[{"innerBlocks":[],"name":"core\/freeform","postId":297,"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":"Penn Medicine researchers have developed a machine learning algorithm that identifies oncology patients at risk of short-term mortality who need end-of-life conversations with clinicians.In a study of 26,525 patients receiving outpatient oncology care, the algorithm accurately predicted patients with cancer who were at risk of six-month mortality using electronic health records, including whether a patient had high blood pressure as well as laboratory and electrocardiogram data.

The study found that 51 percent of the patients the algorithm identified as \u201chigh priority\u201d for end-of-life conversations died within six months vs. fewer than 4 percent in the \u201clower priority\u201d group. \u201cOur findings suggest that ML tools hold promise for integration into clinical workflows to ensure that patients with cancer have timely conversations about their goals and values,\u201d concludes the study<\/a>, which was published in the journal JAMA Network Open<\/i>. Initially, researchers developed, validated and compared three ML models\u2014gradient boosting, logistic regression and random forest\u2014to estimate six-month mortality among patients seen in oncology clinics affiliated with a large academic cancer center. However, the random forest model in the study demonstrated the best predictive results. \u201cWe\u2019re excited about the scalability of this decision support method for providers, and not just in oncology,\u201d says Ravi Parikh, MD, lead author and an instructor of medical ethics and health policy at the University of Pennsylvania. \u201cOur process of using machine learning to flag high-risk patients in real time is broadly applicable, and our approach risk-stratifies patients in a usable way that just hasn\u2019t been available to us before.\u201d According to Parikh, the algorithm is being implemented at a medical center\u2014not part of the original pilot for the study\u2014where the research team will test whether identifying the best patients for conversations actually does prompt the doctors to initiate those discussions. Ultimately, researchers plan to conduct a three- to six-month randomized controlled trial involving about 100 clinicians.","saveContent":"Penn Medicine researchers have developed a machine learning algorithm that identifies oncology patients at risk of short-term mortality who need end-of-life conversations with clinicians.In a study of 26,525 patients receiving outpatient oncology care, the algorithm accurately predicted patients with cancer who were at risk of six-month mortality using electronic health records, including whether a patient had high blood pressure as well as laboratory and electrocardiogram data. The study found that 51 percent of the patients the algorithm identified as \u201chigh priority\u201d for end-of-life conversations died within six months vs. fewer than 4 percent in the \u201clower priority\u201d group. \u201cOur findings suggest that ML tools hold promise for integration into clinical workflows to ensure that patients with cancer have timely conversations about their goals and values,\u201d concludes the study<\/a>, which was published in the journal JAMA Network Open<\/i>. Initially, researchers developed, validated and compared three ML models\u2014gradient boosting, logistic regression and random forest\u2014to estimate six-month mortality among patients seen in oncology clinics affiliated with a large academic cancer center. However, the random forest model in the study demonstrated the best predictive results. \u201cWe\u2019re excited about the scalability of this decision support method for providers, and not just in oncology,\u201d says Ravi Parikh, MD, lead author and an instructor of medical ethics and health policy at the University of Pennsylvania. \u201cOur process of using machine learning to flag high-risk patients in real time is broadly applicable, and our approach risk-stratifies patients in a usable way that just hasn\u2019t been available to us before.\u201d According to Parikh, the algorithm is being implemented at a medical center\u2014not part of the original pilot for the study\u2014where the research team will test whether identifying the best patients for conversations actually does prompt the doctors to initiate those discussions. Ultimately, researchers plan to conduct a three- to six-month randomized controlled trial involving about 100 clinicians.","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]

Machine learning identifies patients in need of end-of-life planning

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