Development of a Machine Learning Model Using Multiple, Heterogeneous Data Sources to Estimate Weekly US Suicide Fatalities
Suicide is a leading cause of death in the US. However, official national statistics on suicide rates are delayed by 1 to 2 years, hampering evidence-based public health planning and decision-making. To estimate weekly suicide fatalities in the US in near real time. This cross-sectional national stu...
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| Veröffentlicht in: | JAMA network open 2020-12, Vol.3 (12), p.e2030932 |
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| creator | Choi, Daejin Sumner, Steven A Holland, Kristin M Draper, John Murphy, Sean Bowen, Daniel A Zwald, Marissa Wang, Jing Law, Royal Taylor, Jordan Konjeti, Chaitanya De Choudhury, Munmun |
| description | Suicide is a leading cause of death in the US. However, official national statistics on suicide rates are delayed by 1 to 2 years, hampering evidence-based public health planning and decision-making.
To estimate weekly suicide fatalities in the US in near real time.
This cross-sectional national study used a machine learning pipeline to combine signals from several streams of real-time information to estimate weekly suicide fatalities in the US in near real time. This 2-phase approach first fits optimal machine learning models to each individual data stream and subsequently combines predictions made from each data stream via an artificial neural network. National-level US administrative data on suicide deaths, health services, and economic, meteorological, and online data were variously obtained from 2014 to 2017. Data were analyzed from January 1, 2014, to December 31, 2017.
Longitudinal data on suicide-related exposures were obtained from multiple, heterogeneous streams: emergency department visits for suicide ideation and attempts collected via the National Syndromic Surveillance Program (2015-2017); calls to the National Suicide Prevention Lifeline (2014-2017); calls to US poison control centers for intentional self-harm (2014-2017); consumer price index and seasonality-adjusted unemployment rate, hourly earnings, home price index, and 3-month and 10-year yield curves from the Federal Reserve Economic Data (2014-2017); weekly daylight hours (2014-2017); Google and YouTube search trends related to suicide (2014-2017); and public posts on suicide on Reddit (2 314 533 posts), Twitter (9 327 472 tweets; 2015-2017), and Tumblr (1 670 378 posts; 2014-2017).
Weekly estimates of suicide fatalities in the US were obtained through a machine learning pipeline that integrated the above data sources. Estimates were compared statistically with actual fatalities recorded by the National Vital Statistics System.
Combining information from multiple data streams, the machine learning method yielded estimates of weekly suicide deaths with high correlation to actual counts and trends (Pearson correlation, 0.811; P |
| doi_str_mv | 10.1001/jamanetworkopen.2020.30932 |
| format | Article |
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To estimate weekly suicide fatalities in the US in near real time.
This cross-sectional national study used a machine learning pipeline to combine signals from several streams of real-time information to estimate weekly suicide fatalities in the US in near real time. This 2-phase approach first fits optimal machine learning models to each individual data stream and subsequently combines predictions made from each data stream via an artificial neural network. National-level US administrative data on suicide deaths, health services, and economic, meteorological, and online data were variously obtained from 2014 to 2017. Data were analyzed from January 1, 2014, to December 31, 2017.
Longitudinal data on suicide-related exposures were obtained from multiple, heterogeneous streams: emergency department visits for suicide ideation and attempts collected via the National Syndromic Surveillance Program (2015-2017); calls to the National Suicide Prevention Lifeline (2014-2017); calls to US poison control centers for intentional self-harm (2014-2017); consumer price index and seasonality-adjusted unemployment rate, hourly earnings, home price index, and 3-month and 10-year yield curves from the Federal Reserve Economic Data (2014-2017); weekly daylight hours (2014-2017); Google and YouTube search trends related to suicide (2014-2017); and public posts on suicide on Reddit (2 314 533 posts), Twitter (9 327 472 tweets; 2015-2017), and Tumblr (1 670 378 posts; 2014-2017).
Weekly estimates of suicide fatalities in the US were obtained through a machine learning pipeline that integrated the above data sources. Estimates were compared statistically with actual fatalities recorded by the National Vital Statistics System.
Combining information from multiple data streams, the machine learning method yielded estimates of weekly suicide deaths with high correlation to actual counts and trends (Pearson correlation, 0.811; P < .001), while estimating annual suicide rates with low error (0.55%).
The proposed ensemble machine learning framework reduces the error for annual suicide rate estimation to less than one-tenth of that of current forecasting approaches that use only historical information on suicide deaths. These findings establish a novel approach for tracking suicide fatalities in near real time and provide the potential for an effective public health response such as supporting budgetary decisions or deploying interventions.</description><identifier>ISSN: 2574-3805</identifier><identifier>EISSN: 2574-3805</identifier><identifier>DOI: 10.1001/jamanetworkopen.2020.30932</identifier><identifier>PMID: 33355678</identifier><language>eng</language><publisher>United States: American Medical Association</publisher><subject>Cross-Sectional Studies ; Emergency Service, Hospital - statistics & numerical data ; Estimates ; Fatalities ; Forecasting - methods ; Health Informatics ; Humans ; Information Storage and Retrieval ; Machine Learning ; Online Only ; Original Investigation ; Public health ; Public Health - statistics & numerical data ; Public Health Surveillance - methods ; Real time ; Suicide - trends ; Suicide prevention ; Suicides & suicide attempts ; Trends ; United States - epidemiology</subject><ispartof>JAMA network open, 2020-12, Vol.3 (12), p.e2030932</ispartof><rights>2020. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Copyright 2020 Choi D et al. .</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a473t-fe71e4caa05fac909aeaa697cef939a0f242721b6fb78aa1e1141a757834ad303</citedby><cites>FETCH-LOGICAL-a473t-fe71e4caa05fac909aeaa697cef939a0f242721b6fb78aa1e1141a757834ad303</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,864,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33355678$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Choi, Daejin</creatorcontrib><creatorcontrib>Sumner, Steven A</creatorcontrib><creatorcontrib>Holland, Kristin M</creatorcontrib><creatorcontrib>Draper, John</creatorcontrib><creatorcontrib>Murphy, Sean</creatorcontrib><creatorcontrib>Bowen, Daniel A</creatorcontrib><creatorcontrib>Zwald, Marissa</creatorcontrib><creatorcontrib>Wang, Jing</creatorcontrib><creatorcontrib>Law, Royal</creatorcontrib><creatorcontrib>Taylor, Jordan</creatorcontrib><creatorcontrib>Konjeti, Chaitanya</creatorcontrib><creatorcontrib>De Choudhury, Munmun</creatorcontrib><title>Development of a Machine Learning Model Using Multiple, Heterogeneous Data Sources to Estimate Weekly US Suicide Fatalities</title><title>JAMA network open</title><addtitle>JAMA Netw Open</addtitle><description>Suicide is a leading cause of death in the US. However, official national statistics on suicide rates are delayed by 1 to 2 years, hampering evidence-based public health planning and decision-making.
To estimate weekly suicide fatalities in the US in near real time.
This cross-sectional national study used a machine learning pipeline to combine signals from several streams of real-time information to estimate weekly suicide fatalities in the US in near real time. This 2-phase approach first fits optimal machine learning models to each individual data stream and subsequently combines predictions made from each data stream via an artificial neural network. National-level US administrative data on suicide deaths, health services, and economic, meteorological, and online data were variously obtained from 2014 to 2017. Data were analyzed from January 1, 2014, to December 31, 2017.
Longitudinal data on suicide-related exposures were obtained from multiple, heterogeneous streams: emergency department visits for suicide ideation and attempts collected via the National Syndromic Surveillance Program (2015-2017); calls to the National Suicide Prevention Lifeline (2014-2017); calls to US poison control centers for intentional self-harm (2014-2017); consumer price index and seasonality-adjusted unemployment rate, hourly earnings, home price index, and 3-month and 10-year yield curves from the Federal Reserve Economic Data (2014-2017); weekly daylight hours (2014-2017); Google and YouTube search trends related to suicide (2014-2017); and public posts on suicide on Reddit (2 314 533 posts), Twitter (9 327 472 tweets; 2015-2017), and Tumblr (1 670 378 posts; 2014-2017).
Weekly estimates of suicide fatalities in the US were obtained through a machine learning pipeline that integrated the above data sources. Estimates were compared statistically with actual fatalities recorded by the National Vital Statistics System.
Combining information from multiple data streams, the machine learning method yielded estimates of weekly suicide deaths with high correlation to actual counts and trends (Pearson correlation, 0.811; P < .001), while estimating annual suicide rates with low error (0.55%).
The proposed ensemble machine learning framework reduces the error for annual suicide rate estimation to less than one-tenth of that of current forecasting approaches that use only historical information on suicide deaths. These findings establish a novel approach for tracking suicide fatalities in near real time and provide the potential for an effective public health response such as supporting budgetary decisions or deploying interventions.</description><subject>Cross-Sectional Studies</subject><subject>Emergency Service, Hospital - statistics & numerical data</subject><subject>Estimates</subject><subject>Fatalities</subject><subject>Forecasting - methods</subject><subject>Health Informatics</subject><subject>Humans</subject><subject>Information Storage and Retrieval</subject><subject>Machine Learning</subject><subject>Online Only</subject><subject>Original Investigation</subject><subject>Public health</subject><subject>Public Health - statistics & numerical data</subject><subject>Public Health Surveillance - methods</subject><subject>Real time</subject><subject>Suicide - trends</subject><subject>Suicide prevention</subject><subject>Suicides & suicide attempts</subject><subject>Trends</subject><subject>United States - epidemiology</subject><issn>2574-3805</issn><issn>2574-3805</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpdkU9LAzEQxYMoKupXkKBXW5PN7mbXgyBqrVDxUIvHMN3O1tRtsiZZRfzypv5DPWUgb968mR8hB5z1OWP8eAFLMBherHu0LZp-whLWF6wUyRrZTjKZ9kTBsvVf9RbZ837BWBRyUebZJtkSQmRZLott8naBz9jYdokmUFtToDdQPWiDdITgjDZzemNn2NCJ_6i7Jui2wSM6xIDOztGg7Ty9gAB0bDtXoafB0ksf9BIC0nvEx-aVTsZ03OlKz5AOorTRQaPfJRs1NB73vt4dMhlc3p0Pe6Pbq-vzs1EPUilCr0bJMa0AWFZDVbISECAvZYV1KUpgdZImMuHTvJ7KAoAj5ykHmclCpDATTOyQ00_ftpsucVbFVR00qnUxontVFrT6-2P0g5rbZyVlVhR8ZXD4ZeDsU4c-qEVc1cTMKsnjGYuUsSKqTj5VlbPeO6x_JnCmVuzUP3ZqxU59sIvN-78z_rR-kxLvxTOdPQ</recordid><startdate>20201201</startdate><enddate>20201201</enddate><creator>Choi, Daejin</creator><creator>Sumner, Steven A</creator><creator>Holland, Kristin M</creator><creator>Draper, John</creator><creator>Murphy, Sean</creator><creator>Bowen, Daniel A</creator><creator>Zwald, Marissa</creator><creator>Wang, Jing</creator><creator>Law, Royal</creator><creator>Taylor, Jordan</creator><creator>Konjeti, Chaitanya</creator><creator>De Choudhury, Munmun</creator><general>American Medical Association</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>5PM</scope></search><sort><creationdate>20201201</creationdate><title>Development of a Machine Learning Model Using Multiple, Heterogeneous Data Sources to Estimate Weekly US Suicide Fatalities</title><author>Choi, Daejin ; 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However, official national statistics on suicide rates are delayed by 1 to 2 years, hampering evidence-based public health planning and decision-making.
To estimate weekly suicide fatalities in the US in near real time.
This cross-sectional national study used a machine learning pipeline to combine signals from several streams of real-time information to estimate weekly suicide fatalities in the US in near real time. This 2-phase approach first fits optimal machine learning models to each individual data stream and subsequently combines predictions made from each data stream via an artificial neural network. National-level US administrative data on suicide deaths, health services, and economic, meteorological, and online data were variously obtained from 2014 to 2017. Data were analyzed from January 1, 2014, to December 31, 2017.
Longitudinal data on suicide-related exposures were obtained from multiple, heterogeneous streams: emergency department visits for suicide ideation and attempts collected via the National Syndromic Surveillance Program (2015-2017); calls to the National Suicide Prevention Lifeline (2014-2017); calls to US poison control centers for intentional self-harm (2014-2017); consumer price index and seasonality-adjusted unemployment rate, hourly earnings, home price index, and 3-month and 10-year yield curves from the Federal Reserve Economic Data (2014-2017); weekly daylight hours (2014-2017); Google and YouTube search trends related to suicide (2014-2017); and public posts on suicide on Reddit (2 314 533 posts), Twitter (9 327 472 tweets; 2015-2017), and Tumblr (1 670 378 posts; 2014-2017).
Weekly estimates of suicide fatalities in the US were obtained through a machine learning pipeline that integrated the above data sources. Estimates were compared statistically with actual fatalities recorded by the National Vital Statistics System.
Combining information from multiple data streams, the machine learning method yielded estimates of weekly suicide deaths with high correlation to actual counts and trends (Pearson correlation, 0.811; P < .001), while estimating annual suicide rates with low error (0.55%).
The proposed ensemble machine learning framework reduces the error for annual suicide rate estimation to less than one-tenth of that of current forecasting approaches that use only historical information on suicide deaths. These findings establish a novel approach for tracking suicide fatalities in near real time and provide the potential for an effective public health response such as supporting budgetary decisions or deploying interventions.</abstract><cop>United States</cop><pub>American Medical Association</pub><pmid>33355678</pmid><doi>10.1001/jamanetworkopen.2020.30932</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Cross-Sectional Studies Emergency Service, Hospital - statistics & numerical data Estimates Fatalities Forecasting - methods Health Informatics Humans Information Storage and Retrieval Machine Learning Online Only Original Investigation Public health Public Health - statistics & numerical data Public Health Surveillance - methods Real time Suicide - trends Suicide prevention Suicides & suicide attempts Trends United States - epidemiology |
| title | Development of a Machine Learning Model Using Multiple, Heterogeneous Data Sources to Estimate Weekly US Suicide Fatalities |
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