A method comprising receiving an input indicating intake information associated with a patient. Based on the input, the method further includes determining an initial discharge date and receiving mobility information associated with the patient. Based in part on the mobility information, the method further includes determining an estimated discharge date and a confidence metric associated with the estimated discharge date, determining that the estimated discharge date is later than the initial discharge date by more than a threshold period of time, and determining that the confidence metric is greater than a threshold metric. Based in part on the estimated discharge date being later than the initial discharge date by more than the threshold period of time and the confidence metric being greater than the threshold metric, the method further includes generating an alert.

Various techniques relate to aligning parameters and audio recordings obtained at a rescue scene. An example method includes receiving, from a first device, a first file including first measurements of a first parameter at first discrete times in a time interval. The first file further indicates a marker output by the first device during the time interval. The method also includes receiving, from a second device, a second file comprising second measurements of a second parameter at second discrete times in the time interval. The method includes detecting the marker output by the first device in the second measurements of the second parameter and based on detecting the signal output by the first device in the second measurements, generating aligned data by time-aligning the first measurements of the first parameter and the second measurements of the second parameter. The method further includes outputting the aligned data.

Various techniques relate to aligning parameters and audio recordings obtained at a rescue scene. An example method includes receiving, from a first device, a first file including first measurements of a first parameter at first discrete times in a time interval. The first file further indicates a marker output by the first device during the time interval. The method also includes receiving, from a second device, a second file comprising second measurements of a second parameter at second discrete times in the time interval. The method includes detecting the marker output by the first device in the second measurements of the second parameter and based on detecting the signal output by the first device in the second measurements, generating aligned data by time-aligning the first measurements of the first parameter and the second measurements of the second parameter. The method further includes outputting the aligned data.

A computerized method includes obtaining a set of entities. The method also includes, for each entity, obtaining data specific to the entity, generating a feature vector based on the data, and processing the feature vector to generate an entity fall likelihood that indicates a likelihood that the entity will experience a fall based on the feature vector. The method further includes determining a subset of entities having entity fall likelihoods that satisfy a threshold. For each entity in the subset, the method includes determining impact scores for parameters of the feature vector associated with the entity and generating a feature list based on the determined impact scores. Each impact score is indicative of an effect of the parameter on the entity fall likelihood for the entity. The feature list is specific to the entity and includes a parameter having the highest impact score.

The invention relates to computer-implemented systems and methods for analyzing medical images and records in various formats and structures from disparate sources and providers to provide answers to specific medical-related questions. An embodiment of the present invention is directed to accessing a health care form, identifying related text, leveraging computer vision technology to group nearby text, and applying deep learning models to identify fields from the text. This information may then be used to create a structured standardized output.

Techniques are described for generating multimodal training data cohorts tailored to specific clinical machine learning (ML) model inferencing tasks. In an embodiment, a method comprises accessing, by a system comprising a processor, multimodal clinical data for a plurality of subjects included in one or more clinical data sources. The method further comprises selecting, by the system, datasets from the multimodal clinical data based on the datasets respectively comprising subsets of the multimodal clinical data that satisfy criteria determined to be relevant to a clinical processing task. The method further comprises generating, by the system, a training data cohort comprising the datasets for training a clinical inferencing model to perform the clinical processing task.

A system for facilitating compliance with a rehabilitative exercise regimen is configurable to access a rehabilitative exercise regimen comprising a plurality of rehabilitative exercises. The plurality of rehabilitative exercises includes at least a first rehabilitative exercise and a second rehabilitative exercise. The system is further configurable to obtain exercise analysis output. The exercise analysis output is based on sensor data capturing a user performance of a user performing the first rehabilitative exercise. The system is further configurable to, in response to determining that the exercise analysis output indicates satisfactory completion of the first rehabilitative exercise by the user, configure an exercise interface to permit the user to proceed to the second rehabilitative exercise.

A system for facilitating compliance with a rehabilitative exercise regimen is configurable to access a rehabilitative exercise regimen comprising a plurality of rehabilitative exercises. The plurality of rehabilitative exercises includes at least a first rehabilitative exercise and a second rehabilitative exercise. The system is further configurable to obtain exercise analysis output. The exercise analysis output is based on sensor data capturing a user performance of a user performing the first rehabilitative exercise. The system is further configurable to, in response to determining that the exercise analysis output indicates satisfactory completion of the first rehabilitative exercise by the user, configure an exercise interface to permit the user to proceed to the second rehabilitative exercise.

Methods and systems of enhancing an electronic interaction between a behavioral-modification program and a user in the program by providing customized content specific to the user. The systems and methods allow for coach-counselor assistance to the individual-user or for automated content delivery.

Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.