index.which	123
abstract_inverted_index.work,	122
abstract_inverted_index.(0.740	128
abstract_inverted_index.(0.764	110
abstract_inverted_index.0.798.	158
abstract_inverted_index.AUROC)	111
abstract_inverted_index.MIMIC-	91
abstract_inverted_index.domain	2
abstract_inverted_index.method	119
abstract_inverted_index.models	34
abstract_inverted_index.paper,	47
abstract_inverted_index.single	43, 70
abstract_inverted_index.solely	35, 66
abstract_inverted_index.(AUROC)	109
abstract_inverted_index.AUROC).	129
abstract_inverted_index.assists	22
abstract_inverted_index.benefit	162
abstract_inverted_index.cardiac	144
abstract_inverted_index.chronic	137
abstract_inverted_index.curated	39
abstract_inverted_index.current	30
abstract_inverted_index.dataset	93
abstract_inverted_index.dynamic	51
abstract_inverted_index.highest	101
abstract_inverted_index.imaging	11, 167
abstract_inverted_index.instead	64
abstract_inverted_index.medical	20, 181
abstract_inverted_index.patient	78
abstract_inverted_index.propose	49
abstract_inverted_index.relying	67
abstract_inverted_index.results	75
abstract_inverted_index.variety	18
abstract_inverted_index.However,	26
abstract_inverted_index.MIMIC-IV	85
abstract_inverted_index.achieves	99
abstract_inverted_index.approach	53, 98
abstract_inverted_index.compared	112
abstract_inverted_index.diseases	138
abstract_inverted_index.failure,	151
abstract_inverted_index.improves	154
abstract_inverted_index.learning	33, 179
abstract_inverted_index.modality	168
abstract_inverted_index.periodic	140
abstract_inverted_index.previous	121
abstract_inverted_index.proposed	97
abstract_inverted_index.receiver	105
abstract_inverted_index.training	52
abstract_inverted_index.auxiliary	62
abstract_inverted_index.benchmark	118
abstract_inverted_index.carefully	38
abstract_inverted_index.episodes,	142
abstract_inverted_index.features,	63
abstract_inverted_index.including	143
abstract_inverted_index.integrate	61
abstract_inverted_index.modality.	44, 71
abstract_inverted_index.operating	106
abstract_inverted_index.potential	176
abstract_inverted_index.practice,	16
abstract_inverted_index.recurring	135
abstract_inverted_index.clinicians	23
abstract_inverted_index.conduction	146
abstract_inverted_index.congestive	149
abstract_inverted_index.disorders,	147
abstract_inverted_index.healthcare	1
abstract_inverted_index.highlights	174
abstract_inverted_index.leveraging	164
abstract_inverted_index.experiments	74
abstract_inverted_index.illustrates	160
abstract_inverted_index.modalities,	8
abstract_inverted_index.multi-modal	178
abstract_inverted_index.performance	115
abstract_inverted_index.phenotyping	79, 171
abstract_inverted_index.preliminary	73
abstract_inverted_index.radiographs	88
abstract_inverted_index.dysrhythmia,	145
abstract_inverted_index.applications.	182
abstract_inverted_index.characterized	4
abstract_inverted_index.heterogeneous	6
abstract_inverted_index.physiological	13, 82, 126
abstract_inverted_index.characteristic	107
abstract_inverted_index.representations	58
abstract_inverted_index.decision-making.	25
abstract_inverted_index.state-of-the-art	31
abstract_inverted_index.modality-specific	56
cited_by_percentile_year
countries_distinct_count	1
institutions_distinct_count	3
sustainable_development_goals[0].id	https://metadata.un.org/sdg/16
sustainable_development_goals[0].score	0.7300000190734863
sustainable_development_goals[0].display_name	Peace, Justice and strong institutions
citation_normalized_percentile