CPC Class G16H

Methods, computer systems, and computer-storage medium are provided for providing closed-loop intelligence. A selection of data is received, at a cloud service, from a database comprising data from a plurality of sources in a Fast Healthcare Interoperability Resources (FHIR) format to build a data model. After a feature vector corresponding to the data model is extracted, a selection of an algorithm for a machine learning model to apply to the data model is received. A portion of the selection of data is utilized for training data and test data and the machine learning model is applied to the training data. Once the model is trained, the trained machine learning model can be saved at the cloud service, where it may be accessed by others.

The present invention relates to a method for assisting an interviewing party in deciding a response action in response to an interview between said interviewing party and an interviewee party. The method comprises providing a processing unit and inputting the voice of the interviewee party into the processing unit as an electronic signal, and processing the electronic signal by means of said processing unit in parallel with the interview taking place. The method further includes an anomaly routine comprising a statistically learned model, and by means of said statistically learned model determining a respective number of samples of said sequence of samples being an anomaly of said statistically learned model and returning to said anomaly routine for processing a subsequent number of samples of said sequence of samples by said anomaly routine.

A medical imaging system ( 100, 300, 400, 700 ) includes a processor and memory with instructions executable by the processor to receive ( 200 ) three-dimensional medical image data ( 122 ) comprising multiple slices, receive ( 202 ) an imaging modality ( 124 ) of the three-dimensional medical image data, receive ( 204 ) an anatomical view classification ( 126 ) of the three-dimensional medical image data, select ( 206 ) a chosen abnormality detection module ( 130 ) from a set of abnormality detection modules ( 128 ) using the imaging modality and the anatomical view classification, wherein at least a portion of the abnormality detection modules is a convolution neural network trained for identifying if the at least a portion of the multiple slices as either normal or abnormal, classify ( 208 ) the at least a portion of the multiple slices as normal or abnormal using the abnormality detection module, and choose ( 210 ) a set of selected slices ( 136 ) from the multiple slices according to a predetermined selection criteria ( 134 ) if a predetermined number of the multiple slices are classified as abnormal.