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Threshold decision-making in clinica...
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Djulbegovic, Benjamin.
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Threshold decision-making in clinical medicine = with practical application to hematology and oncology /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Threshold decision-making in clinical medicine/ by Benjamin Djulbegovic, Iztok Hozo ; with assistance from David Lizarraga.
其他題名:
with practical application to hematology and oncology /
作者:
Djulbegovic, Benjamin.
其他作者:
Hozo, Iztok.
出版者:
Cham :Springer International Publishing : : 2023.,
面頁冊數:
xxiii, 138 p. :ill. (some col.), digital ;24 cm.
內容註:
Evidence and decision-making -- Evidence-based summary measures -- Making decisions when diagnosis is certain: Under expected utility theory -- Under regret theory -- Under dual processing theory -- Hybrid model -- Making decisions when diagnosis is uncertain: Under expected utility theory -- Under regret theory -- Under dual processing theory -- Hybrid model -- Using predictive modelling to tailor therapy -- Under expected utility theory -- Under regret theory -- Heuristic decision-making: fast-and-frugal tree -- Conclusions: future of decision-making in oncology and hematology -- Artificial intelligence vs standard decision theories.
Contained By:
Springer Nature eBook
標題:
Hematology - Decision making. -
電子資源:
https://doi.org/10.1007/978-3-031-37993-2
ISBN:
9783031379932
Threshold decision-making in clinical medicine = with practical application to hematology and oncology /
Djulbegovic, Benjamin.
Threshold decision-making in clinical medicine
with practical application to hematology and oncology /[electronic resource] :by Benjamin Djulbegovic, Iztok Hozo ; with assistance from David Lizarraga. - Cham :Springer International Publishing :2023. - xxiii, 138 p. :ill. (some col.), digital ;24 cm. - Cancer treatment and research,v. 1892509-8497 ;. - Cancer treatment and research ;v. 189..
Evidence and decision-making -- Evidence-based summary measures -- Making decisions when diagnosis is certain: Under expected utility theory -- Under regret theory -- Under dual processing theory -- Hybrid model -- Making decisions when diagnosis is uncertain: Under expected utility theory -- Under regret theory -- Under dual processing theory -- Hybrid model -- Using predictive modelling to tailor therapy -- Under expected utility theory -- Under regret theory -- Heuristic decision-making: fast-and-frugal tree -- Conclusions: future of decision-making in oncology and hematology -- Artificial intelligence vs standard decision theories.
This book aims to provide threshold models to help physicians to make optimal diagnostic, therapeutic and predictive decisions. Readers will not only find theoretical information but also practical examples illustrating how these decisions should be made. Poor decision-making is considered a leading cause of death in contemporary medicine. Decisions, however, have to be made - at a given threshold of risk and unfortunately physicians are not trained on how to make decisions. This book provides help to all those who want to improve their decision-making for a better patient outcome. With its examples from hematology and oncology the book will not only benefit haematologists and oncologists but physicians from all disciplines, hence the threshold model is applicable to all fields in medicine. This book will be useful to experienced physicians as well as trainees alike.
ISBN: 9783031379932
Standard No.: 10.1007/978-3-031-37993-2doiSubjects--Topical Terms:
3668172
Hematology
--Decision making.
LC Class. No.: RC71.3
Dewey Class. No.: 616.075
Threshold decision-making in clinical medicine = with practical application to hematology and oncology /
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Evidence and decision-making -- Evidence-based summary measures -- Making decisions when diagnosis is certain: Under expected utility theory -- Under regret theory -- Under dual processing theory -- Hybrid model -- Making decisions when diagnosis is uncertain: Under expected utility theory -- Under regret theory -- Under dual processing theory -- Hybrid model -- Using predictive modelling to tailor therapy -- Under expected utility theory -- Under regret theory -- Heuristic decision-making: fast-and-frugal tree -- Conclusions: future of decision-making in oncology and hematology -- Artificial intelligence vs standard decision theories.
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