A system for informing food element decisions in the acquisition of edible materials from any source. The system includes a processor coupled to a memory configured to receive from a user client device a food element descriptor uniquely identifying a particular food element. The system retrieves from a physiological database at least an element of physiological data. The system identifies using at least an element of physiological data and a machine-learning algorithm user constitutional enhancing food elements and user constitutional advancing food elements. The system classifies using a food element classifier a food element descriptor. The system displays on a graphical user interface a constitutional enhancing food element or a constitutional advancing food element.

Methods and systems for providing various skin-related metrics and tracking the effects of skincare and cosmetic products are described. The system may acquire user images via optical scanning methods and analyze the images before and after application of a product to provide quantitative feedback to the user of beneficial or adverse effects of the product. The system may track response of the skin based on changes in inflammation, dryness, elasticity, pH levels, and/or microbiomes and correlate these changes with user information including ethnicity, location, and lifestyle to generate models that are capable of predicting a user's response to certain ingredients and/or predicting long-tern effects of certain ingredients on the skin.

A method for automated analysis of medical records which executes a medical analysis algorithm to analyze patient data to generate an electronic narrative document. The electronic narrative document is stored in a patient database, which may be retrieved for viewing and editing by a user. As changes are made by the user, the medical analysis algorithm will predict and make suggestions in real-time. If the user selects to manually modify the electronic narrative document, the changes are stored for subsequent use.

A system configured to receive health data pertaining to a user; select a user health profile from a plurality of user health profiles based on the collected health data, each of the plurality of user health profiles being associated with a health and wellness program and a set of interventions; receive user activity data and updated health data pertaining to, or during the user's participation in the associated health and wellness program from health devices; select a new set of interventions based on the user activity data; and select a new user health profile from the plurality of user health profiles based on at least one of the user activity data and the updated health data.

A system configured to receive health data pertaining to a user; select a user health profile from a plurality of user health profiles based on the collected health data, each of the plurality of user health profiles being associated with a health and wellness program and a set of interventions; receive user activity data and updated health data pertaining to, or during the user's participation in the associated health and wellness program from health devices; select a new set of interventions based on the user activity data; and select a new user health profile from the plurality of user health profiles based on at least one of the user activity data and the updated health data.

An apparatus for use in a milk management system comprising a first device, wherein the first device includes a non-transitory storage medium; and a processor communicatively coupled to the non-transitory storage medium. The processor is configured to: receive first data from a second device, the first data describing milk disposed in a first container, the first container having a first identifier associated therewith; and associate the first data with a first feeding order in a data structure store in a database, the first feeding order describing a first nutrition regimen.

Various embodiments of the present disclosure disclose generating contraindication alert communications. A knowledge graph data structure, including a graph-based representation associated with a user identifier and having nodes and edges, is accessed. Edge weights are adjusted based on medical data associated with the user identifier. One or more sequential traversals of the knowledge graph data structure are performed until an equilibrium condition is met. Based on determining that a subset of nodes is associated with visit tallies totaling more than a threshold proportion of all node visits associated with the one or more sequential traversals, a contraindication alert communication, which includes representation of a biological effect for the user identifier, can be generated and transmitted.

A medical information navigation engine (“MINE”) is capable of inferring referral activity not reported into a referral workflow system by utilizing intent-based clustering of medical information. The intent based clustering reconciles received medical data, from a variety of sources, and then clusters the data by applying one or more clustering rules. After the referrals not otherwise reported are inferred, they may be utilized to generate metrics that can be utilized to enhance patient care, and reduce costs. Metrics may be generated for both in-network and out-of-network referrals in order to distinguish differences in reporting activity.

Systems and methods that use multi-tasking and transfer learning with sparse gating mechanisms and domain knowledge to generate pheno-embeddings in a scalable manner that can improve the relevance of the patient embeddings from Electronic Health Records. A system, comprises at least one processor that executes the following computer executable components stored in memory: a structural pheno-embedding model that employs a hierarchical knowledge graph; a data augmentation component that expands on a sparse data set associated with the knowledge graph; and an embedding component that generates a specialized embedding for phenotypes using the structural pheno-embedding model and the augmented data set for a selected cohort.

Embodiments herein include systems and methods for detecting, predicting and/or assessing acute kidney injury. In an embodiment, a monitoring system to detect acute kidney injury is included. The monitoring system can include a sensor circuit configured to collect renal data including at least one of systemic renal data, direct renal data, urinary tract data, and renal-relevant extracorporeal data. The monitoring system can also include a memory circuit to store collected renal data, an evaluation circuit to assess renal status, and a telemetry circuit. The evaluation circuit can determine whether acute kidney injury has occurred or is likely to occur by comparing the renal data to at least one of threshold values, personal historical values, patient population values and patterns indicative of acute kidney injury. The evaluation circuit can initiate a warning notification if acute kidney injury has occurred or is likely to occur. Other embodiments are also included herein.