An Extreme Value Analysis-Based Systemic Approach in Healthcare Information Systems: The Case of Dietary Intake

Abstract

Biomarkers are measurements of biological variables that can determine a state of health. They consist of measuring a single variable or a combination of variables related to the state of health that these variables represent. Biomarkers can provide an early warning of a health problem in relation to an individual patient or group of patients, and thus trigger actions and lead to interventions. Nutritional biomarkers measure the biological consequences of one’s diet. In our recent work, we have used machine learning to predict weight, metabolic syndrome and blood pressure, using blood-exam-based biomarkers. In the current work, we use extreme value theory to examine the significance of outliers in health data, with a focus on diet and the standard biochemistry profile. Specifically, we show that, using extreme value analysis and by applying a systemisation of the process, health trends can be predicted, and thus, health interventions can be (at least partially) automated. For that purpose, public access datasets have been used, which were retrieved from the National Health and Nutrition Examination Survey. The NHANES is a program of studies designed to assess the health and nutritional status of the population in the United States. In total, about 70,000 datapoints were analysed, covering about a decade’s worth of observations.

Integrating Deep Learning with Symbolic Reasoning in TinyLlama for Accurate Information Retrieval

“…The application of symbolic reasoning in natural language understanding has been investigated, especially in tasks that require understanding complex sentences and deductive reasoning [37], [38], [39], [40], [41]. Research has also focused on the use of symbolic AI in problem-solving and decision-making tasks, demonstrating its capability to handle complex, rule-based scenarios [42], [43], [44], [45], [6]. The potential of symbolic reasoning in enhancing the reliability and consistency of AI systems has been another area of interest, with studies aiming to create models that can consistently apply rules and logic [32], [46], [47].…”

Augmenting Large Language Models with Rules for Enhanced Domain-Specific Interactions: The Case of Medical Diagnosis

“…This particular technology consists of a specific conditional logic that extracts those blood variables that are outside the normal ranges. Our system also consists of machine learning algorithms [4,49] that can identify similarities with specific weight groups, based on blood exams, and thus recognize other possible health states related to an unbalanced biochemical profile. Metabolic syndrome is also identified through a similar process [4,10].…”