Background: Anemia due to malnutrition may develop as a result of iron, folate and vitamin B12 deficiencies. This situation poses a higher risk of morbidity and mortality in the geriatric population than in other age groups. Therefore, early diagnosis of anemia and early initiation of treatment is very important. This study aims to predict the diagnosis of anemia with using machine learning (ML) methods in geriatric patients followed in an outpatient clinic.Methods: In line with the purpose of the study, anemia classification was made by analysing patients' hemogram and biochemistry blood values and medical data such as malnutrition, physical and cognitive activity scores with ML methods.Results: In our data set consisting of 438 patient observations, the most successful ML algorithm was the J48 algorithm with 97.77% accuracy. In the continuation of the study, the predictive performance of anemia was investigated by excluding blood values and selecting only attributes consisting of malnutrition and physical activity scores. In this case, the most successful prediction was obtained with the Random Forest algorithm with 85.39% accuracy.Conclusions: The study showed that anemia can be predicted with high accuracy in geriatric patients without hemogram data. Additionally, our geriatric data set was shared with researchers for future research. Thus, it has contributed to the literature by opening a new path for studies on subjects such as comparing classification performances with new methodologies or predicting different diseases in geriatric patients.

Aim: This study aimed to evaluate the association between admission lactate to albumin ratio (LAR) and in-hospital mortality in critically ill patients hospitalized with acute decompensated heart failure (ADHF).Material sand Methods: This retrospective single-center study included 87 adult patients admitted to a coronary intensive care unit (ICU) with ADHF. LAR was calculated using admission laboratory values. Patients were classified according to in-hospital survival status. Clinical, laboratory, and echocardiographic variables were analyzed to identify predictors of in-hospital mortality. Receiver operating characteristic (ROC) analysis was performed to assess the prognostic performance of the LAR.Results: In-hospital mortality occurred in 29 of 87 patients (33.3%). Median lactate levels were significantly higher in non-survivors compared with survivors (3.47 vs. 2.10 mmol/L, p < 0.001). The LAR was also higher in non-survivors (0.93 vs. 0.59, p = 0.002), while left ventricular ejection fraction (LVEF) was lower (28.2% vs. 36.1%, p = 0.006). In multivariable logistic regression analysis, mechanical ventilation (adjusted OR 80.8, p < 0.001) and LVEF (adjusted OR 0.92 per %, p = 0.016) were independently associated with in-hospital mortality, whereas the LAR was not (p = 0.266). ROC analysis demonstrated fair discrimination for the LAR in predicting mortality (AUC 0.706).Conclusion: Although higher LAR values were associated with mortality in unadjusted analysis, this marker did not provide independent prognostic value beyond established indicators of disease severity in critically ill patients with ADHF.

Artificial intelligence (AI) is a field within computer science that has vast applications and has transformed medical technologies. It is often regarded to be the branch of computer science that can handle complicated problems with minimal theory and many applications. AI is utilized to assist researchers in the analysis of large data sets, enabling precision medicine and assisting physicians in improving patient outcomes. New techniques in AI can bring together various types of data to make sense of new information obtained from multiomics datasets. Analyzing high-quality data combined with machine learning, a subset of AI, can help modify patients' unhealthy behaviors, predict risk or recurrence of chronic diseases after a surgical and curative treatment, prediction of progression and survival rates of patients with chronic diseases, therapeutic need, generation of improved clinical trial interpretations and identification of new targets. Howeveri, to effectively implement precision medicine in healthcare, a more user-friendly interface would be required. If AI technologies are applied correctly, fairly and robustly, in close cooperation with human intelligence, it is expected to open up new possibilities for effective and personalised healthcare services worldwide. In this review, the general outlines of AI technology, its application areas in healthcare and its future are overviewed.