Description:Title of The Invention
Machine learning and edge based scalable system for real-time and automatic pre-diagnosis of patient for hospitals
Field of the Invention
This invention relates to machine learning and edge based scalable system for real-time and automatic pre-diagnosis of patient for hospitals
Background of the Invention
CN107076746B: A method of analyzing biological data comprising expression values of polypeptides in the blood of a subject is disclosed. The method comprises the following steps: a distance between a segment of a curve and an axis defined by a direction is calculated at a point above the curve defined by a coordinate along the direction. The method further comprises correlating the distance to the presence, absence, or likelihood of the subject having a bacterial infection. The coordinates are defined by a combination of the expression values, wherein at least 90% of the segments are between a lower bound line and an upper bound line.
US9501624B2: Methods and systems for automatically establishing an enhanced electronic health record (EHR) for a patient include an automatic data collection facility that collects data of a medically related event in proximity to a patient upon occurrence of the event. The collected data may include medication administration data such as medication, time of administration, administration of a dosage of medication, reaction data, and the like. The collected data is communicated to a real-time data integration facility that automatically integrates the data with a patient's electronic health record to establish an enhanced electronic health record.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. Present invention is help to display all the information related to body factors like your body temperature, oxygen level, pulse rate, etc.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention. This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
It is machine learning and edge based scalable system for real-time and automatic pre-diagnosis of patient for hospitals which consists of two nodes Pre-diagnosis node and edge based analytic node. Pre-diagnosis node (10) it is a node which contain all the information about the body temperature, fingerprint, pulse rate, oxygen level, spo2 .Edge based analytic node (20) this node is work with the help of machine learning model and there is a co-processor which give additional computing power and LoRA will be used for long range communication. Cloud server (21) to make them accessible to users from remote locations server, Mobile App (22) we can see all the information via internet. This Pre-Diagnosis figures. 2 consists of computing unit (30) will be control the entire system of pre-diagnosis, Fingerprint Sensor (37) it mainly senses the health by finger touch on fingerprint sensor, Temperature sensor (38) will sense the body temperature, Pulse rate sensor (39) it basically senses the pulse rate of body, Spo2 sensor (40) this will sense the oxygen level, display unit (41) it displays all the information about the sensor what they have sense about the boy factors, LoRA (44) this provides a long-range communication to communicate, LED (43) it will display the health condition of body, and these all sensor work with the help of battery power supply (42) because it provides power to entire system. This figure. 3 Edge based analytic node consists of computing unit (101) this will control the entire system, Machine learning model (102) by applying machine learning in this device we train our model by testing/training which help our device to give accurate result, LoRA module (103) this will help for long range communication, Wi-Fi module (104) this work similar like internet It provide system to work via internet, external power supply (105) this will provide power to entire system.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
Pre-diagnosis node (10) it is a node which contain all the information about the body temperature, fingerprint, pulse rate, oxygen level, spo2 .Edge based analytic node (20) this node is work with the help of machine learning model and there is a co-processor which give additional computing power and LoRA will be used for long range communication. Cloud server (21) to make them accessible to users from remote locations server, Mobile App (22) we can see all the information via internet. This Pre-Diagnosis figures. 2 consists of computing unit (30) will be control the entire system of pre-diagnosis, Fingerprint Sensor (37) it mainly senses the health by finger touch on fingerprint sensor, Temperature sensor (38) will sense the body temperature, Pulse rate sensor (39) it basically senses the pulse rate of body, Spo2 sensor (40) this will sense the oxygen level, display unit (41) it displays all the information about the sensor what they have sense about the boy factors, LoRA (44) this provides a long-range communication to communicate, LED (43) it will display the health condition of body, and these all sensor work with the help of battery power supply (42) because it provides power to entire system. This figure. 3 Edge based analytic node consists of computing unit (101) this will control the entire system, Machine learning model (102) by applying machine learning in this device we train our model by testing/training which help our device to give accurate result, LoRA module (103) this will help for long range communication, Wi-Fi module (104) this work similar like internet It provide system to work via internet, external power supply (105) this will provide power to entire system.
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
It should be noted that the description merely illustrates the principles of the present subject matter. It will thus be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described herein, embody the principles of the present subject matter and are included within its scope.
Pre-diagnosis node (10) it is a node which contain all the information about the body temperature, fingerprint, pulse rate, oxygen level, spo2 .Edge based analytic node (20) this node is work with the help of machine learning model and there is a co-processor which give additional computing power and LoRA will be used for long range communication. Cloud server (21) to make them accessible to users from remote locations server, Mobile App (22) we can see all the information via internet. This Pre-Diagnosis figures. 2 consists of computing unit (30) will be control the entire system of pre-diagnosis, Fingerprint Sensor (37) it mainly senses the health by finger touch on fingerprint sensor, Temperature sensor (38) will sense the body temperature, Pulse rate sensor (39) it basically senses the pulse rate of body, Spo2 sensor (40) this will sense the oxygen level, display unit (41) it displays all the information about the sensor what they have sense about the boy factors, LoRA (44) this provides a long-range communication to communicate, LED (43) it will display the health condition of body, and these all sensor work with the help of battery power supply (42) because it provides power to entire system. This figure. 3 Edge based analytic node consists of computing unit (101) this will control the entire system, Machine learning model (102) by applying machine learning in this device we train our model by testing/training which help our device to give accurate result, LoRA module (103) this will help for long range communication, Wi-Fi module (104) this work similar like internet It provide system to work via internet, external power supply (105) this will provide power to entire system.

ADVANTAGES OF THE INVENTION:
• This proposed system will patient to see all the information about her body.
• All the information about the patient can be easily accessed through web application.
• There is also a Display unit which display about patient Health on Screen.
• This system consists of two nodes pre-diagnosis node and edge based analytic node.
, Claims:We Claim:
1. A Machine learning and edge based scalable system for real-time and automatic pre-diagnosis of patient for hospitals comprises with Cloud server (21), Mobile App (22), Sensor (37), Temperature sensor (38), Spo2 sensor (40), display unit (41), battery power supply (42), computing unit (101), machine learning model (102), LoRA module (103), Wi-Fi module (104), external power supply (105).
2. The system as claimed in claim 1, wherein which is cloud server (21) to make them accessible to users from remote locations server, Mobile App (22) we can see all the information via internet.
3. The system as claimed in claim 1, wherein which is computing unit (30) controls the entire system of pre-diagnosis, Fingerprint Sensor (37) it mainly senses the health by finger touch on fingerprint sensor.
4. The system as claimed in claim 1, wherein temperature sensor (38) senses the body temperature, Pulse rate sensor (39) it basically senses the pulse rate of body, Spo2 sensor (40) this senses the oxygen level, display unit (41) it displays all the information about the sensor what they have sense about the boy factors,
5. The system as claimed in claim 1, wherein LoRA (44) provides a long-range communication to communicate, LED (43) it displays the health condition of body, and these all sensor work with the help of battery power supply (42) because it provides power to entire system.
6. The system as claimed in claim 1, wherein which is Edge based analytic node consists of computing unit (101) which controls the entire system, Machine learning model (102) by applying machine learning.