To enable automated processing of certain observation data by machine-learning based models, as well as to ensure those machine-learning based models are trained utilizing reliable and consistent data, independently generated observation data records each comprising biomarker mutation indicators, are utilized to generate a model input data set by applying biomarker mutation indicator-based filters, including intra-date filters and inter-date filters to identify and rectify observation data records that do not satisfy applicable biomarker mutation indicators. By identifying and rectifying the observation data records that do not satisfy applicable biomarker mutation indicators, a clean, model input data set is generated that is then utilized to generate a severity score for the patient.

To automatically model oncology treatment severity levels for a particular patient, a plurality of independently generated and potentially inconsistent observation data records are received for a patient. A condition-specific plurality of observation data records each having a common cancer type identifier are selected, and those condition-specific observation records are filtered to potentially eliminate one or more observation data records failing to satisfy one or more preliminary filter criteria. A specific pre-processing process for the remaining observation data records is executed to generate one or more output data records having at least one shared identifier, and the output data records are provided as model input to a machine-learning based severity model to generate a severity score for the patient.

A computer system includes processor hardware configured to execute instructions from memory hardware. The instructions include training a machine learning model to generate an entity expiration likelihood output, obtaining a set of multiple database entities, and processing, by the machine learning model, feature vector inputs associated with each database entry to generate an entity expiration likelihood output. The instructions include determining a subset of the database entities having the highest entity expiration likelihood outputs, and, for each database entity in the subset, determining output impact scores for parameters of the feature vector input associated with the database entity, generating a feature list based on the determined output impact scores, and automatically selecting an executable sequence according to the entity expiration likelihood output associated with the database entity. The feature list is specific to the database entity and includes one or more of the parameters having the highest output impact scores.

A drive-through treatment system for a drive-through clinic includes: a plurality of monitoring devices positioned at an inlet of a plurality of treatment lines; a plurality of treatment devices positioned on a plurality of medical shelters in each of the plurality of treatment lines; a plurality of outlet devices positioned at an outlet of the plurality of treatment lines; and a treatment line determining device for determining a treatment line into which a target vehicle to enter the drive-through clinic will enter among the plurality of treatment lines based on information received from a plurality of monitoring devices and a plurality of outlet devices. Each of the plurality of monitoring devices may check a treatment start, a treatment progress situation, and a treatment speed of vehicles that have entered the treatment line from the inlet of the corresponding treatment line to be transmitted to the treatment line determining device.

An apparatus for providing a bundled set of individually redeemable healthcare services in a purchase data record, determining a Good Faith Estimate (GFE) for the at least one healthcare service of the bundled set, associating the purchase data record with user debt for purchase of the healthcare services, providing a digital health asset token representing the purchase data record identified by and with a unique confirmation number, said purchase data record comprising the associated user debt of the at least one bundled set of healthcare services, and providing marketplace access to the token to finance the debt. The marketplace may use the token for buying, selling or trading the bundled set of healthcare services to finance the debt. The token and a Good Faith Estimate (GFE) may be provided to an Independent Dispute Resolution (IDR) process for resolving a dispute under the No Surprises Act.

There is provided a computer implemented method for detection of likelihood of malignancy in an anatomical image of a patient for planning treatment thereof, comprising: receiving a pair of images that are either of a same patient or from two different patients, wherein the pair of images comprises anatomically corresponding anatomical images each depicting internal anatomical structures of the patient, feeding the pair of images into a model, outputting by the model, an indication of whether the pair of images are of a same patient or not, and generating an indication of likelihood of malignancy when the model wrongly outputs that the pair of images are not of the same patient, when in fact the pair of images are of the same patient, wherein treatment of the patient is planned according to the indication of likelihood of malignancy.

A method for operating an administrator viewer on an administrator computing device. The method includes receiving first information pertaining to a service performed by a person for a user. The method further includes receiving an action instruction generated by an artificial intelligence engine of a cognitive intelligence platform based on a data structure associated with a user and a condition of the user, wherein the action instruction specifies that an administrator performs an action tailored for the user based on the first information. The method further includes presenting the first information and the action instruction in a screen of the administrator viewer.

The present disclosure relates to a clinical workflow, method, system and apparatus for performing an remote ocular health examination of a patient using medical equipment adapted to apparatus and emulating software and remote administration tool technology application. According to various embodiments portable medical equipment kit (OEK) can be assembled anywhere in the world connected to a computer. The MCB will have proprietary software to provide indirect to direct connection via layers. In an example embodiment, a method includes instructing a patient to position their eyes onto a camera where the image of the eyes are viewed on computer software. Where an on demand doctor can control the software that controls the cameras and review real time or near realtime the ocular images or videos via a indirect to direct connection to the main control base via a remote administration tool technology and emulating software using an internet connection.

An image processing system includes at least one processor including hardware. The processor performs a process that acquires, as a processing target image sequence, images captured in a time series manner with an inside of a living body by an endoscope imaging device, a process based on a database generated by using a plurality of in-vivo images captured at earlier timings than timings at which the processing target image sequence is captured, and a process that determines, when a bleeding has been occurring inside the living body, a kind of a desirable bleeding stopping treatment for a blood vessel on which the bleeding has been occurring based on the processing target image sequence and the database to present, to a user, the determined type of the bleeding stopping treatment.

An image processing system includes at least one processor including hardware. The processor performs a process that acquires, as a processing target image sequence, images captured in a time series manner with an inside of a living body by an endoscope imaging device, a process based on a database generated by using a plurality of in-vivo images captured at earlier timings than timings at which the processing target image sequence is captured, and a process that determines, when a bleeding has been occurring inside the living body, a kind of a desirable bleeding stopping treatment for a blood vessel on which the bleeding has been occurring based on the processing target image sequence and the database to present, to a user, the determined type of the bleeding stopping treatment.