14-18: Wounded bodies - argotic names of death machines and the injuries they caused by Jean-Pierre Goudaillier

“…To these weapons, as well as to other death machines of the time, correspond both old designations and neologisms used by the soldeirs. …”
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Construction and Validation of a Hospital Mortality Risk Model for Advanced Elderly Patients with Heart Failure Based on Machine Learning by Shang S, Wei M, Lv H, Liang X, Lu Y, Tang B

Subjects: “…Heart Failure；Advanced Elderly；Death；Machine Learning；…”
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Corps (é)conduits : La construction d’un regard-machine dans Crash (1996) et Cosmopolis (2012) de David Cronenberg by Guilhem Billaudel

“…When narrating the quest for the perfect car-crash in a group of fin de siècle fetishists or the physical and mental dissolution of a trader locked in his limousine, Cronenberg always makes the car become more than a prop or a means of transportation. A death machine, a magnifying glass, a coffin-to-be or a metaphorical cradle, it is the true protagonist of works focusing on desire and its variations. …”
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Machine learning-based construction of a programmed cell death-related model reveals prognosis and immune infiltration in pancreatic adenocarcinoma patients by Bing Wang, Zhida Long, Xun Zou, Zhengang Sun, Yuanchu Xiao

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Multiple machine learning algorithms identify 13 types of cell death-critical genes in large and multiple non-alcoholic steatohepatitis cohorts by Renao Jiang, Longfei Dai, Xinjian Xu, Zhen Zhang

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Machine learning model for predicting in-hospital cardiac mortality among atrial fibrillation patients by Huasheng Lv, Xuehua Bi, Shuai Shang, Meng Wei, Xianhui Zhou, Kai Wang, Baopeng Tang, Yanmei Lu

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A Hybrid Artificial Neural Network and Particle Swarm Optimization algorithm for Detecting COVID-19 Patients by Alla Ahmad Hassan, Tarik A Rashid

“…Diseased people experience severe symptoms in more severe cases. such as shortness of breath, which can lead to respiratory failure and death. Machine learning techniques for detection and classification are commonly used in current medical diagnoses. …”
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Optimizing Cancer Detection: Swarm Algorithms Combined with Deep Learning in Colon and Lung Cancer using Biomedical Images by HariKrishna Pathipati, Lova Naga Babu Ramisetti, Desidi Narsimha Reddy, Swetha Pesaru, Mashetty Balakrishna, Thota Anitha

“…Prompt disease recognition on both front significantly reduces the probability of death. Machine learning (ML) and deep learning (DL) approaches are employed to haste up cancer recognition, letting researchers to analysis a great amount of patients in a short period of time and at a low cost. …”
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Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approach by Tianshu Chen, Yuhan Yang, Zhizhong Huang, Feng Pan, Zhendi Xiao, Kunxue Gong, Wenguang Huang, Liu Xu, Xueqin Liu, Caiyun Fang

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Development and validation of an early prediction model for cardiac death risk in patients with light chain amyloidosis: a multicenter study by Naidong Pang, Ying Tian, Hongjie Chi, Xiaohong Fu, Xin Li, Shuyu Wang, Feifei Pan, Dongying Wang, Lin Xu, Jingyi Luo, Aijun Liu, XingPeng Liu

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Bioinformatics and experimental approach identify DNMT3A as a diagnostic marker associated with regulated cell death patterns in psoriasis by Linyu Zhu, Zhiyu Ye, Ling Wang, Shaomin Chen, Menger Guo, Lvya Zhang, Yuansheng Wu

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Integrating machine learning and multi-omics analysis to unveil key programmed cell death patterns and immunotherapy targets in kidney renal clear cell carcinoma by Fanyan Ou, Yi Pan, Qiuli Chen, Lixiong Zeng, Kanglai Wei, Delin Liu, Qian Guo, Liquan Zhou, Jie Yang

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Establishment of a prognostic model based on ER stress-related cell death genes and proposing a novel combination therapy in acute myeloid leukemia by Minghui Wang, Huajian Xian, Xiaoli Xia, Wenjie Zhang, Zixuan Huang, Chaoqun Lu, Yuling Zheng, Yixin Wang, Shufeng Xie, Renyao Pan, YaoYifu Yu, Ruiheng Wang, Huijian Zheng, Guorui Huang, Han Liu

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Computer-aided diagnosis of Haematologic disorders detection based on spatial feature learning networks using blood cell images by Jamal Alsamri, Hamed Alqahtani, Ali M. Al-Sharafi, Abdulbasit A. Darem, Khalid Nazim, Abdul Sattar, Menwa Alshammeri, Ahmad A. Alzahrani, Marwa Obayya

“…Currently, numerous physical methods exist to evaluate and forecast blood cancer utilizing the microscopic health information of white blood cell (WBC) images that are stable for prediction and cause many deaths. Machine learning (ML) and deep learning (DL) have aided the classification and collection of patterns in data, foremost in the growth of AI methods employed in numerous haematology fields. …”
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