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논문분류	춘계학술대회 초록집
제목	Forecasting Acute kidney injury using Machine learning Algorithms
저자	Sejoong Kim
출판정보	2020; 2020(1):
키워드	acute kidney injury \| prediction \| machine learning \| hospitalization
초록	Electrolyte abnormalities are the most common reason for a renal consult in oncology, with wide ranging disorders and multiple causes. They could worsen outcomes, reduce the quality of life and preclude the use of certain anti-cancer therapy. Their presence may provoke significant morbidity, multiorgan dysfunction and even life-threatening conditions. Moreover, studies have demonstrated that patients with electrolyte abnormalities have worse outcomes, with correction of these disorders having a positive impact on treatment consequences and survival. Furthermore, there is a correlation between electrolyte alterations and poorer performance status, delays in therapy commencement and continuation, and departure from desired treatment response. Management of electrolytes abnormalities in a cancer patient requires a logical and comprehensive diagnostic approach to evaluate the underlying cause. This requires an understanding of the biology and natural history of the underlying tumor, the extent of cancer involvement, associated comorbidities, chemotherapeutic treatment schedule and concurrent medications. In addition, the rapid advancement and extensive range of chemotherapeutic agents in treating malignancies brings with it a need to understand the pharmacological properties of the drug, both from the pharmacokinetic and pharmacodynamic perspectives. Finally, close communication with the managing oncologist is imperative in determining the most appropriate managment plan, personalized to the patient's prognosis, quality of life, comorbidities and values.
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