TWI789040B - Device, method, and system for collecting, managing and displaying physiological data - Google Patents

Abstract

The present disclosure discloses a device and a method for collecting, managing and displaying physiological data. The device comprises physiological data baseline value determination module, optimization range determination module, sampling instrument connection module, physiological data quality analysis module and physiological data optimization analysis module, wherein, the physiological data baseline value determination module and the optimization range determination module are connected with the sampling instrument connection module and/or the physiological data optimization analysis module, preferably connected with the sampling instrument connection module and the physiological data optimization analysis module respectively. Based on the information of a management object, the best original physiological data can be obtained through calculation and comparison, the physiological indexes and parameters which required for optimal management can be determined automatically, high quality physiological data can be obtained from the massive data of multiple sampling instruments, the physiological data can be obtained accuracy, completely and timely (higher frequency), and big data can be processed directly.

Description

Physiological data collection, management, display device, method and system

This application claims the priority of the previous application with the patent application number 202011062612.5 and the title of the invention "Physiological Data Collection, Management, Display Device and Method" submitted by the applicant to the State Intellectual Property Office of China on September 30, 2020. The entirety of said prior application is incorporated by reference into this application.

The invention belongs to the field of data processing and analysis, and in particular relates to a device and method for collecting, managing and displaying physiological data.

With the development of medical testing and communication technologies, there are a wide variety of instruments used for sampling, analyzing, and monitoring various physiological data, and the types of physiological data involved are constantly increasing, and the detection frequency is also gradually increasing. Based on the needs of the current development trend of big data, various physiological data from many different monitoring instruments need to be collected on a platform that can be used for big data analysis. These different monitoring instruments include basic monitors, anesthesia ventilators, tissue oxygen meters, hemodynamic instruments, brain function testing instruments, etc. However, although the current technical level allows the collection of physiological data from multiple monitoring instruments and stored in the hospital's case files, these data are incomplete, inaccurate, can be manually modified, the data density is thin, and cannot be directly used for big data analysis, etc. defect. The current collection method is mainly to deal with legal disputes and insurance companies to collect money, and this collection method is usually difficult to distinguish different physiological data when faced with the massive physiological data obtained by so many sampling instruments. The lack of effective optimization standards for physiological data and its indicators leads to low data processing efficiency for physiological data but the inability to effectively manage raw data in accordance with optimized physiological indicators. Moreover, although different sampling devices may collect the same or related types of physiological data at the same time, the quality of these data itself is usually difficult to directly screen, and there may be a lack of correlation between them, resulting in the acquisition of higher quality physiological data, and then reliable The analysis results are more difficult.

Therefore, how to more effectively collect, process, display and/or manage physiological data has become an urgent technical problem to be solved in this field.

The present invention provides a physiological data collection and management device, including a physiological data baseline value determination module, an optimization range determination module, a sampling instrument connection module, a physiological data quality analysis module and a physiological data optimization analysis module; wherein, the The physiological data baseline value determination module and the optimization range determination module are connected to the sampling instrument connection module and/or the physiological data optimization analysis module, preferably respectively connected to the sampling instrument connection module and the physiological data optimization analysis module.

According to an embodiment of the present invention, the sampling instrument connection module can be connected to at least two sampling instruments, for example, more than two sampling instruments that are the same as or different from each other. For example, the sampling instrument may be a known physiological data sampling instrument, such as a physiological data detection device, such as a physiological data monitoring device, an example of which may be a monitor. As an example, the sampling instrument can be continuous sampling or discontinuous sampling, for example, selected from single-parameter monitors (blood pressure monitors, blood oxygen saturation monitors, ECG monitors), multi-parameter comprehensive monitors (capable of monitoring cardiac At least two of parameters such as electricity, respiration, body temperature, blood pressure, and blood oxygen), plug-in combined monitors, etc.

According to an embodiment of the present invention, the sampling instrument connection module consists of the sampling instrument Get the raw physiological data of the managed object.

According to the embodiment of the present invention, the physiological data quality analysis module is used for analyzing and screening the quality of the original physiological data output by the sampling instrument connection module, so as to obtain the original physiological data which is retained after screening. For this purpose, the sampling instrument connection module can transmit the raw physiological data of the managed object acquired by the sampling instrument to the physiological data quality analysis module.

Preferably, compared with the original physiological data not retained after screening, the original physiological data retained after screening is of higher quality, which may also be referred to as "higher-quality original physiological data" in the context of this specification. More preferably, the raw physiological data retained after the screening is the data with the best quality among the raw physiological data output by the connection module of the sampling instrument, and may also be referred to as "the raw physiological data with the best quality" in the context of this specification. .

According to the embodiment of the present invention, the collection and management device further includes a management object data module, which is used to obtain and/or transmit the data of the management object.

According to the embodiment of the present invention, the data of the management object include but are not limited to one, two or more selected from the following data: basic data (such as gender, age, marriage and childbearing status, past medical history, genetic disease history, etc.), Concurrent diseases, surgical history, family history, medications, allergy history, living habits (smoking, alcohol, drug use), diagnostic data (laboratory test results, electrocardiogram results, various imaging test results, ultrasonography results, isotope test results ), surgical data (method, operation time, surgical site, postoperative complications), anesthesia data (method, various medications, fluid volume, various blood products, anesthesia-related complications), physiological monitoring data (various hemodynamic parameters, various vital sign parameters, various respiratory parameters, various brain function monitoring parameters, various tissue perfusion oxygenation parameters, body temperature, inspiratory oxygen concentration, end-expiratory carbon dioxide), etc.

According to the embodiment of the present invention, the management object data module can be connected with the hospital information system (HIS) to obtain the required management object data, such as its perioperative data.

According to an embodiment of the present invention, the management object data module may include an input device, so that the management object data can be input manually or non-manually.

According to the embodiment of the present invention, the physiological data baseline value determination module and the optimization range determination module are all connected to the management object data module, and the optimized physiological data indicators required for the data analysis of the management object are used to make the sampling instrument The connection module transmits the required original physiological data according to the optimized physiological data index.

According to an embodiment of the present invention, the physiological data can be the physiological data of the managed object, such as the managed object in the perioperative period (pre-operation, operation and/or post-operative management period), ICU, intensive care, emergency care and other stages. Physiological data, or non-perioperative, non-ICU, non-critical care, non-emergency care, etc., or physiological data not intended for treatment or diagnosis.

Unless otherwise stated, the management objects in the context of this specification are not particularly limited.

According to the embodiment of the present invention, the management object may include but not limited to living human, animal or inanimate human body, animal body, specimen or sample. For example, the management subject can be selected from healthy or unhealthy human or animal bodies, such as subjects of scientific research, such as human or animal bodies with or without health risks, such as surgical patients, such as general surgical patients, such as Relatively young and healthy patients who will undergo intermediate-risk or low-risk surgical procedures. Further, the management object can also be selected from other patients, including patients who will receive higher-risk surgical operations. Alternatively, the management object can also be selected from healthy, unhealthy or possible persons at risk to health.

According to an embodiment of the present invention, the data of the managed object includes basic data, electronic medical record related data, operation data, and/or baseline status. For example, the basic information includes the date of birth, height, weight, gender, etc. of the management object. For example, the relevant data of the electronic medical record includes the disease diagnosis, medical history, chronic disease history, drug history, PONV history, etc. of the management object. For example, the operation information includes the type of operation, anesthesia, ASA level, etc. of the management object. For example, the baseline status includes baseline values, thresholds, outlier definitions, and the like of each physiological index of the managed object.

According to an embodiment of the present invention, the optimized physiological data index is selected from one, two or more of the following: blood pressure, heart rate, hemodynamics, tissue oxygen, respiratory tidal volume, respiratory rate, airway pressure, per hour Minute ventilation, temperature, systolic blood pressure, diastolic blood pressure, mean arterial pressure, intravascular volume, stroke volume, peripheral vascular resistance, cardiac output, cerebral oxygen, body oxygen, carbon dioxide level, pulse oximetry, based on EEG depth of anesthesia, etc.

According to the embodiment of the present invention, the physiological data optimization analysis module is connected with the physiological data quality analysis module to obtain the original physiological data connected and transmitted by the physiological data quality analysis module, such as higher quality original physiological data or the best quality raw physiological data.

According to the embodiment of the present invention, the physiological data optimization analysis module performs analysis according to the original physiological data output by the physiological data quality analysis module and the physiological optimization management index parameters output by the physiological data optimization index determination module, and outputs the physiological optimization management index parameters. data, such as values and/or graphs.

According to the embodiment of the present invention, the data collection and management device may further include one, two or more data storage modules to store the physiological data acquired or transmitted by the modules.

According to the context of the present invention, the connection or transmission may be by wired or wireless means, preferably by direct connection or transmission by wired or wireless means. For example, the wired method includes the method through a serial port or the network port; the wireless method includes the method selected from wifi, bluetooth and other wireless communication protocols. Alternatively, the connection or transmission can also be performed through the cloud, preferably an indirect connection or transmission through the cloud.

The present invention also provides a method for processing physiological data, including using the above-mentioned device for collecting and managing physiological data to process physiological data.

According to the embodiment of the present invention, the processing method can calculate the waveform and value of the physiological indicators for optimal management required for management according to the optimal raw physiological data and index parameter sets of the managed objects.

According to the embodiment of the present invention, the optimal raw physiological data is obtained by performing quality analysis on multiple sets of raw physiological data, and/or the index parameter set is determined by the data of the managed object.

According to an exemplary embodiment of the present invention, the method may include the following steps: S1: Obtain the raw physiological data of the managed object from the sampling instrument, and obtain the information of the managed object through input or through the HIS system; S2: According to the data of the managed object Data, select the physiological index T={T ₀ ,...,T _x } that needs to be optimally managed, and the corresponding index parameter set P={P ₀₀ ,...,P _0y ,...,P _x0 , ..., P _xy }; S3: For any physiological index Ti that requires optimized management, automatically search for the required sampling instrument, obtain the packet from the required sampling instrument, analyze the agreement, and obtain the original physiological data OD={ OD ₀ ,...,OD _m }; S4: Judge the quality of each physiological data Q=Q(OD), select Q as the best sampling instrument S required, and obtain the best original physiological data OD'={OD' ₀ ,...,OD' _n }; S5: According to the best original physiological data OD' and index parameter set P, calculate the waveform and value of the physiological index IT=F(OD', P) for optimal management, Get higher quality physiological data.

According to the embodiment of the present invention, in step S3, the analysis agreement includes a data analysis agreement and/or a standardization agreement customized by the instrument. For example, the standardized protocol can be selected from IHE, HL7 v2, HL7 FHIR, CDA, etc.

According to an embodiment of the present invention, in step S3, the analyzed physiological data can be divided into numerical data and waveform data according to the resolution. For example, the frequency of the numerical data is 1-0.5 Hz. For example, the frequency of the waveform data may be kHz to Hz, where kHz preferably represents 1 kHz and above, and Hz preferably represents 100 Hz and above, for example, it may be 100 Hz or 200 Hz.

According to an embodiment of the present invention, in step S4, the quality of each physiological data can be analyzed or calculated according to factors including factors affecting the integrity of data records and the validity of data records; for example, factors affecting the integrity of data records and the validity of data records The validity factors include factors such as should be collected data, missing data, actually collected data, interference data, outlier data and available data.

For example, define the data point Ndata that should be collected, the actual data point Nrecords, the missing data point Nmiss, the outlier data point Noutlier, the interference data point Nartifacts, and the available data point Naavailable; define the proportion of actual data Precords=Nrecords/Ndata; define The proportion of missing data Pmiss=Nmiss/Ndata, where Precords=1-Pmiss; define the proportion of outlier data Poutlier=Noutlier/Nrecords; define the proportion of interference data Partifacts=Nartifacts/Nrecords; Define the proportion of available data Pavailable=Navailable/Nrecords, where Pavailable=1-Poutlier-Partifacts; among them, the proportion of actual collected data is Precords and the proportion of missing data is Pmiss, indicating the integrity of data records; the proportion of outlier data is Poutlier, The proportion of interference data Partifacts and the proportion of available data Pavailable indicate the validity of data records.

According to an embodiment of the present invention, the quality considerations of each physiological data also include acquisition time and sampling frequency.

For example, define collection time Trecords, sampling frequency Frecords, quality of each physiological data Q=Q(OD), and the quality of physiological data can be expressed by any of the following formulas: Formula 1: Q=Func(Trecords, Frecords, Precords, Pavailable) =m* Trecords+n*Frecords+x* Precords+y* Available

Formula 2: Q’=Func(Trecords, Frecords, Pmiss, Pavailable)=m’* Trecords+n’*Frecords-x’* Pmiss+y’* Pavailable

Formula 3: Q”=Func(Trecords, Frecords, Precords, Poutlier, Partifacts)=m”* Trecords+n”*Frecords+x”* Precords-y”* Poutlier-z”* Partifacts

Formula 4: Q'''=Func(Trecords, Frecords, Pmiss, Poutlier,Partifacts)=m'''* Trecords+n'''*Frecords-x'''* Pmiss-y'''* Poutlier-z '''*Partifacts; among them, m*, m'*, m”*, m'''* represent the weight value of collection time in the calculation of data quality indicators; n*, n'*, n”*, n' ''* represents the sampling frequency when calculating the data quality index Weight value; x*, x”* represent the weight value of the proportion of actual collected data in the calculation of data quality indicators; x'*, x'''* represent the weight value of the proportion of lost data in the calculation of data quality indicators; y*, y'* represent the weight value of the proportion of available data in the calculation of data quality indicators; y"*, y'''* represent the weight value of the proportion of outlier data in the calculation of data quality indicators; z"*, z'''* represents the weight value of the proportion of interference data in the calculation of data quality indicators; Q, Q', Q", Q''' all represent the quality of physiological data. The above weight values can be adjusted based on experience and known knowledge in the field.

According to the embodiment of the present invention, the should-collected data point Ndata, the actually-collected data point Nrecords, the missing data point Nmiss, the collection time Trecords and the sampling frequency Frecords are obtained according to actual statistics. For example, for the HR (heartbeat) parameter, the device records data every 1 second for 30 seconds, and a total of 29 data points are recorded, then Ndata is 30, Nrecords is 29, Nmiss is 1, Trecords is 30 seconds, and Frecords is 1Hz.

According to the embodiment of the present invention, the interference data judges whether there is interference in the data by using the same index or a correlation index.

Specifically, the judging method of the interference data point Nartifact can be selected from the following methods: an abnormal situation occurs in the data, and it is judged as an artifact; At least one of the range, sudden change in value, and deviation of the fitting curve.

According to an embodiment of the present invention, the outlier data is judged by a physiological parameter threshold.

According to embodiments of the present invention, the judgment of the outlier data point Noutlier is judged by using statistical methods in combination with medical professional knowledge, and can be selected from any of the following three methods: Method 1: Set the upper and lower limits of the outlier threshold. If the upper or lower limit of the threshold is exceeded, it is an outlier; the upper and lower limits of the outlier threshold are based on medical professional knowledge or self-judgment, such as body temperature parameters, the lower limit of the outlier threshold 30, the upper limit of the outlier threshold is 45; Method 2: According to the existing outlier statistical judgment methods, such as chanwennt criterion, Mahalanobis distance, box plot, bar graph, linear regression method, normal distribution, fitting method, etc. Judgment; Method 3: Statistical methods combined with medical professional knowledge, such as using the normal distribution in statistical methods to make judgments, wherein the upper and lower limits of the normal distribution can be adjusted according to the medical knowledge of specific physiological parameters.

According to an embodiment of the present invention, the threshold value of the physiological parameter may be derived from the data of the managed subject or obtained through analysis based on the data.

According to an embodiment of the present invention, in step S5, the calculation method includes but is not limited to: AUC; the ratio exceeding the absolute threshold of the upper and lower limits; the ratio of the relative threshold exceeding the upper and lower limits of the relative baseline value; optimized physiological ratio and duration; other Physiological indicators obtained by statistical methods; physiological indicators obtained by machine learning.

According to an embodiment of the present invention, the AUC calculation method includes the following steps: Step 1: Dynamic changes of physiological parameters over time to generate a curve; Step 2: The area enclosed by the curve and the threshold boundary is AUC; Step 3: The user defines the relative threshold Upper limit, lower relative threshold, baseline value, for evaluation AUC at relative threshold; Step 4: User defined upper absolute threshold, lower absolute threshold, used to evaluate AUC at absolute threshold; Step 5: Based on AUC, determine the optimal range of physiological parameters; Step 6: Based on AUC, physiological The optimal range of parameters is determined to determine the optimal physiological time ratio.

According to an embodiment of the present invention, the other statistical method may be selected from univariate physiological parameter time series plot or multivariate physiological parameter correlation plot. Among them, the single-variable physiological parameter timing diagram is obtained through the following method: the dynamic change of physiological parameters over time, and a curve is generated; and then through timing signal analysis, the area with more data entropy is obtained, indicating that the user focuses on it. Wherein, the multi-variable physiological parameter correlation graph includes multiple physiological parameter correlation graphs, such as scatter plots, to provide users with statistical analysis solutions. Preferably, the scatter plot can be presented in the form of a regular scatter plot, and can also be presented in the form of at least two, three, four, five, six, seven, eight, nine or more areas Presentation, preferably in the form of nine regions. Among them, the presentation form of nine regions refers to dividing the display region into nine regions by the upper threshold and lower threshold of different physiological parameters or physiological indicators represented by the x and y axes, forming a nine-region display.

According to an embodiment of the present invention, the machine learning includes at least one of classification, regression, clustering and the like. Preferably, classification prediction or regression prediction is performed through machine learning, such as postoperative recovery quality, PONV (probability of postoperative nausea, vomiting, headache), mortality rate, length of hospital stay, complication rate, etc.; preferably , performing cluster analysis of different management methods or results through machine learning; preferably, extracting features of physiological parameters through machine learning, and using the features for subsequent analysis and judgment. Preferably, the machine learning analysis is not limited to time-series physiological signals, but also includes processed data obtained after pre-processing and analysis of time-series signals. excellent Optionally, machine learning analysis is not limited to time-series physiological signals, but can also combine constant data such as patient data, preoperative evaluation, electronic medical records, and postoperative evaluation. Preferably, machine learning analysis is not limited to time-series physiological signals, but can also be combined with intervention event data, such as drug use, perioperative phase data, intake, output, etc.

Preferably, the physiological data processing method is implemented in the above-mentioned physiological data collection and management device.

According to an embodiment of the present invention, the direct purpose of the processing method is not a live diagnosis result or health status, but only a method aimed at processing physiological data obtained by the managed object as an intermediate result.

The present invention also provides a physiological data display device, including the above-mentioned physiological data collection and management device and a physiological optimization management display module connected thereto.

Preferably, the physiological optimization management display module is used to visualize the physiological optimization management data output by the physiological data optimization analysis module of the physiological data collection and management device.

According to an embodiment of the present invention, the physiological data display device may further include a storage module.

Preferably, the storage module is used to store the physiological data collection and management device's physiological data optimization analysis module output physiological optimization management data.

The present invention also provides a physiological data display method, including using a display module to visualize the physiological optimization management data output by the physiological data optimization analysis module of the physiological data collection and management device.

The display method according to the present invention includes the processing method using the above-mentioned physiological data Finally, the obtained high-quality physiological data is visualized, for example, shown on a display device.

Preferably, the visualization or presentation is a real-time display or a statistical display.

According to the display method of the present invention, the physiological data visualization can be presented in the form of single, two, three, four, five, six, seven, eight, nine or more regions; preferably nine presented in the form of a region.

Among them, the display form of nine regions refers to dividing the display region into nine regions by the upper threshold and lower threshold of different physiological parameters or physiological indicators represented by the x and y axes, forming a nine-region display. For example, the x-axis represents the numerical range of the first physiological parameter or the first physiological index, and the y-axis represents the numerical range of the second physiological parameter or the second physiological index. The closed grid enclosed by the upper and lower thresholds of the index and the second physiological parameter or the upper and lower thresholds of the second physiological index represents the range of physiological optimization management data, which is recorded as the preferred area.

Further, in addition to the above-mentioned preferred areas, according to the physiological parameters or physiological indicators in each area falling above the upper threshold, below the lower threshold, or between the upper threshold and the lower threshold, it can be divided into: low high area, low optimal area, Double low area, excellent high area, excellent low area, double high area, high excellent area, high low area (as shown in Figure 4).

Further, the physiological parameters or physiological indicators in each area may be presented in the corresponding area in the form of scattered points.

The method of displaying divided areas can facilitate the optimal management of the physiological parameters or physiological indicators of the management object. The optimal area can be obtained by limiting the threshold, and the difference between the physiological parameters or physiological indicators in other areas and the optimal area can be directly clarified, and the intervention can be clearly defined. Direction, through subsequent intervention, these physiological parameters or physiological indicators located in other areas can be adjusted to the preferred area.

The present invention also provides a physiological data display system for visualizing physiological data in the form of single, two, three, four, five, six, seven, eight, nine or more regions presented.

According to the display system of the present invention, it includes a display unit, a selection and threshold determination unit of physiological parameters or physiological indicators; the selection and threshold determination unit of the physiological parameters or physiological indicators is connected with the display unit, and each physiological parameter or physiological The status of the scientific indicators is presented by the display unit.

Preferably, the presentation can be presented in a two-dimensional form or a three-dimensional form.

According to the display system of the present invention, the physiological parameter or physiological index selection and threshold determination unit may at least include a first physiological parameter or physiological index selection and threshold determination unit, and a second physiological parameter or physiological index The selection and threshold determination unit. Further, it may also include a unit for selecting a third physiological parameter or a physiological index and determining a threshold.

According to the display system of the present invention, when the physiological parameter or physiological index selection and threshold determination unit only includes the first physiological parameter or physiological index selection and threshold determination unit and the second physiological parameter or physiological index When selecting and determining the threshold unit, the first physiological parameter or physiological index and its (threshold value) range, and the second physiological parameter or physiological index and its (threshold value) range can be in two-dimensional plane form (x-axis and y The plane coordinate system constructed by axes) is presented through the display unit.

According to the display system of the present invention, when the physiological parameter or physiological index selection and threshold value determination unit includes the first physiological parameter or physiological index selection and threshold value determination unit, the second physiological parameter or physiological index selection and Threshold Determination Units, and Tertiary When the selection of physiological parameters or physiological indicators and the threshold determination unit, the first, second and third physiological parameters or physiological indicators and their (threshold) ranges can be constructed in three-dimensional form (x-axis, y-axis and z-axis The three-dimensional coordinate system) is presented through the display unit.

The threshold value described in the present invention can be an absolute threshold value or a relative threshold value, wherein the absolute threshold value is determined according to the physiological parameters, the situation of the managed population, the basic situation of the managed object, the relevant data of the electronic medical record of the managed object, etc., and the relative threshold value is determined according to the baseline of the managed object Value, threshold ratio, basic information of the management object, relevant information of the management object electronic medical record, etc.

The present invention also provides the application of the above physiological data collection and management device and/or processing method in processing physiological data.

The present invention also provides an application of the above physiological data display device and/or display method in displaying physiological data.

The present invention also provides a device for physiological optimization management, including the above-mentioned device for collecting and managing physiological data.

The present invention also provides a method for physiological optimization management, including the above-mentioned physiological data processing method.

According to different application environments and purposes, the above-mentioned technical solution of the present invention may be for diagnosis or treatment purposes or non-diagnosis and non-treatment purposes.

Beneficial effect:

The physiological data processing method, display method and device provided by the present invention obtain the best original physiological data through calculation and comparison according to the management object data, and automatically determine the physiological indicators and parameters for optimal management, which can be obtained from multiple Obtain high-quality physiological data from the massive data of the sampling instrument, and the acquisition of physiological data is accurate, complete, and timely (frequency is more High) performance, can directly process big data.

By calculating and optimizing the values and waveforms of physiological indicators, the most effective physiological data can be selected and presented, thereby reducing the burden on users, effectively reducing workload and management risks, and improving the outcomes of managed objects.

1: Sampling instrument connection module

2: Physiological data baseline value determination module

3: Optimize the range determination module

4: Physiological data quality analysis module

5: Physiological data optimization analysis module

6: Physiological optimization management display module

7: Storage module

8: Management object data module

9: Database

A,B,C: sampling instrument

11: Display unit

12: Parameter X selection and threshold determination unit

13: Parameter Y selection and threshold determination unit

S1 to S6: steps

[ Fig. 1 ] A schematic structural view of the device provided for Example 1.

[ Fig. 2 ] A schematic flowchart of the physiological data processing method provided in Example 2.

[ FIG. 3 ] A schematic diagram of a nine-region display method provided in Embodiment 3. [ FIG.

[Fig. 4] is a display diagram of nine regions.

[ FIG. 5 ] A schematic structural view of the nine-area display system provided for Embodiment 3. [ FIG.

The technical solutions of the present invention will be further described in detail below in conjunction with specific embodiments. It should be understood that the following examples are only for illustrating and explaining the present invention, and should not be construed as limiting the protection scope of the present invention. All technologies realized based on the above contents of the present invention are covered within the scope of protection intended by the present invention.

Unless otherwise stated, the elements used in the following examples are commercially available or can be prepared by known methods.

Example 1

The physiological data display device shown in FIG. 1 includes a physiological data collection and management device and a physiological optimization management display module 6 and a storage module 7 connected thereto.

The physiological data collection and management device includes a physiological data baseline value determination module 2, Optimization range determination module 3, sampling instrument connection module 1, physiological data quality analysis module 4, physiological data optimization analysis module 5 and management object data module 8; among them, physiological data baseline value determination module 2 and optimization range The determination module 3 is connected with the sampling instrument connection module 1 and the physiological data optimization analysis module 5 respectively; the management object data module 8 is used to obtain and/or transmit the data of the management object; the physiological optimization management display module 6 Used to visualize the physiological optimization management data output by the physiological data optimization analysis module 5 of the physiological data collection and management device. The storage module 7 is used for storing physiological data collection and physiological data optimization analysis module 5 outputted by the physiological data optimization management device of the management device.

The sampling instrument connection module 1 is connected to at least two sampling instruments, and the sampling instrument connection module 1 obtains the original physiological data of the managed object from the sampling instrument.

The physiological data quality analysis module 4 is used to analyze and screen the quality of the original physiological data output by the sampling instrument connection module 1, so as to obtain the original physiological data that is retained after screening. The sampling instrument connection module 1 transmits the raw physiological data of the managed object acquired by the sampling instrument to the physiological data quality analysis module 4 . Compared with the original physiological data that is not retained after screening, the original physiological data that is retained after screening is the data with the best quality among the original physiological data output by the sampling instrument connection module 1, that is, "the original physiological data with the best quality" .

The data of management objects include but are not limited to one, two or more selected from the following: basic data (such as gender, age, marriage and childbearing status, past medical history, genetic disease history, etc.), concurrent diseases, surgical history, family history , Medications taken, history of allergies, living habits (smoking, alcohol, drug use), diagnostic data (laboratory test results, electrocardiogram test results, various imaging tests Results, ultrasonography results, isotope examination results), surgical data (method, operation time, surgical site, postoperative complications), anesthesia data (method, various medications, fluid volume, various blood products, anesthesia-related complications), physiological Medical monitoring data (various hemodynamic parameters, various vital sign parameters, various respiratory parameters, various brain function monitoring parameters, various tissue perfusion oxygenation parameters, body temperature, inspiratory oxygen concentration, end-expiratory carbon dioxide), etc.

The management object data module 8 is connected with the hospital information system (HIS) to obtain the required management object data. The management object data module 8 includes an input device, so that the management object data can be input manually or non-manually.

The physiological data baseline value determination module 2 and the optimization range determination module 3 are all connected to the management object data module 8, and the optimized physiological data indicators required for the data analysis according to the management object are used to make the sampling instrument connection module 1 according to the optimized Physiological data indicators, the original physiological data required for transmission.

Physiological data is the physiological data of the management object, such as the physiological data of the management object in the perioperative period (pre-operation, operation and/or post-operation management period), ICU, intensive care, emergency care, etc., or non-perioperative, Physiological data in non-ICU, non-critical care, non-emergency care, etc., or physiological data not intended for treatment or diagnosis. Management objects include but are not limited to living people, animals or inanimate human, animal bodies, specimens or samples. The data of the management object includes basic data, electronic medical record related data, operation data, and/or baseline status.

The optimized physiological data index is selected from one, two or more of the following: blood pressure, heart rate, hemodynamics, tissue oxygen, respiratory tidal volume, respiratory rate, airway pressure, minute ventilation, temperature and the like.

Physiological data optimization analysis module 5 is connected with physiological data quality analysis module 4, with Obtain the best quality raw physiological data transmitted through the connection of the physiological data quality analysis module 4 .

The physiological data optimization analysis module 5 determines the physiological optimization management index parameters output by the module according to the original physiological data output by the physiological data quality analysis module 4 and the physiological data optimization index parameters for analysis, and outputs physiological optimization management data, such as numerical values and /or Atlas.

The data collection and management device further includes one, two or more data storage modules to store the physiological data acquired or transmitted by the modules.

The connection or transmission of each module in the above device is directly connected or transmitted by wired or wireless means. The wired way includes the way through the serial port or the network port; the wireless way includes the way through selected from wifi, bluetooth and other wireless communication protocols. Alternatively, the connection or transmission of each module in the above device is indirectly connected or transmitted through the cloud.

Example 2

The physiological data processing method (as shown in Figure 2) comprises the following steps: S1: Obtain the original physiological data of the managed object from each sampling instrument, and at the same time, obtain the data of the managed object through input or through the HIS system; S2: According to the patient data, select the optimized physiological index T={T ₀ ,...,T _x }, such as {BP, StO ₂ }, etc.; according to the patient’s data, select the parameters of the corresponding optimized physiological index, and the index parameter combination is P= {P ₀₀ ,...,P _0y ,...,P _x0 ,...,P _xy }, eg {BP is below the relative lower limit of the immediate value, StO ₂ is below the absolute lower threshold of the AUC value, StO ₂ is higher than the real-time value of the upper limit of the absolute threshold}; S3: According to the required optimized physiological index T={T ₀ ,...,T _x }, search for supported sampling equipment; through the specific interface of the sampling equipment , to obtain the monitoring packet; through the data agreement of the sampling equipment, the original packet is analyzed to obtain real-time physiological data OD={OD ₀ ,...,OD _m }, and the data resolution is determined by the packet sending frequency. For example {StO ₂ _N1: {76%}, MBP_M1: {123^100^...^30mmHg, 80mmHg}}; S4: instant physiological data OD={OD ₀ ,...,OD _m }, for Corresponding to the same physiological index, such as StO ₂ _N1 and StO ₂ _C1, judge its data quality; retain the index with the best quality of physiological data, such as StO ₂ _N1, and obtain the best original physiological data OD'={OD' ₀ ,. .., _OD'n }.

The quality of each physiological data is judged according to the data that should be collected, missing data, actual data, interference data, outlier data, available data, collection time and sampling frequency. Define the data point Ndata to be collected, the actual data point Nrecords, the missing data point Nmiss, the outlier data point Noutlier, the interference data point Nartifacts, the available data point Navailable, the collection time Trecords, the sampling frequency Frecords; define the proportion of the actual collection data Precords =Nrecords/Ndata; define the proportion of missing data Pmiss=Nmiss/Ndata, where Precords=1-Pmiss; define the proportion of outlier data Poutlier=Noutlier/Nrecords; define the proportion of interference data Partifacts=Nartifacts/Nrecords; define the available data The proportion of Pavailable=Navailable/Nrecords, where Pavailable=1-Poutlier-Partifacts; among them, the proportion of actual collected data is Precords and the proportion of missing data is Pmiss, indicating the integrity of data records; the proportion of outlier data is Poutlier, and the proportion of interference data is Pmiss. The ratio of Partifacts and available data to Pavailable indicates the validity of data records.

The quality of each physiological data Q=Q(OD), and the quality of physiological data is expressed by any of the following formulas: Formula 1: Q=Func(Trecords, Frecords, Precords, Pavailable)=m* Trecords+n*Frecords+x* Precords+y* Pavailable

Formula 2: Q’=Func(Trecords, Frecords, Pmiss, Pavailable)=m’* Trecords+n’*Frecords-x’* Pmiss+y’* Pavailable

Formula 3: Q”=Func(Trecords, Frecords, Precords, Poutlier, Partifacts)=m”* Trecords+n”*Frecords+x”* Precords-y”* Poutlier-z”* Partifacts

Formula 4: Q'''=Func(Trecords, Frecords, Pmiss, Poutlier,Partifacts)=m'''* Trecords+n'''*Frecords-x'''* Pmiss-y'''* Poutlier-z '''* Partifacts; Among them, m*, m'*, m"*, m'''* represent the weight value of the acquisition time in the calculation of data quality indicators; n*, n'*, n"*, n'''* represent the sampling frequency The weight value in the calculation of the data quality index; x*, x”* represent the weight value of the proportion of the actual collected data in the calculation of the data quality index; x’*, x’’’* represent the proportion of missing data in the data quality The weight value when calculating the index; y*, y'* represent the weight value of the available data proportion in the calculation of the data quality index; y”*, y'''* represent the proportion of outlier data in the calculation of the data quality index Weight value; z"*, z'''* represent the weight value of the proportion of interference data in the calculation of data quality indicators; Q, Q', Q", Q''' all represent the quality of physiological data. The above weight values can be adjusted based on experience and known knowledge in the field.

The data point Ndata to be collected, the actual data point Nrecords, the missing data point Nmiss, the collection time Trecords, and the sampling frequency Frecords are obtained according to the actual statistics. For example, for HR (heartbeat) parameters, the device records data every 1 second for 30 seconds, a total of If 29 data points are obtained, then Ndata is 30, Nrecords is 29, Nmiss is 1, Trecords is 30 seconds, and Frecords is 1Hz.

Interference data judges whether there is interference in the data through the same index or correlation index. Specifically, the judging method of the interference data point Nartifact is selected from the following methods: an abnormal situation occurs in the data, and it is judged as an artifact; the abnormal situation includes exceeding the upper or lower limit of the defined threshold (artifact threshold), exceeding the range limited by the statistical method, and a sudden change in the value At least one of the situations of , fitting curve offset and the like.

Outlier data were judged by physiological parameter thresholds. Specifically, the judgment of the outlier data point Noutlier is judged by statistical methods combined with medical professional knowledge, and is selected from any of the following three methods: Method 1: setting the upper and lower limits of the outlier threshold, exceeding the upper or lower limit of the threshold, that is The upper and lower limits of the outlier threshold are based on medical professional knowledge or self-judgment, such as body temperature parameters, the lower limit of the outlier threshold is 30, and the upper limit of the outlier threshold is 45; Group statistical judgment methods, such as chanwennt criterion, Mahalanobis distance, box plot, bar graph, linear regression method, normal distribution, fitting method, etc.; method three: statistical methods combined with medical professional knowledge, such as using statistical methods The normal distribution is judged, and the upper and lower limits of the normal distribution can be adjusted according to the medical knowledge of specific physiological parameters.

S5: According to the best original physiological data OD'={OD' ₀ ,...,OD' _n }, such as {StO ₂ _N1, MBP_M1}, and the index parameter set P={P ₀₀ ,...,P _{0y ,} ..., P _x0, ..., P _xy }, get the best raw physiological data index (OD', P): {OD' ₀ : {P ₀₀ ... P _0y }, ... OD' _n : {P _n0 ... P _ny }}, such as {StO ₂ _N1: {76%, 3020%*min}, MBP_M1: {123^100^...^30mmHg, 80mmHg, 923mmHg*min}}; S6: According to the best raw physiological data index (OD',P): {OD' ₀ : {P ₀₀ ... P _0y }, ... OD' _n : {P _n0 ... P _ny }}, process Obtain the waveform, value or index of the optimized physiological index I _T =F(OD',P) required for anesthesia management, and obtain high-quality physiological data.

The AUC calculation method includes the following steps: Step 1: Dynamic changes of physiological parameters over time to generate a curve; Step 2: The area enclosed by the curve and the threshold boundary is AUC; Step 3: The user defines the upper limit of the relative threshold, the lower limit of the relative threshold, and the baseline value, used to evaluate the AUC under the relative threshold; step 4: the user defines the upper absolute threshold, and the lower absolute threshold, used to evaluate the AUC under the absolute threshold; step 5: according to the AUC, determine the optimal range of the physiological parameters; step 6: According to AUC, the optimal range of physiological parameters, the optimal physiological time ratio is determined.

Example 3

The display system as shown in Figure 5 comprises a display unit 11, a parameter X selection and a threshold determination unit 12, a parameter Y selection and a threshold determination unit 13; a parameter X selection and a threshold determination unit 12, a parameter Y selection and a threshold determination unit 13 respectively It is connected with the display unit 11, and the condition of each physiological parameter or physiological index is displayed in a two-dimensional form by the display unit 11.

The above-mentioned display system can display the physiological data in the form of nine regions (as shown in FIG. 4 ). visual presentation. The nine-region refers to the upper threshold and the lower threshold of different physiological parameters or physiological indicators represented by the x and y axes divide the display area into nine regions to form a nine-region display.

For example, the x-axis represents MAP, and the y-axis represents StO ₂ . After setting the upper and lower thresholds of these two physiological indicators, the closed grid enclosed by the upper and lower thresholds of MAP and the upper and lower thresholds of StO ₂ represents physiological The range of scientifically optimized management data is recorded as the preferred area (as shown in Figure 3). In addition to the preferred area, according to the physiological parameters or physiological indicators in each area fall above the upper limit of the threshold, below the lower limit of the threshold, or between the upper limit of the threshold and the lower limit of the threshold, it can be divided into: low high area, low excellent area, double low area, Excellent high area, excellent low area, double high area, high excellent area, and high low area (as shown in Figure 4).

The embodiments of the present invention have been described above. However, the present invention is not limited to the above-mentioned embodiments. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

1: Sampling instrument connection module

2: Physiological data baseline value determination module

3: Optimize the range determination module

4: Physiological data quality analysis module

5: Physiological data optimization analysis module

6: Physiological optimization management display module

7: Storage module

8: Management object data module

9: Database

Claims (14)

A physiological data collection and management device, the device includes a physiological data baseline value determination module, an optimization range determination module, a sampling instrument connection module, a physiological data quality analysis module and a physiological data optimization analysis module ; Wherein, the physiological data baseline value determination module and the optimization range determination module are connected to the sampling instrument connection module and/or the physiological data optimization analysis module, and the sampling instrument connection module and the physiological data optimization analysis The modules are connected separately; wherein, the device performs the following steps: according to the best raw physiological data and index parameter set of the managed object, calculates the waveform and value of the physiological indexes that need to be optimized for management; wherein, the best raw physiological data It is obtained by performing quality analysis on multiple sets of raw physiological data, and/or the index parameter set is determined by the data of the managed object; wherein, the device also performs the following steps: S1: Acquire the raw physiological data of the managed object from the sampling instrument , and obtain the data of the management object through input or through the HIS system; S2: According to the data of the management object, select the physiological index T={T0,...,Tx} for optimized management, and the corresponding index parameter set P ={P00,...,P0y,...,Px0,...,Pxy}; S3: For any physiological index Ti that needs to be optimally managed, automatically search for the required sampling instrument, from the required sampling instrument Obtain the package, analyze the agreement, and obtain the original physiological data OD={OD0,...,ODm}; S4: Determine the quality of each physiological data Q=Q(OD), select Q as the best required sampling instrument S, and obtain the best original physiological data OD'={OD'0,...,OD'n}; S5: According to the optimal original physiological data OD' and the index parameter set P, calculate the waveform and value of the physiological index IT=F(OD', P) that needs to be optimized for management, and obtain higher-quality physiological data; wherein, the step In S4, the quality of each physiological data is analyzed or calculated according to factors including factors affecting the completeness and validity of the data record. The device according to claim 1, wherein the sampling instrument connection module is connected to at least two sampling instruments, and more than two sampling instruments are the same as or different from each other; wherein, the sampling instrument connection module is managed by the sampling instrument The original physiological data of the subject; wherein, the physiological data quality analysis module is used to analyze and screen the quality of the original physiological data output by the sampling instrument connection module, so as to obtain the original physiological data that is retained after screening; wherein, the The sampling instrument connection module can transmit the original physiological data of the managed objects obtained by these sampling instruments to the physiological data quality analysis module; wherein, compared with the original physiological data that is not retained after screening, the said screening is retained The quality of the original physiological data is relatively high; and wherein, the original physiological data retained after the screening is the data with the best quality among the original physiological data output by the connection module of the sampling instrument. The device according to claim 1, wherein the collection and management device also includes a management object data module, which is used to obtain and/or transmit the data of the management object; Among them, the information of the management object includes but is not limited to one, two or more selected from the following information: basic information (sex, age, marriage and childbearing status, past medical history, genetic disease history), concurrent diseases, surgical history, family history, etc. History, medication, allergy history, living habits (smoking, drinking, drug use), diagnostic data (laboratory test results, electrocardiogram test results, various imaging test results, ultrasonography results, isotope test results), surgical data (method, Operation time, surgical site, postoperative complications), anesthesia data (method, various medications, fluid volume, various blood products, anesthesia-related complications), physiological monitoring data (various hemodynamic parameters, various vital sign parameters, Various respiratory parameters, various brain function monitoring parameters, various tissue perfusion oxygenation parameters, body temperature, inhaled oxygen concentration, end-expiratory carbon dioxide); wherein, the management object data module is connected with the hospital information system (HIS) to obtain the required The management object data is the data of the perioperative period; and wherein, the management object data module includes an input device for inputting the management object data manually or non-manually. The device according to claim 1, wherein the physiological data baseline value determination module and the optimization range determination module are both connected to the management object data module, and analyze the required optimized physiological data according to the management object data index, so that the sampling instrument connection module transmits the required original physiological data according to the optimized physiological data index; wherein, the physiological data is the physiological data of the management object, and the management object is in the perioperative period (before operation, during operation and Physiological data during the management period after surgery), ICU, intensive care, emergency care, or non-perioperative, non-ICU, non-critical care, non-emergency care, or physiological data not intended for treatment or diagnosis data; Among them, the management object includes but not limited to living people, animals or inanimate human body, animal body, specimen or sample; wherein, the data of the management object includes basic data, electronic medical record related data, surgical data, and/or Baseline state; wherein, the optimized physiological data index is selected from one, two or more of the following: blood pressure, heart rate, hemodynamics, tissue oxygen, respiratory tidal volume, respiratory rate, airway pressure, minute ventilation Quantity, temperature; wherein, the physiological data optimization analysis module is connected with the physiological data quality analysis module to obtain the original physiological data connected and transmitted by the physiological data quality analysis module, the original physiological data with higher quality or the best quality The original physiological data; wherein, the physiological data optimization analysis module analyzes according to the original physiological data output by the physiological data quality analysis module and the physiological optimization management index parameters output by the physiological data optimization index determination module, and outputs the physiological optimization Managed data, values and/or graphs; wherein, the data collection and management device further includes one, two or more data storage modules to store the physiological data acquired or transmitted by the data storage modules; wherein, The connection or transmission may be performed in a wired or wireless manner; or, the connection or transmission may be performed through the cloud, and the indirect connection or transmission may be performed through the cloud. A method for processing physiological data, the processing method comprising using the device for collecting and managing physiological data described in any one of Claims 1 to 4 to process the physiological data. The processing method as described in claim item 5, wherein, in step S3, the analysis agreement includes a data analysis agreement and/or a standardization agreement customized by the instrument; Wherein, in step S3, the analyzed physiological data is divided into numerical data and waveform data according to the resolution; wherein, in step S4, the factors affecting the integrity of the data record and the validity of the data record include the data to be collected, the missing Data, actual data, interference data, outlier data, and available data factors; wherein, the interference data judges whether there is interference in the data through the same index or a correlation index; wherein, the outlier data is judged by the physiological parameter threshold; among them, In step S5, the calculation method includes but is not limited to: AUC; the proportion exceeding the upper and lower absolute thresholds; the proportion exceeding the relative baseline value of the upper and lower limits relative threshold; optimized physiological proportion and duration; physiological indicators obtained by other statistical methods ; and physiological indicators obtained by machine learning. A physiological data display device, the display device includes the physiological data collection and management device described in any one of claim items 1 to 4 and a physiological optimization management display module connected thereto; wherein, the physiological optimization management display module It is used to visualize the physiological optimization management data output by the physiological data optimization analysis module of the physiological data collection and management device. The physiological data display device according to claim 7, wherein the physiological data display device further includes a storage module; and Wherein, the storage module is used to store the data of physiological optimization management output by the physiological data optimization analysis module of the physiological data collection and management device. A method for displaying physiological data, the display method comprising using a display module to visualize the physiological optimization management data output by the physiological data optimization analysis module of the physiological data collection and management device described in any one of the request items 1 to 4 wherein, after using the physiological data processing method described in Claim 5, visualizing the high-quality physiological data obtained. The display method of physiological data as claimed in item 9, wherein the visualization is shown on a display device. The display method of physiological data as claimed in item 9, wherein the visualization is real-time display or statistical display. A system for collection and management of physiological data, comprising the collection and management device of physiological data as described in any one of claims 1 to 4 and/or the processing method described in claim 5, the system for collection and management of physiological data is used to process and/or display physiological data. A device for physiological optimization management, including the physiological data collection and management device described in any one of claim items 1 to 4. A method for physiological optimization management, including the physiological data processing method described in Claim 5.

Images