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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Classification Tree Analysis Based On Machine Learning for Predicting Linezolid-Induced Thrombocytopenia.

Saki Takahashi¹, Yasuhiro Tsuji², Hidefumi Kasai³

¹Department of Medical Pharmaceutics, Faculty of Pharmaceutical Sciences, University of Toyama, 2630 Sugitani, Toyama, 930-0194, Japan.

Journal of Pharmaceutical Sciences

|February 20, 2021

Summary

Early detection of linezolid-induced thrombocytopenia is possible. Monitoring linezolid concentration and platelet reduction at 96 hours can help predict and prevent this adverse effect.

Keywords:

Clinical pharmacokinetics Machine learning Pharmacodynamics Pharmacokinetic/pharmacodynamic model Pharmacokinetics Population pharmacodynamics Population pharmacokinetics

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Area of Science:

Pharmacology
Hematology
Machine Learning in Medicine

Background:

Linezolid-induced thrombocytopenia is a known adverse effect.
Predicting its onset is crucial for patient safety.
Understanding the relationship between drug exposure, patient factors, and platelet count is essential.

Purpose of the Study:

To identify predictive factors for linezolid-induced thrombocytopenia.
To establish cutoff values for early detection using machine learning.
To guide therapeutic interventions to prevent thrombocytopenia.

Main Methods:

Classification and Regression Tree (CART) analysis was employed.
Machine learning techniques were used to analyze patient data.
Evaluated factors included linezolid concentration, platelet count changes, baseline platelet count, age, weight, and creatinine clearance.

Main Results:

CART analysis identified two key predictive cutoff values at 96 hours post-dose.
A platelet count reduction below 2.3% from baseline.
A linezolid concentration greater than or equal to 13.5 mg/L.

Conclusions:

Linezolid concentration and platelet reduction at 96 hours are critical indicators.
These thresholds precede the clinical onset of thrombocytopenia.
Monitoring these parameters allows for timely intervention to avoid linezolid-induced thrombocytopenia.