Data-Driven Decision Support for Autism Diagnosis using Machine Learning

Sotiris Batsakis; Marios Adamou; Ilias Tachmazidis; Grigoris Antoniou; Thanasis Kehagias

doi:10.1145/3444757.3485101

Data-Driven Decision Support for Autism Diagnosis using Machine Learning

Sotiris Batsakis, Marios Adamou, Ilias Tachmazidis, Grigoris Antoniou, Thanasis Kehagias

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

This paper describes work in progress about using AI technologies to support diagnostic decision making. In particular, we analyse clinical data of past cases to develop a data-driven prediction model for future cases. To do so, we use a versatile AutoML platform that applies a multitude of machine learning algorithms and their configurations. Our results show initial promise, but also point to limitations of currently available data, opening up avenues for further research.

Original language	English
Title of host publication	MEDES '21
Subtitle of host publication	Proceedings of the 13th International Conference on Management of Digital EcoSystems
Publisher	Association for Computing Machinery (ACM)
Pages	30-34
Number of pages	5
ISBN (Print)	9781450383141
DOIs	https://doi.org/10.1145/3444757.3485101
Publication status	Published - 1 Nov 2021
Event	13th International Conference on Management of Digital EcoSystems - Virtual, Online, Tunisia Duration: 1 Nov 2021 → 3 Nov 2021 Conference number: 13 https://medes.sigappfr.org/21/

Conference

Conference	13th International Conference on Management of Digital EcoSystems
Abbreviated title	MEDES 2021
Country/Territory	Tunisia
City	Virtual, Online
Period	1/11/21 → 3/11/21
Internet address	https://medes.sigappfr.org/21/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1145/3444757.3485101Licence: Unspecified

Cite this

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title = "Data-Driven Decision Support for Autism Diagnosis using Machine Learning",

abstract = "This paper describes work in progress about using AI technologies to support diagnostic decision making. In particular, we analyse clinical data of past cases to develop a data-driven prediction model for future cases. To do so, we use a versatile AutoML platform that applies a multitude of machine learning algorithms and their configurations. Our results show initial promise, but also point to limitations of currently available data, opening up avenues for further research.",

keywords = "Autism, Clinical decision support, Computing methodologies, Artificial intelligence, Machine learning",

author = "Sotiris Batsakis and Marios Adamou and Ilias Tachmazidis and Grigoris Antoniou and Thanasis Kehagias",

note = "Publisher Copyright: {\textcopyright} 2021 ACM. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 13th International Conference on Management of Digital EcoSystems, MEDES 2021 ; Conference date: 01-11-2021 Through 03-11-2021",

year = "2021",

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doi = "10.1145/3444757.3485101",

language = "English",

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Batsakis, S, Adamou, M, Tachmazidis, I, Antoniou, G & Kehagias, T 2021, Data-Driven Decision Support for Autism Diagnosis using Machine Learning. in MEDES '21: Proceedings of the 13th International Conference on Management of Digital EcoSystems. Association for Computing Machinery (ACM), pp. 30-34, 13th International Conference on Management of Digital EcoSystems, Virtual, Online, Tunisia, 1/11/21. https://doi.org/10.1145/3444757.3485101

Data-Driven Decision Support for Autism Diagnosis using Machine Learning. / Batsakis, Sotiris; Adamou, Marios; Tachmazidis, Ilias et al.

MEDES '21: Proceedings of the 13th International Conference on Management of Digital EcoSystems. Association for Computing Machinery (ACM), 2021. p. 30-34.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Data-Driven Decision Support for Autism Diagnosis using Machine Learning

AU - Batsakis, Sotiris

AU - Adamou, Marios

AU - Tachmazidis, Ilias

AU - Antoniou, Grigoris

AU - Kehagias, Thanasis

N1 - Conference code: 13

PY - 2021/11/1

Y1 - 2021/11/1

N2 - This paper describes work in progress about using AI technologies to support diagnostic decision making. In particular, we analyse clinical data of past cases to develop a data-driven prediction model for future cases. To do so, we use a versatile AutoML platform that applies a multitude of machine learning algorithms and their configurations. Our results show initial promise, but also point to limitations of currently available data, opening up avenues for further research.

AB - This paper describes work in progress about using AI technologies to support diagnostic decision making. In particular, we analyse clinical data of past cases to develop a data-driven prediction model for future cases. To do so, we use a versatile AutoML platform that applies a multitude of machine learning algorithms and their configurations. Our results show initial promise, but also point to limitations of currently available data, opening up avenues for further research.

KW - Autism

KW - Clinical decision support

KW - Computing methodologies

KW - Artificial intelligence

KW - Machine learning

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U2 - 10.1145/3444757.3485101

DO - 10.1145/3444757.3485101

M3 - Conference contribution

AN - SCOPUS:85121584955

SN - 9781450383141

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EP - 34

BT - MEDES '21

PB - Association for Computing Machinery (ACM)

T2 - 13th International Conference on Management of Digital EcoSystems

Y2 - 1 November 2021 through 3 November 2021

ER -

Data-Driven Decision Support for Autism Diagnosis using Machine Learning

Abstract

Conference

UN SDGs

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