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AI-Enabled Palliative Care : = From Algorithms To Clinical Deployment.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	AI-Enabled Palliative Care :/
其他題名:	From Algorithms To Clinical Deployment.
作者:	Avati, Anand Vishweswaran.
面頁冊數:	1 online resource (127 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-04, Section: A.
Contained By:	Dissertations Abstracts International84-04A.
標題:	Patients. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29342283click for full text (PQDT)
ISBN:	9798351496153

AI-Enabled Palliative Care : = From Algorithms To Clinical Deployment.
Avati, Anand Vishweswaran.

AI-Enabled Palliative Care :From Algorithms To Clinical Deployment. - 1 online resource (127 pages)

Source: Dissertations Abstracts International, Volume: 84-04, Section: A.

Thesis (Ph.D.)--Stanford University, 2022.

Includes bibliographical references

Healthcare is one of the most promising application areas for Artificial Intelligence (AI) to have a positive impact on society. There has been impressive progress in predictive modeling with health data in recent literature, even matching or exceeding expert-human level performance on a variety of tasks. Yet, translating these machine learning advances into improved patient care has proven to be particularly challenging. While developing accurate and well calibrated models (i.e. the machine learning problem) is necessary to make AI-enabled healthcare applications even possible, a careful understanding and analysis of the healthcare problem is just as essential for bridging the gap between accurate predictions and improved clinical care for the patient. Acknowledging and addressing both these problems is crucial for a successful AI clinical deployment.In this work, we consider the healthcare problem of improving access to palliative care for hospitalized patients. We frame it as a machine learning problem and validate that the framing is indeed appropriate for the healthcare problem at hand by conducting a prospective analysis study involving palliative care specialists. Our technical contributions include a novel survival loss (SurvivalCRPS), evaluation metric (SurvivalAUPRC), a gradient boosting algorithm for probabilistic prediction (NGBoost), among others. We perform a cost-benefit analysis and study the impact of various factors affecting care delivery to inform the design of a clinical workflow to increase access to palliative care services of hospitalized patients. We report on our experiences in operationalizing this workflow, powered by the above algorithmic advances, at the General Medicine service line of Stanford Hospital.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798351496153Subjects--Topical Terms:

1961957
Patients.
Index Terms--Genre/Form:

542853
Electronic books.

AI-Enabled Palliative Care : = From Algorithms To Clinical Deployment.
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