Disclosed herein are systems and methods for identifying radiation therapy treatment data for different patients, such as field geometry. A central server collects patient data, radiation therapy treatment planning data, clinic-specific rules, and other pertinent treatment/medical data associated with a patient. The server then executes one or more machine-learning computer models to predict field geometry variables and weights associated with the patient's treatments. Using the predicted variables and weights, the server execute a clinic-specific set of logic to identify suggested field geometry, such as couch/gantry angles and/or arc attributes. The server then monitors whether end users (e.g., medical professionals) revise the suggested field geometry and trains the model accordingly.

A method of analysis using mass spectrometry and/or ion mobility spectrometry is disclosed comprising: (a) using a first device to generate smoke, aerosol or vapour from a target in vitro or ex vivo cell population; (b) mass analysing and/or ion mobility analysing said smoke, aerosol or vapour, or ions derived therefrom, in order to obtain spectrometric data; and (c) analysing said spectrometric data in order to identify and/or characterise said target cell population or one or more cells and/or compounds present in said target cell population.

Some embodiments include a system and computer-implemented method for aggregating and tracking medical delivery to a patient including a non-transitory computer- readable medium in data communication with at least one processor, where the non-transitory computer-readable medium includes software instructions for a medical services tracking system and method. Upon execution of the software instructions, information from a patient database or server can be received and displayed a medical record dashboard. A user can view and edit access to the information, and a user selectable link can display medical record information. The system and method enable auto-population of medical data entry fields based at least one part on at least one claim made or billing signed off by a physician for at least one medical service or procedure previously provided to or performed on at least one patient.

A clinical data interface device provides integrated portions of the electronic medical record system to identify and confirm a patient file for receiving data and personality modules for receiving and translating data from a variety of clinical device monitors for that identified patient.

A clinical data interface device provides integrated portions of the electronic medical record system to identify and confirm a patient file for receiving data and personality modules for receiving and translating data from a variety of clinical device monitors for that identified patient.

Systems and methods are provided for calculating athletic activity parameters. Multiple housings are position at different locations on a user's body. The housings are configured to be removably engaged with an electronic module that includes a sensor and a processor configured to calculate athletic activity parameters. Each housing is connected to or includes an identification memory that stores information identifying a location of the housing. The electronic module uses the location information to select an algorithm to use when calculating the athletic activity parameters.

Systems and methods are provided for calculating athletic activity parameters. Multiple housings are position at different locations on a user's body. The housings are configured to be removably engaged with an electronic module that includes a sensor and a processor configured to calculate athletic activity parameters. Each housing is connected to or includes an identification memory that stores information identifying a location of the housing. The electronic module uses the location information to select an algorithm to use when calculating the athletic activity parameters.

A method for creating a patient-specific surgical plan includes receiving one or more pre-operative images of a patient having one or more infirmities affecting one or more anatomical joints. three-dimensional anatomical model of the one or more anatomical joints is created based on the one or more pre-operative images. One or more transfer functions and the three-dimensional anatomical model are used to identify a patient-specific implantation geometry that corrects the one or more infirmities. The transfer functions model performance of the one or more anatomical joints as a function of anatomical geometry and anatomical implantation features. surgical plan comprising the patient-specific implantation geometry may then be displayed.

A system and method for training a text report identification machine learning model and an image identification machine learning model, including: initially training a text report machine learning model, using a labeled set of text reports including text pre-processing the text report and extracting features from the pre-processed text report, wherein the extracted features are input into the text report machine learning model; initially training an image machine learning model, using a labeled set of images; applying the initially trained text report machine learning model to a first set of unlabeled text reports with associated images to label the associated images; selecting a first portion of labeled associated images; re-training the image machine learning model using the selected first portion of labeled associated images; applying the initially trained image machine learning model to a first set of unlabeled images with associated text reports to label the associated text reports; selecting a first portion of labeled associated text reports; and re-training the text report machine learning model using the selected first portion of labeled associated text reports.

A system and method for training a text report identification machine learning model and an image identification machine learning model, including: initially training a text report machine learning model, using a labeled set of text reports including text pre-processing the text report and extracting features from the pre-processed text report, wherein the extracted features are input into the text report machine learning model; initially training an image machine learning model, using a labeled set of images; applying the initially trained text report machine learning model to a first set of unlabeled text reports with associated images to label the associated images; selecting a first portion of labeled associated images; re-training the image machine learning model using the selected first portion of labeled associated images; applying the initially trained image machine learning model to a first set of unlabeled images with associated text reports to label the associated text reports; selecting a first portion of labeled associated text reports; and re-training the text report machine learning model using the selected first portion of labeled associated text reports.