首页|Mahidol University Reports Findings in Machine Learning (Development and interna l validation of machine-learning models for predicting survival in patients who underwent surgery for spinal metastases)
Mahidol University Reports Findings in Machine Learning (Development and interna l validation of machine-learning models for predicting survival in patients who underwent surgery for spinal metastases)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Machine Learning is the subject o f a report. According to news reporting originating from Bangkok, Thailand, by N ewsRx correspondents, research stated, "A retrospective study. This study aimed to develop machine-learning algorithms for predicting survival in patients who u nderwent surgery for spinal metastasis." Our news editors obtained a quote from the research from Mahidol University, "Th is study develops machine-learning models to predict postoperative survival in s pinal metastasis patients, filling the gaps of traditional prognostic systems. U tilizing data from 389 patients, the study highlights XGBoost and CatBoost algor ithms effectiveness for 90, 180, and 365-day survival predictions, with preopera tive serum albumin as a key predictor. These models offer a promising approach f or enhancing clinical decisionmaking and personalized patient care. A registry of patients who underwent surgery (instrumentation, decompression, or fusion) fo r spinal metastases between 2004 and 2018 was used. The outcome measure was surv ival at postoperative days 90, 180, and 365. Preoperative variables were used to develop machinelearning algorithms to predict survival chance in each period. The performance of the algorithms was measured using the area under the receiver operating characteristic curve (AUC). A total of 389 patients were identified, with 90-, 180-, and 365-day mortality rates of 18%, 41% , and 45% postoperatively, respectively. The XGBoost algorithm sho wed the best performance for predicting 180-day and 365-day survival (AUCs of 0. 744 and 0.693, respectively). The CatBoost algorithm demonstrated the best perfo rmance for predicting 90-day survival (AUC of 0.758). Serum albumin had the high est positive correlation with survival after surgery."
BangkokThailandAsiaAlgorithmsCyb orgsEmerging TechnologiesHealth and MedicineMachine LearningSurgery
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