Learning patient monitoring and intervention system

• US 9,375,142 B2
• Filed: 12/18/2012
• Issued: 06/28/2016
• Est. Priority Date: 03/15/2012
• Status: Active Grant

• Current Assignee: Siemens Healthineers AG (Siemens AG)
• Original Assignee: Siemens AG
• Inventors: Schultz, Christian P, Russell, Steven A, Rosca, Justinian, Claussen, Heiko, Joiner, Carolyn, Tas, Nazif
• Primary Examiner(s): Thomson, William
• Assistant Examiner(s): JIAN, SHIRLEY XUEYING
• Application Number:
  US13/718,023
• Publication Number:
  US 20130245389A1
• Time in Patent Office: 1,288 Days
• Field of Search: 600300-301
• US Class Current: 1/1
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/0077 Devices for viewing the surface of the body, e.g. camera, magnifying lens
• CPC Class Codes: A61B 5/02 Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/0813 Measurement of pulmonary parameters by tracers, e.g. radioactive tracers
• CPC Class Codes: A61B 5/14542 for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/68 Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
• CPC Class Codes: A61B 5/6892 Mats
• CPC Class Codes: G08B 21/0461 integrated or attached to an item closely associated with the person but not worn by the person, e.g. chair, walking stick, bed sensor
• CPC Class Codes: G08B 25/10 using wireless transmission systems G08B25/009 takes precedence
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A patient monitoring and intervention system, comprises an interface for receiving data representing multiple different parameters from multiple different sensors, comprising sensors in a patient bed and attached to a patient including, a heart rate sensor, a respiration sensor and a pressure sensor indicating bed pressure points. A learning processor determines a normal range for a set of the different received patient parameters for the patient by recording the patient parameter values over a time period and analyzing the recorded parameter values to determine their range. A data processor determines if the set of different received patient parameters exceeds the determined normal range and in response to this determination and in response to the type of parameters in the set and medical record information of the patient, initiates adjustment of a patient bed and at least one of, (a) changes medication administered to a patient and (b) alerts a worker of the patient parameter change.

Claims

1. A patient monitoring and intervention system, comprising:

an interface for receiving data representing a plurality of different parameters from a plurality of different sensors, comprising sensors in a patient bed and configured to be attached to a patient including,

a heart rate sensor,

a respiration sensor and

a pressure sensor indicating bed pressure points;

a learning processor for determining a normal range for a set of a plurality of said different received patient parameters for said patient by recording the patient parameter values over a time period and analyzing the recorded parameter values to determine their range;

a data processor in communication with a first database and a second database, said processor for selecting, based on a configurable lookup table of medical conditions, said plurality of different sensors from predetermined available sensors in response to at least patient medical condition data of the patient derived from a first database, and for determining the set of different received patient parameters that exceeds the determined normal range;

one or more actuators in communication with the data processor and connected to the patient bed, wherein the data processor for adjusting, via the one or more actuators, the patient bed in response to this determination and in response to type of parameters in the set and medical record information of said patient;

said data processor for selecting said plurality of different sensors further in response to at least one of,

(a) decisions of a clinician in a comparable medical case stored in the second database, and

(b) the amount of available data stored in the second database; and

a display in communication with the data processor for alerting a worker of the a patient parameter change.

2. A system according to claim 1, wherein

said data processor for selecting said set of said plurality of said different received patient parameters from a plurality of different sets in response to an individual parameter of said set exceeding a predetermined threshold.

3. A system according to claim 1, wherein

said sensors include a chemical sensor for sensing a chemical parameter of a body fluid and said set of a plurality of said different received patient parameters includes said chemical parameter.

4. A system according to claim 1, wherein

said sensors include a sensor located in at least one of, (a) a mattress, (b) a pillow and (c) a support member of a bed.

5. A system according to claim 1, wherein

said sensors include a microphone for detecting at least one of breath, heartbeat and gastric sounds or vibrations and said data processor automatically analyzes signal data representing a detected sound to provide a sound parameter and said set of a plurality of said different received patient parameters includes said sound parameter.

6. A system according to claim 1, wherein

said sensors include at least one of, (a) a vibration sensor and (b) a camera for detecting at least one of pulse, shivering and seizure and said data processor automatically analyzes signal data representing a detected vibration to provide a parameter derived from vibration and said set of a plurality of said different received patient parameters includes said parameter derived from vibration.

7. A system according to claim 1, wherein

said sensors include at least one vital sign sensor for sensing at least one of blood pressure, blood oxygen saturation SPO2, an ECG signal and temperature and said set of a plurality of said different received patient parameters includes a vital sign parameter.

8. A system according to claim 1, wherein

said pressure sensor for indicating said bed pressure points including at least one of tension, body position, and local high-pressure points where tissues may be compressed.

9. A system according to claim 1, wherein

said data processor for automatically initiating automatic prioritizing treatment, in response to said determination of the set of different received patient parameters exceeding the determined normal range.

10. A system according to claim 1, wherein

said sensors include proteomics or genomic expression sensors.

11. A system according to claim 1, further comprising a third database in communication with the data processor, wherein the third database stores data from a patient population having similar demographics including age, weight, height, gender, pregnancy status as the patient and similar medical conditions, wherein the data processor derives said normal range from the data.

12. A system according to claim 1, further comprising a third database in communication with the learning processor, wherein the third database for storing training data, wherein said learning processor for adaptively selecting, based on amount of the training data available, from a plurality of different predetermined functions, a function employed by said learning processor for determining at least one of, (a) a normal range and (b) an abnormal range, for said set of a plurality of said different received patient parameters in response to at least one of, (i) the amount of recorded patient data available from sensors and (ii) the type of recorded patient data available from sensors.

13. A system according to claim 12, wherein

said plurality of different functions include at least two of, (a) a one class support vector machine, (b) a one class relevance vector machine and (c) a multiple class relevance vector machine.

14. A system according to claim 12, wherein

said data processor, in response to determining said set of said plurality of said different received patient parameters exceeds the determined normal range, for adaptively selecting an action to be performed, wherein said selection is made in response to at least one of, (a) the amount of recorded patient data available from sensors, (b) the type of recorded patient data available from sensors and (c) the type of the selected function.

15. A system according to claim 12, wherein

said data processor, in response to determining said set of said plurality of said different received patient parameters exceeds the determined normal range, for adaptively selecting from a plurality of predetermined actions, an action to be performed, wherein said selection is made in response to at least one of, (a) a medical condition of the patient and (b) the criticality of said different received patient parameters.

16. A system according to claim 15, wherein

said plurality of predetermined actions include, initiating adjusting a patient bed, changing medication administered to a patient, alerting a worker of the patient parameter change, labeling a parameter from a sensor as indicated by a clinician and labeling parameters from sensors for use in training.

17. A system according to claim 1, including

a mesh communication network deployed by said system, said network for enabling said devices in said system to collaboratively maintain connectivity, wherein said devices include said sensors.

18. A patient monitoring and intervention system, comprising:

an interface for receiving data representing a plurality of different parameters from a plurality of different sensors, comprising sensors in a patient bed and configured to be attached to a patient;

a learning processor for determining a normal range for a set of a plurality of said different received patient parameters for said patient by recording the patient parameter values over a time period and analyzing the recorded parameter values to determine their range;

a data processor for selecting, based on a configurable lookup table of medical conditions, said plurality of different sensors from predetermined available sensors in response to at least patient medical condition data of the patient derived from a first database, and in response to determining said set of said plurality of said different received patient parameters exceeds the determined normal range, for adaptively selecting an action to be performed from a plurality of different predetermined actions in response to at least one of,

(a) a medical condition of the patient and

(b) the criticality of said different received patient parameters,

said actions comprising at least one of,

(i) initiating, via one or more actuators in communication with the data processor, adjusting a patient bed, and

(ii) changing, via a medicine dispenser in communication with the data processor, medication administered to the patient; and

a second database in communication with the data processor, wherein said data processor selects said plurality of different sensors further in response to at least one of,

(a) decisions of a clinician in a comparable medical case stored in the second database, and

(b) the amount of available data stored in the second database;

a display in communication with the data processor for alerting a worker of the patient parameter change.

19. A patient monitoring and intervention system, comprising:

an interface for receiving data representing a plurality of different parameters from a plurality of different sensors, comprising sensors in a patient bed and configured to be attached to a patient;

a learning processor for,

adaptively selecting, based on amount of training data available, from a plurality of different predetermined functions, a function for determining at least one of, (a) a normal range and (b) an abnormal range, for said set of a plurality of said different received patient parameters, in response to at least one of, (i) the amount of recorded patient data and (ii) the type of recorded patient data, available from said plurality of different sensors and

determining a normal range or a degree of abnormality for said set of said plurality of said different received patient parameters for said patient by recording the patient parameter values over a time period and using the selected function in analyzing the recorded parameter values to determine their range;

a data processor, in response to determining said set of said plurality of said different received patient parameters exceeds the determined normal range, for adaptively selecting an action from multiple different predetermined actions to be performed in response to at least one of, (a) the amount of recorded patient data available from sensors, (b) the type of recorded patient data available from sensors and (c) the type of the selected function; and

a display in communication with the data processor for alerting a worker of the patient parameter change.

20. A method for patient monitoring and intervention, comprising the activities of:

receiving, via an interface, data representing a plurality of different parameters from a plurality of different sensors, comprising sensors in a patient bed and configured to be attached to a patient including,

a heart rate sensor,

a respiration sensor and

a pressure sensor indicating bed pressure points;

determining, by a learning processor, a normal range for a set of a plurality of said different received patient parameters for said patient by recording the patient parameter values over a time period and analyzing the recorded parameter values to determine their range;

selecting, by a data processor, based on a configurable lookup table of medical conditions, said plurality of different sensors from predetermined available sensors in response to patient medical condition data of the patient and at least one of,

(a) decisions of a clinician in a comparable medical case stored in the second database, and

(b) the amount of available data stored in the second database;

determining, by the data processor, said set of different received patient parameters exceeds the determined normal range and in response to this determination and type of parameters in the set and medical record information of said patient, initiating adjusting a patient bed; and

communicating, by a communication device, a message to alert a worker of the patient parameter change.