Mining free-text medical notes for suicide risk assessment

Marios Adamou; Vincenzo Lagani; Grigoris Antoniou; Paulos Charonyktakis; Elissavet Greasidou; Ioannis Tsamardinos

doi:10.1145/3200947.3201020

Mining free-text medical notes for suicide risk assessment

Marios Adamou, Vincenzo Lagani, Grigoris Antoniou, Paulos Charonyktakis, Elissavet Greasidou, Ioannis Tsamardinos

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

6 Scopus citations

Abstract

Suicide has been considered as an important public health issue for a very long time, and is one of the main causes of death worldwide. Despite suicide prevention strategies being applied, the rate of suicide has not changed substantially over the past decades. Advances in machine learning make it possible to attempt to predict suicide based on the analysis of relevant data to inform clinical practice. This paper reports on findings from the analysis of data of patients who died by suicide in the period 2013-2016 and made use of both structured data and free-text medical notes. We focus on examining various text-mining approaches to support risk assessment. The results show that using advance machine learning and text-mining techniques, it is possible to predict within a specified period which people are most at risk of taking their own life at the time of referral to a mental health service.

Original language	English (US)
Title of host publication	ACM International Conference Proceeding Series
Publisher	Association for Computing [email protected]
ISBN (Print)	9781450364331
DOIs	https://doi.org/10.1145/3200947.3201020
State	Published - Jul 9 2018
Externally published	Yes

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10.1145/3200947.3201020

Cite this

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title = "Mining free-text medical notes for suicide risk assessment",

abstract = "Suicide has been considered as an important public health issue for a very long time, and is one of the main causes of death worldwide. Despite suicide prevention strategies being applied, the rate of suicide has not changed substantially over the past decades. Advances in machine learning make it possible to attempt to predict suicide based on the analysis of relevant data to inform clinical practice. This paper reports on findings from the analysis of data of patients who died by suicide in the period 2013-2016 and made use of both structured data and free-text medical notes. We focus on examining various text-mining approaches to support risk assessment. The results show that using advance machine learning and text-mining techniques, it is possible to predict within a specified period which people are most at risk of taking their own life at the time of referral to a mental health service.",

author = "Marios Adamou and Vincenzo Lagani and Grigoris Antoniou and Paulos Charonyktakis and Elissavet Greasidou and Ioannis Tsamardinos",

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AU - Adamou, Marios

AU - Lagani, Vincenzo

AU - Antoniou, Grigoris

AU - Charonyktakis, Paulos

AU - Greasidou, Elissavet

AU - Tsamardinos, Ioannis

N1 - Generated from Scopus record by KAUST IRTS on 2023-09-23

PY - 2018/7/9

Y1 - 2018/7/9

N2 - Suicide has been considered as an important public health issue for a very long time, and is one of the main causes of death worldwide. Despite suicide prevention strategies being applied, the rate of suicide has not changed substantially over the past decades. Advances in machine learning make it possible to attempt to predict suicide based on the analysis of relevant data to inform clinical practice. This paper reports on findings from the analysis of data of patients who died by suicide in the period 2013-2016 and made use of both structured data and free-text medical notes. We focus on examining various text-mining approaches to support risk assessment. The results show that using advance machine learning and text-mining techniques, it is possible to predict within a specified period which people are most at risk of taking their own life at the time of referral to a mental health service.

AB - Suicide has been considered as an important public health issue for a very long time, and is one of the main causes of death worldwide. Despite suicide prevention strategies being applied, the rate of suicide has not changed substantially over the past decades. Advances in machine learning make it possible to attempt to predict suicide based on the analysis of relevant data to inform clinical practice. This paper reports on findings from the analysis of data of patients who died by suicide in the period 2013-2016 and made use of both structured data and free-text medical notes. We focus on examining various text-mining approaches to support risk assessment. The results show that using advance machine learning and text-mining techniques, it is possible to predict within a specified period which people are most at risk of taking their own life at the time of referral to a mental health service.

UR - https://dl.acm.org/doi/10.1145/3200947.3201020

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U2 - 10.1145/3200947.3201020

DO - 10.1145/3200947.3201020

M3 - Conference contribution

SN - 9781450364331

BT - ACM International Conference Proceeding Series

PB - Association for Computing [email protected]

ER -

Mining free-text medical notes for suicide risk assessment

Abstract

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