Identifying cancer cachexia in patients without weight loss information: machine learning approaches to address a real-world challenge.
Liangyu YinJiuwei CuiXin LinNa LiYang FanLing ZhangJie LiuFeifei ChongChang WangTingting LiangXiangliang LiuLi DengMei YangJiami YuXiaojie WangMinghua CongZengning LiMin WengQinghua YaoPingping JiaZengqing GuoWei LiChun-Hua SongHanping ShiHongxia XuPublished in: The American journal of clinical nutrition (2022)
We developed a ML model that can facilitate the identification of cancer cachexia in patients without weight loss information, which might improve decision-making and lead to the development of novel management strategies in cancer care.
Keyphrases
- weight loss
- end stage renal disease
- machine learning
- ejection fraction
- chronic kidney disease
- bariatric surgery
- decision making
- papillary thyroid
- peritoneal dialysis
- prognostic factors
- squamous cell carcinoma
- type diabetes
- deep learning
- artificial intelligence
- skeletal muscle
- social media
- insulin resistance
- childhood cancer
- bioinformatics analysis