[{"innerBlocks":[],"name":"core\/freeform","postId":802,"blockType":{"name":"core\/freeform","keywords":[],"attributes":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"providesContext":[],"usesContext":[],"selectors":[],"supports":{"className":false,"customClassName":false,"reusable":false},"styles":[],"variations":[],"apiVersion":3,"title":"Classic","description":"Use the classic WordPress editor.","category":"text"},"originalContent":"The language that people use in their postings on Facebook is effective in predicting diabetes and mental health conditions, including anxiety, depression and psychoses.

That\u2019s the conclusion of a new study from Penn Medicine and Stony Brook University researchers.

Published on Monday in PLOS ONE<\/a><\/i>, they evaluated whether consenting patients\u2019 Facebook posts could be used to predict their diagnoses documented in their electronic medical records.

Researchers analyzed Facebook postings from almost 1,000 patients who agreed to have their EMR data linked to their profiles.

\u201cWe found that the language people use in Facebook is predictive of their health conditions reported in an EMR, often more so than typically available demographic data,\u201d state the authors, who note that 10 of 21 conditions included in the study were better predicted through the use of Facebook data vs. demographic information.

\u201cAlthough some early research has linked social media language use with health, this is the first study to the best of our understanding to do so at the level of the patient with EMR data,\u201d contend researchers.

Also See<\/b>: What social media postings can reveal about health concerns<\/a>

\u201cSocial media data are a quantifiable link into the otherwise elusive daily lives of patients, providing an avenue for study and assessment of behavioral and environmental disease risk factors,\u201d states the article. \u201cAnalogous to the genome, social media data linked to medical diagnoses can be banked with patients\u2019 consent, and an encoding of social media language can be used as markers of disease risk, serve as a screening tool and elucidate disease epidemiology.\u201d

Researchers see potential for clinicians to analyze patients\u2019 social media data through an \u201copt- in system\u201d in order to determine disease risk and conduct health interventions.

\u201cThis work is early, but our hope is that the insights gleaned from these posts could be used to better inform patients and providers about their health,\u201d says Raina Merchant, MD, <\/b>lead author and the director of Penn Medicine\u2019s Center for Digital Health. \u201cAs social media posts are often about someone\u2019s lifestyle choices and experiences or how they\u2019re feeling, this information could provide additional information about disease management and exacerbation.\u201d

Merchant plans to conduct a large trial later this year in which patients will be asked to share social media posts with their provider to determine whether the management and application of the data is feasible, as well as how many patients would actually consent.

\u201cOne challenge with this is that there is so much data and we, as providers, aren\u2019t trained to interpret it ourselves\u2014or make clinical decisions based on it,\u201d adds Merchant. \u201cTo address this, we will explore how to condense and summarize social media data.\u201d","saveContent":"The language that people use in their postings on Facebook is effective in predicting diabetes and mental health conditions, including anxiety, depression and psychoses.

That\u2019s the conclusion of a new study from Penn Medicine and Stony Brook University researchers.

Published on Monday in PLOS ONE<\/a><\/i>, they evaluated whether consenting patients\u2019 Facebook posts could be used to predict their diagnoses documented in their electronic medical records.

Researchers analyzed Facebook postings from almost 1,000 patients who agreed to have their EMR data linked to their profiles.

\u201cWe found that the language people use in Facebook is predictive of their health conditions reported in an EMR, often more so than typically available demographic data,\u201d state the authors, who note that 10 of 21 conditions included in the study were better predicted through the use of Facebook data vs. demographic information.

\u201cAlthough some early research has linked social media language use with health, this is the first study to the best of our understanding to do so at the level of the patient with EMR data,\u201d contend researchers.

Also See<\/b>: What social media postings can reveal about health concerns<\/a>

\u201cSocial media data are a quantifiable link into the otherwise elusive daily lives of patients, providing an avenue for study and assessment of behavioral and environmental disease risk factors,\u201d states the article. \u201cAnalogous to the genome, social media data linked to medical diagnoses can be banked with patients\u2019 consent, and an encoding of social media language can be used as markers of disease risk, serve as a screening tool and elucidate disease epidemiology.\u201d

Researchers see potential for clinicians to analyze patients\u2019 social media data through an \u201copt- in system\u201d in order to determine disease risk and conduct health interventions.

\u201cThis work is early, but our hope is that the insights gleaned from these posts could be used to better inform patients and providers about their health,\u201d says Raina Merchant, MD, <\/b>lead author and the director of Penn Medicine\u2019s Center for Digital Health. \u201cAs social media posts are often about someone\u2019s lifestyle choices and experiences or how they\u2019re feeling, this information could provide additional information about disease management and exacerbation.\u201d

Merchant plans to conduct a large trial later this year in which patients will be asked to share social media posts with their provider to determine whether the management and application of the data is feasible, as well as how many patients would actually consent.

\u201cOne challenge with this is that there is so much data and we, as providers, aren\u2019t trained to interpret it ourselves\u2014or make clinical decisions based on it,\u201d adds Merchant. \u201cTo address this, we will explore how to condense and summarize social media data.\u201d","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]

Facebook posts support predictions of patient medical conditions

More for you