# US 12,002,583 B2 · Universal health machine for the automatic assessment of patients > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,002,583 B2 - Document ID: US12002583B2 - Status: Granted - Kind: B2 - Application number: 17/126,408 - Filed: 2020-12-18 - Published: 2022-06-23 - Granted: 2024-06-04 - Earliest claimed: 2020-12-18 - Assignees: Siemens Healthineers AG - Applicants: Siemens Healthineers AG - Inventors: Ahmet Tuysuzoglu; Dorin Comaniciu; Tommaso Mansi - Web page: https://patentgrep.com/patent/US12002583B2 ## CPC - A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor} - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - A61B 5/0205: Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition - A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems} - A61B 5/7267: {involving training the classification device} - G06F 16/258: {Data format conversion from or to a database} - G06N 3/045: Combinations of networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/08: Learning methods - G06N 3/084: Backpropagation, e.g. using gradient descent - G06N 3/09: Supervised learning - G06N 5/025: Extracting rules from data - G16H 10/20: for electronic clinical trials or questionnaires - G16H 10/60: for patient-specific data, e.g. for electronic patient records - G16H 15/00: ICT specially adapted for medical reports, e.g. generation or transmission thereof - G16H 50/30: for calculating health indices; for individual health risk assessment - G16H 50/50: for simulation or modelling of medical disorders - A61B 5/0022: {Monitoring a patient using a global network, e.g. telephone networks, internet} - A61B 5/01: Measuring temperature of body parts {; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue} (clinical contact thermometers G01K13/20) - A61B 5/021: Measuring pressure in heart or blood vessels - A61B 5/024: Measuring pulse rate or heart rate - A61B 5/0816: {Measuring devices for examining respiratory frequency (measuring frequency of electric signals G01R23/00)} - A61B 5/082: {Evaluation by breath analysis, e.g. determination of the chemical composition of exhaled breath (A61B5/083, A61B5/091 take precedence)} - A61B 5/14542: {for measuring blood gases (A61B5/14551 takes precedence)} - A61B 5/318: Heart-related electrical modalities, e.g. electrocardiography [ECG] - G16H 40/20: for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Systems and methods for automatically determining an assessment of a patient are provided. A patient is automatically interacted with, by a first trained machine learning based model, to acquire initial patient data. One or more risk factors associated with the patient are automatically determined, by a second trained machine learning based model, based on the received initial patient data. The patient is automatically interacted with, by the first trained machine learning based model, to acquire additional patient data based on the one or more determined risk factors. An assessment of the patient is automatically determined, by the second trained machine learning based model, based on the initial patient data and the additional patient data. The assessment of the patient is output. ## Claims ### Claim 1 A method for automatically determining an assessment of a patient, comprising: automatically interacting with a patient, by a first trained machine learning based model guided by a second trained machine learning based model, to acquire initial patient data; automatically retrieving, by a third trained machine learning based model guided by the second trained machine learning based model, health related information from one or more databases; automatically determining one or more risk factors associated with the patient, by the second trained machine learning based model, based on the acquired initial patient data and the retrieved health related information; presenting a consolidated information view of the one or more risk factors to the patient via a display device; automatically interacting with the patient, by the first trained machine learning based model guided by the second trained machine learning based model, using the presented consolidated information view to acquire additional patient data based on 1) the one or more determined risk factors and 2) data required by one or more physiology models of the patient for simulating one or more clinical scenarios; updating the presented consolidated information view of the one or more risk factors based on the additional patient data as the additional patient data is acquired; automatically determining the assessment of the patient, by the second trained machine learning based model, based on the initial patient data and the additional patient data using the one or more physiology models simulating the one or more clinical scenarios; and displaying the assessment of the patient on the display device. ### Claim 2 (depends on 1) The method of claim 1, wherein automatically determining the assessment of the patient, by the second trained machine learning based model, based on the initial patient data and the additional patient data using the one or more physiology models simulating the one or more clinical scenarios comprises: processing the initial patient data and the additional patient data into a particular format; and inputting the processed initial patient data and the processed additional patient data into the one or more physiology models of the patient. ### Claim 3 (depends on 1) The method of claim 1, wherein automatically determining the assessment of the patient, by the second trained machine learning based model, based on the initial patient data and the additional patient data using the one or more physiology models simulating the one or more clinical scenarios comprises: determining the assessment of the patient with an associated level of confidence. ### Claim 4 (depends on 1) The method of claim 1, wherein automatically determining the assessment of the patient, by the second trained machine learning based model, based on the initial patient data and the additional patient data using the one or more physiology models simulating the one or more clinical scenarios comprises: determining a diagnosis for a medical condition associated with the patient. ### Claim 5 (depends on 1) The method of claim 1, wherein automatically determining the assessment of the patient, by the second trained machine learning based model, based on the initial patient data and the additional patient data using the one or more physiology models simulating the one or more clinical scenarios comprises: determining a recommended course of action for the patient. ### Claim 6 (depends on 1) The method of claim 1, wherein the one or more risk factors comprises at least one of genetic risk factors, environmental risk factors, and lifestyle related risk factors. ### Claim 7 (depends on 1) The method of claim 1, wherein automatically interacting with the patient, by the first trained machine learning based model guided by the second trained machine learning based model, using the presented consolidated information view to acquire additional patient data based on 1) the one or more determined risk factors and 2) data required by one or more physiology models of the patient for simulating one or more clinical scenarios comprises: asking questions to the patient and receiving answers from the patient. ### Claim 8 (depends on 1) The method of claim 1, wherein automatically interacting with the patient, by the first trained machine learning based model guided by the second trained machine learning based model, using the presented consolidated information view to acquire additional patient data based on 1) the one or more determined risk factors and 2) data required by one or more physiology models of the patient for simulating one or more clinical scenarios comprises: retrieving clinical measurements of the patient. ### Claim 9 An apparatus for automatically determining an assessment of a patient, comprising: means for automatically interacting with a patient, by a first trained machine learning based model guided by a second trained machine learning based model, to acquire initial patient data; means for automatically retrieving, by a third trained machine learning based model guided by the second trained machine learning based model, health related information from one or more databases; means for automatically determining one or more risk factors associated with the patient, by the second trained machine learning based model, based on the acquired initial patient data and the retrieved health related information; means for presenting a consolidated information view of the one or more risk factors to the patient via a display device; means for automatically interacting with the patient, by the first trained machine learning based model guided by the second trained machine learning based model, using the presented consolidated information view to acquire additional patient data based on 1) the one or more determined risk factors and 2) data required by one or more physiology models of the patient for simulating one or more clinical scenarios; means for updating the presented consolidated information view of the one or more risk factors based on the additional patient data as the additional patient data is acquired; means for automatically determining the assessment of the patient, by the second trained machine learning based model, based on the initial patient data and the additional patient data using the one or more physiology models simulating the one or more clinical scenarios; and means for displaying the assessment of the patient on the display device. ### Claim 10 (depends on 9) The apparatus of claim 9, wherein the means for automatically determining the assessment of the patient, by the second trained machine learning based model, based on the initial patient data and the additional patient data using the one or more physiology models simulating the one or more clinical scenarios comprises: means for processing the initial patient data and the additional patient data into a particular format; and means for inputting the processed initial patient data and the processed additional patient data into the one or more physiology models of the patient. ### Claim 11 (depends on 9) The apparatus of claim 9, wherein the means for automatically determining the assessment of the patient, by the second trained machine learning based model, based on the initial patient data and the additional patient data using the one or more physiology models simulating the one or more clinical scenarios comprises: means for determining the assessment of the patient with an associated level of confidence. ### Claim 12 (depends on 9) The apparatus of claim 9, wherein the means for automatically determining the assessment of the patient, by the second trained machine learning based model, based on the initial patient data and the additional patient data using the one or more physiology models simulating the one or more clinical scenarios comprises: means for determining a diagnosis for a medical condition associated with the patient. ### Claim 13 A non-transitory computer readable medium storing computer program instructions for automatically determining an assessment of a patient, the computer program instructions when executed by a processor cause the processor to perform operations comprising: automatically interacting with a patient, by a first trained machine learning based model guided by a second trained machine learning based model, to acquire initial patient data; automatically retrieving, by a third trained machine learning based model guided by the second trained machine learning based model, health related information from one or more databases; automatically determining one or more risk factors associated with the patient, by the second trained machine learning based model, based on the acquired initial patient data and the retrieved health related information; presenting a consolidated information view of the one or more risk factors to the patient via a display device; automatically interacting with the patient, by the first trained machine learning based model guided by the second trained machine learning based model, using the presented consolidated information view to acquire additional patient data based on 1) the one or more determined risk factors and 2) data required by one or more physiology models of the patient for simulating one or more clinical scenarios; updating the presented consolidated information view of the one or more risk factors based on the additional patient data as the additional patient data is acquired; automatically determining the assessment of the patient, by the second trained machine learning based model, based on the initial patient data and the additional patient data using the one or more physiology models simulating the one or more clinical scenarios; and displaying the assessment of the patient on the display device. ### Claim 14 (depends on 13) The non-transitory computer readable medium of claim 13, wherein automatically determining the assessment of the patient, by the second trained machine learning based model, based on the initial patient data and the additional patient data using the one or more physiology models simulating the one or more clinical scenarios comprises: determining a recommended course of action for the patient. ### Claim 15 (depends on 13) The non-transitory computer readable medium of claim 13, wherein the one or more risk factors comprises at least one of genetic risk factors, environmental risk factors, and lifestyle related risk factors. ### Claim 16 (depends on 13) The non-transitory computer readable medium of claim 13, wherein automatically interacting with the patient, by the first trained machine learning based model guided by the second trained machine learning based model, using the presented consolidated information view to acquire additional patient data based on 1) the one or more determined risk factors and 2) data required by one or more physiology models of the patient for simulating one or more clinical scenarios comprises: asking questions to the patient and receiving answers from the patient. ### Claim 17 (depends on 13) The non-transitory computer readable medium of claim 13, wherein automatically interacting with the patient, by the first trained machine learning based model guided by the second trained machine learning based model, using the presented consolidated information view to acquire additional patient data based on 1) the one or more determined risk factors and 2) data required by one or more physiology models of the patient for simulating one or more clinical scenarios comprises: retrieving clinical measurements of the patient.