Development and validation of interpretable machine learning models for postoperative pneumonia prediction by Bingbing Xiang, Yiran Liu, Shulan Jiao, Wensheng Zhang, Shun Wang, Mingliang Yi

“…This study aimed to develop and validate a predictive model for postoperative pneumonia in surgical patients using nine machine learning methods.ObjectiveOur study aims to develop and validate a predictive model for POP in surgical patients using nine machine learning algorithms. …”
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A distributional reinforcement learning model for optimal glucose control after cardiac surgery by Jacob M. Desman, Zhang-Wei Hong, Moein Sabounchi, Ashwin S. Sawant, Jaskirat Gill, Ana C. Costa, Gagan Kumar, Rajeev Sharma, Arpeta Gupta, Paul McCarthy, Veena Nandwani, Doug Powell, Alexandra Carideo, Donnie Goodwin, Sanam Ahmed, Umesh Gidwani, Matthew A. Levin, Robin Varghese, Farzan Filsoufi, Robert Freeman, Avniel Shetreat-Klein, Alexander W. Charney, Ira Hofer, Lili Chan, David Reich, Patricia Kovatch, Roopa Kohli-Seth, Monica Kraft, Pulkit Agrawal, John A. Kellum, Girish N. Nadkarni, Ankit Sakhuja

“…Abstract This study introduces Glucose Level Understanding and Control Optimized for Safety and Efficacy (GLUCOSE), a distributional offline reinforcement learning algorithm for optimizing insulin dosing after cardiac surgery. …”
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Development and validation of an explainable machine learning model for predicting postoperative pulmonary complications after lung cancer surgery: a machine learning studyResearch... by Shaolin Chen, Ting Deng, Qing Yang, Jin Li, Juanyan Shen, Xu Luo, Juan Tang, Xulian Zhang, Jordan Tovera Salvador, Junliang Ma

“…Summary: Background: Early identification and prediction of postoperative pulmonary complications (PPCs) are vital for patient management in lung cancer (LC) surgery. …”
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Preoperatively-determined Red Distribution Width (RDW) predicts prolonged length of stay after single-level spinal fusion in elderly patients by Anton Früh, Dietmar Frey, Adam Hilbert, Claudius Jelgersma, Christian Uhl, Nitzan Nissimov, Peter Truckenmüller, David Wasilewski, Dimitrios Rallios, Matthias Hoppe, Simon Bayerl, Nils Hecht, Peter Vajkoczy, Lars Wessels

“…Introduction: Elderly patients receiving lumbar fusion surgeries present with a higher risk profile, which necessitates a robust predictor of postoperative outcomes. The Red Distribution Width (RDW) is a preoperative routinely determined parameter that reflects the degree of heterogeneity of red blood cells. …”
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Automated spontaneous breathing trial performance tool is associated with improved outcomes following pediatric cardiac surgery: A single-center retrospective study from Alabama, U... by Matthew G. Clark, Santiago Borasino, Hayden J. Zaccagni, Shannon Payne, Justin Raper, Jeremy Loberger, Kristal M. Hock, Ahmed Asfari

“…Extubation readiness is assessed with a variety of tools. Using algorithmic analysis, we developed a spontaneous breathing trial (SBT) performance tool based on near real-time clinical and ventilator data. …”
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Personalized approach to acute peritonitis treatment based on genetic studies by I. Yu. Polianskyi, V. I. Moskaliuk

“…Materials and methods. 226 patients with signs of acute peritonitis had complex examination with serum cytokines and serotonin levels determination and analysis of the polymorphic sites alleles of IL1ß (-511C / T) and 5-HTTLPR genes by polymerase chain reaction evaluated the treatment outcomes using the developed algorithms. The treatment results with the use of developed algorithms were evaluated. …”
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Vestibular schwannoma combined with hydrocephalus treatment tactic by V.O. Fedirko, M.V. Yehorov, V.V. Shust

“…Objective to develop an algorithm for the management of patients with VS in combination with HC (occlusive, open with increased pressure, normal pressure). …”
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Comparative Effectiveness of Early and Delayed Surgical Interventions in Patients with Acute Adhesive Intestinal Obstruction: A Multicenter Controlled Randomized Trial Prospective... by B. V. Sigua, A. E. Demko, S. V. Petrov, P. A. Kotkov, I. A. Soloviev, A. V. Vovk, A. V. Osipov, V. I. Serdyuk, D. A. Chernyshev, M. A. Protchenkov, I. I. Gubkov, M. G. Zaitsev, S. Yu. Puzanov, A. A. Kozobin, O. V. Babkov, A. V. Glebova, D. S. Sakhno, D. V. Gurzhiy, V. P. Zemlyanoy

“…For the study, 216 patients with AAIO were selected, who, in accordance with the chosen randomization method, were distributed into the main (117 patients) and comparison groups (99 patients). …”
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Learning-based early detection of post-hepatectomy liver failure using temporal perioperative data: a nationwide multicenter retrospective study in ChinaResearch in context by Kai Wang, Qian Yang, Kang Li, Shanhua Tang, Baoluhe Zhang, Xiangyun Liao, Shunda Du, Wenguang Fu, Zhiwei Li, Huanwei Chen, Haorong Xie, Pengxiang Huang, Jieyuan Li, Qiuting Wang, Haiqing Liu, Zhiwei Huang, Pheng Ann Heng, Xueshuai Wan, Chuanjiang Li, Weixin Si

“…Summary: Background: Post-hepatectomy liver failure (PHLF), defined as acute liver failure following hepatectomy, remains a major complication for postoperative mortality lacking early detection approaches. …”
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Predicting Oncological and Functional Outcomes by Nephrectomy Type for T1 Renal Tumors Using Machine Learning Models by Dongrul Shin, Maisy Song, Jungyo Suh, Cheryn Song

“…Materials and Methods Using demographic and preoperative variables of 823 patients with clinical T1N0M0 renal tumors who underwent PN or RN between 2007 and 2019, we employed 5 different machine learning algorithms—general linear model (GLM), extreme gradient boosting (XgBoost), gradient boosting machine, distributed random forest, deep learning—and compared to predict recurrence probability and estimated glomerular filtration rate (eGFR) at 5-year after surgery. …”
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EXPERIENCE OF USING DIGITAL SYSTEMS FOR DIAGNOSTICS OF HYPERTROPHIC SKIN SCARS OF FACE by D.S. Avetikov, O.P. Bukhanchenko, I.O. Ivanytsky, N.A. Sokolova, I.V. Boyko

“…Currently, conventional algorithms for selecting methods of treating patients with scars are available. …”
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Constructing a machine learning model for systemic infection after kidney stone surgery based on CT values by Jiaxin Li, Yao Du, Gaoming Huang, Yawei Huang, Xiaoqing Xi, Zhenfeng Ye

“…Five machine learning algorithms and ten preoperative or intraoperative variables were used to develop a predictive model for SIRS. …”
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