Learning patient monitoring and intervention system

Patent metadata

Field	Value
Publication number	US-9375142-B2
Application number	US-201213718023-A
Country	US
Kind code	B2
Filing date	Dec 18, 2012
Priority date	Mar 15, 2012
Publication date	Jun 28, 2016
Grant date	Jun 28, 2016

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.

First claim

What is claimed is:

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 dete

Assignees

• Schultz Christian P
• Rosca Justinian
• Claussen Heiko
• Joiner Carolyn
• Russell Steven A
• Tas Nazif
• Siemens Ag

Inventors

• Schultz Christian P
• Rosca Justinian
• Claussen Heiko
• Joiner Carolyn
• Russell Steven A
• Tas Nazif

Classifications

• G08B25/10

  using wireless transmission systems {(G08B25/009 takes precedence)} · CPC title

• G16H40/67

  for remote operation · CPC title

• G16H50/70

  for mining of medical data, e.g. analysing previous cases of other patients · CPC title

• A61B5/6892

  Mats · CPC title

• A61B5/0813

  Measurement of pulmonary parameters by tracers, e.g. radioactive tracers · CPC title