Using Machine-Learning to Assess the Prognostic Value of Early Enteral Feeding Intolerance in Critically Ill Patients: A Retrospective Study.

BACKGROUND: The association between gastrointestinal intolerance during early enteral nutrition (EN) and adverse clinical outcomes in critically ill patients is controversial. We aimed to assess the prognostic value of enteral feeding intolerance (EFI) markers during early ICU stays and to predict early EN failure using a machine learning (ML) approach. METHODS: We performed a retrospective analysis of data from adult patients admitted to Beilinson Hospital ICU between January 2011 and December 2018 for more than 48 h and received EN. Clinical data, including demographics, severity scores, EFI markers, and medications, along with 72 h after admission, were analyzed by ML algorithms. Prediction performance was assessed by the area under the receiver operating characteristics (AUCROC) of a ten-fold cross-validation set. RESULTS: The datasets comprised 1584 patients. The means of the cross-validation AUCROCs for 90-day mortality and early EN failure were 0.73 (95% CI 0.71-0.75) and 0.71 (95% CI 0.67-0.74), respectively. Gastric residual volume above 250 mL on the second day was an important component of both prediction models. CONCLUSIONS: ML underlined the EFI markers that predict poor 90-day outcomes and early EN failure and supports early recognition of at-risk patients. Results have to be confirmed in further prospective and external validation studies.

Self-report symptom-based endometriosis prediction using machine learning.

Endometriosis is a chronic gynecological condition that affects 5-10% of reproductive age women. Nonetheless, the average time-to-diagnosis is usually between 6 and 10 years from the onset of symptoms. To shorten time-to-diagnosis, many studies have developed non-invasive screening tools. However, most of these studies have focused on data obtained from women who had/were planned for laparoscopy surgery, that is, women who were near the end of the diagnostic process. In contrast, our study aimed to develop a self-diagnostic tool that predicts the likelihood of endometriosis based only on experienced symptoms, which can be used in early stages of symptom onset. We applied machine learning to train endometriosis prediction models on data obtained via questionnaires from two groups of women: women who were diagnosed with endometriosis and women who were not diagnosed. The best performing model had AUC of 0.94, sensitivity of 0.93, and specificity of 0.95. The model is intended to be incorporated into a website as a self-diagnostic tool and is expected to shorten time-to-diagnosis by referring women with a high likelihood of having endometriosis to further examination. We also report the importance and effectiveness of different symptoms in predicting endometriosis.