Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0015672 (fatigue)
 51,768 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Burden symptom in advanced heart failure highly affects quality of life of both patients and caregivers, leading to severe functional limitation and social isolation. Symptoms in the advanced phases of the disease are numerous and often underestimated and undertreated. This negatively affects not only quality of life, but also increases hospitalizations, reduces therapeutic adherence, impairs cardiac function and leads to reduced survival. When symptom control cannot be achieved only with specific cardiologic therapy, optimal care should shift to a combination of life-prolonging and symptom-relief approach, possibly to be initiated as soon as advanced phases are detected. Optimal treatment of severe and invalidating symptoms requires a multi-modal and multi-dimensional approach, as pharmacological therapy represents only a part of a global evaluation that should include spiritual and psycho-social factors, potentially influencing symptom perception. Assessment therefore should rely on multi-modal and multi-dimensional patient-centered score models, such as the Edmonton Symptom Assessment System (ESAS), the Kansas City Cardiomyopathy Questionnaire (KCCQ), or the Integrated Palliative care Outcome Scale (IPOS).Pain, dyspnea, depression, fatigue and less frequent but distressing symptoms, including gastrointestinal disorders (nausea, vomiting, fecal impaction, hiccups), cough, itching, skin xerosis and restless legs syndrome, will be analyzed, and evidence of best palliative practice will be discussed.
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PMID:[Overview and symptom management in heart failure patients eligible for palliative care]. 3268 90

Purpose: Nutritional intervention was always implemented based on "one-size-fits-all" recommendation instead of personalized strategy. We aimed to develop a machine learning based model to predict the optimal dose of a botanical combination of lutein ester, zeaxanthin, extracts of black currant, chrysanthemum, and goji berry for individuals with eye fatigue. Methods: 504 features, including demographic, anthropometrics, eye-related indexes, blood biomarkers, and dietary habits, were collected at baseline from 303 subjects in a randomized controlled trial. An aggregated score of visual health (VHS) was developed from total score of eye fatigue symptoms, visuognosis persistence, macular pigment optical density, and Schirmer test to represent an overall eye fatigue level. VHS at 45 days after intervention was predicted by XGBoost algorithm using all features at baseline to show the eye fatigue improvement. Optimal dose of the combination was chosen based on the predicted VHS. Results: After feature selection and parameter optimization, a model was trained and optimized with a Pearson's correlation coefficient of 0.649, 0.638, and 0.685 in training, test and validation set, respectively. After removing the features collected by invasive blood test and costly optical coherence tomography, the model remained good performance. Among 58 subjects in test and validation sets, 39 should take the highest dose as the optimal option, 17 might take a lower dose, while 2 could not benefit from the combination. Conclusion: We applied XGBoost algorithm to develop a model which could predict optimized dose of the combination to provide personalized nutrition solution for individuals with eye fatigue.
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PMID:A Machine Learning Based Dose Prediction of Lutein Supplements for Individuals With Eye Fatigue. 3330 16


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