TW202219982A - Device and method 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 and method

This application claims the priority of the prior application with the patent application number of 202011062612.5 and the invention titled "The Collection, Management, Display Device and Method of Physiological Data" submitted by the applicant to the State Intellectual Property Office of China on September 30, 2020. The entire contents of said prior applications are incorporated herein by reference.

The invention belongs to the field of data processing and analysis, and particularly relates to a collection, management and display device and method of physiological data.

With the development of medical detection and communication technology, there are many kinds of instruments used to sample, analyze, and monitor various physiological data, the types of physiological data involved are increasing, and the detection frequency is also gradually increasing. Based on the needs of the current big data development trend, a variety of physiological data from a variety of different monitoring instruments need to be collected on a platform for big data analysis. These different monitoring instruments include basic monitors, anesthesia ventilators, tissue oxygen meters, hemodynamic instruments, and brain function detectors. However, although the current state of the art allows the collection of physiological data from multiple monitoring instruments and stores them in the hospital's case files, these data are incomplete, inaccurate, capable of being artificially 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 the collection of insurance companies, and when faced with the massive amount of physiological data obtained by so many sampling instruments, this collection method is usually difficult to distinguish the quality of different physiological data, and there is a lack of accurate physiological data. And the effective optimization standard of its indicators leads to low efficiency of data processing of physiological data, but it is impossible to effectively manage the original data according to the 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 with each other, resulting in the acquisition of higher-quality physiological data, which in turn is reliable. analysis results are more difficult.

Therefore, how to collect, process, display and/or manage physiological data more effectively has become an extremely unsolved technical problem in the art.

The invention provides a physiological data collection and management device, comprising 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, preferably, the sampling instrument connection module and the physiological data optimization analysis module are respectively connected.

According to an embodiment of the present invention, the sampling instrument connection module can connect at least two sampling instruments, for example, two or more sampling instruments that are the same 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 non-continuous sampling, for example, selected from single-parameter monitors (blood pressure monitor, blood oxygen saturation monitor, ECG monitor), multi-parameter integrated monitor (which can monitor heart rate at the same time) (at least two of parameters such as electricity, respiration, body temperature, blood pressure and blood oxygen), plug-in combination monitors, etc.

According to an embodiment of the present invention, the sampling instrument connection module acquires the raw physiological data of the management object from the sampling instrument.

According to an embodiment of the present invention, 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 to be retained after screening. For this purpose, the sampling instrument connection module can transmit the raw physiological data of the management object acquired by the sampling instrument to the physiological data quality analysis module.

Preferably, the quality of the original physiological data retained after the screening is higher than that of the original physiological data not retained after the screening, which may also be referred to as "higher-quality original physiological data" in the context of this specification. More preferably, 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, which may also be referred to as "the original physiological data with the best quality" in the context of this specification. .

According to an embodiment of the present invention, the collection and management apparatus further includes a management object data module, which is used for acquiring and/or transmitting the data of the management object.

According to an 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, marital status, past medical history, genetic medical history, etc.), Complications, surgical history, family history, medication, allergy history, living habits (tobacco, alcohol, drug use), diagnostic data (laboratory test results, electrocardiogram test results, various imaging test results, ultrasound test results, isotope test results ), surgical data (method, operating time, surgical site, postoperative complications), anesthesia data (method, various medications, fluid volume, various blood products, complications related to anesthesia), physiological monitoring data (various hemodynamics) parameters, various vital sign parameters, various respiratory parameters, various brain function monitoring parameters, various tissue perfusion oxygenation parameters, body temperature, inhaled oxygen concentration, carbon dioxide at the end of exhalation), etc.

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

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 an embodiment of the present invention, 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 indicators according to the management object data, so that the sampling instrument The connection module transmits the required raw physiological data according to the optimized physiological data indicators.

According to an embodiment of the present invention, the physiological data may be the physiological data of the management object, for example, the management object's perioperative period (pre-operative, intra-operative and/or post-operative management period), ICU, intensive care, emergency care and other stages. Physiological data, or physiological data in non-perioperative, non-ICU, non-critical, non-emergency care stages, or in stages not intended for treatment or diagnosis.

Unless otherwise specified, the management object in the context of this specification is not particularly limited.

According to an embodiment of the present invention, the management object may include, but is not limited to, a living human, an animal or an inanimate human, 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 selected from surgical patients, such as general surgery patients, for example Relatively young and healthy patients who will undergo intermediate or low risk surgery. Further, the management object can also be selected from other patients, including patients who will undergo higher-risk surgical operations, and the like. Alternatively, the management subject may also be selected from healthy, unhealthy, or persons who may have health risks.

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

According to an embodiment of the present invention, the optimized physiological data indicator 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, 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 oxygen saturation, based on EEG depth of anesthesia, etc.

According to an 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 transmitted by the connection and transmission of the physiological data quality analysis module, such as higher quality original physiological data or best quality original physiological data raw physiological data.

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

According to an 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 direct connection or transmission by wired or wireless means. For example, the wired way includes the way through the serial port or the network port; the wireless way includes the way through wifi, bluetooth and other wireless communication protocols. Alternatively, the connection or transmission can also be performed through the cloud, preferably the indirect connection or transmission is performed through the cloud.

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

According to the embodiment of the present invention, the processing method can calculate the waveform and value of the physiological index for optimal management required for management according to the best raw physiological data and index parameter set of the management object.

According to an 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 set of index parameters is determined by the data of the management 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 management object from the sampling instrument, and obtain the information of the management object through input or through the HIS system; S2: According to the management object's data data, select the physiological index T={T ₀ , ..., T _x } 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 needs to be optimally managed, automatically search for the required sampling instrument, obtain packets from the required sampling instrument, parse the agreement, and obtain the original physiological data OD={ OD ₀ , ..., OD _m }; 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 best original physiological data OD' and the index parameter set P, calculate the waveform and value of the physiological index IT = F(OD', P) required for optimal management, Obtain higher quality physiological data.

According to an embodiment of the present invention, in step S3, the parsing protocol includes a data parsing protocol and/or a standardized protocol customized by the instrument. For example, the standardized protocol may be selected from IHE, HL7 v2, HL7 FHIR, CDA, and the like.

According to the embodiment of the present invention, in step S3, the parsed physiological data can be divided into numerical data and waveform data according to the resolution. For example, the frequency of this numerical data is 1-0.5 Hz. For example, the frequency of the waveform data can be kHz to Hz, kHz preferably represents 1 kHz and above, and Hz preferably represents 100 Hz and above, for example, it can 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 obtained by analysis or calculation according to factors including the influence of the integrity of the data record and the validity of the data record; The factors of validity include the data that should be collected, the missing data, the actual data, the interference data, the outlier data and the available data.

For example, define the data points Ndata that should be collected, the data points Nrecords actually collected, the missing data points Nmiss, the outlier data points Noutlier, the interference data points Nartifacts, and the available data points Navailable; Define the proportion of actual data collected Precords = Nrecords / Ndata; Define the proportion of missing data as 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 is Partifacts, and the proportion of available data is Pavailable, indicating the validity of data records.

According to embodiments of the present invention, considerations for the quality of the respective physiological data also include acquisition time and sampling frequency.

For example, define the acquisition time Trecords, the sampling frequency Frecords, the quality of each physiological data Q = Q(OD), the physiological data quality can be 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 acquisition time in the calculation of data quality index; n*, n'*, n''*, n'''* represent Weight value of sampling frequency in the calculation of data quality index; x*, x''* represent the weight value of the proportion of actual data collected in the calculation of data quality index; x'*, x'''* represent the proportion of missing data The weight value in the calculation of the data quality index; y*, y'* represent the weight value of the proportion of available data in the calculation of the data quality index; y''*, y'''* represent the proportion of outlier data in the data quality The weight value of the index calculation; z''*, z'''* represent the weight value of the interference data ratio in the calculation of the data quality index; Q, Q', Q'', Q''' all represent the quality of the physiological data . The above weight values can be adjusted based on experience and known knowledge in the art.

According to the embodiment of the present invention, the data points Ndata to be collected, the data points Nrecords actually collected, the missing data points Nmiss, the collection time Trecords and the sampling frequency Frecords are obtained according to actual statistical conditions. For example, for 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 is judged whether there is interference in the data through the same index or a correlation index.

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

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

According to an embodiment of the present invention, the judgment of the outlier data point Noutlier, using statistical methods combined with medical professional knowledge to judge, can be selected from any one 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 outlier is exceeded, it is an outlier; the upper and lower limits of the outlier threshold are stipulated according to medical professional common sense or self-judgment, such as human body temperature parameters, and the lower limit of the outlier threshold. is 30, and the upper limit of the outlier threshold is 45; Method 2: Judging 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.; Method 3: Statistical methods are combined with medical professional knowledge. For example, the normal distribution in the statistical method is used for judgment. 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 can be derived from the data of the management subject or obtained by 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 proportion that exceeds the upper and lower absolute thresholds; The proportion of the upper and lower relative thresholds that exceed the relative baseline value; Optimized physiological ratio and duration; Physiological indicators obtained by other statistical methods; Physiological metrics 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 with time to generate curves; Step 2: The area enclosed by the curve and the threshold boundary is AUC; Step 3: The user defines the upper relative threshold, the lower relative threshold, and the baseline value for evaluating the AUC under the relative threshold; Step 4: The user defines the upper limit of the absolute threshold and the lower limit of the absolute threshold, which are used to evaluate the AUC under the absolute threshold; Step 5: According to AUC, determine the optimal range of physiological parameters; Step 6: Determine the optimal physiological time ratio according to the AUC and the optimal range of the physiological parameters.

According to embodiments of the present invention, the other statistical method may be selected from univariate physiological parameter time series plots or multivariate physiological parameter correlation plots. Among them, the time sequence diagram of the univariate physiological parameters is obtained through the following methods: the dynamic changes of the physiological parameters over time, generating a curve; and then through the analysis of the time sequence signal, the area with more data entropy is obtained, indicating that the user focuses on it. Wherein, the multivariate physiological parameter correlation mapping includes correlation mapping of a plurality of physiological parameters, such as a scatter plot, to provide a statistical analysis scheme for the user. Preferably, the scatter plot can be presented in the form of a regular scatter plot, and can also be in the form of at least two, three, four, five, six, seven, eight, nine or more regions Presented, preferably in the form of nine regions. The nine-area presentation form means that the upper and lower thresholds of different physiological parameters or physiological indicators represented by the x and y axes divide the display area into nine areas to form a nine-area 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, and headache), mortality rate, length of stay in hospital, incidence of complications, etc.; preferably , and perform cluster analysis of different management methods or results through machine learning; preferably, through machine learning, the characteristics of physiological parameters are extracted, and the characteristics are used 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 preprocessing analysis of time-series signals. Preferably, the machine learning analysis is not limited to time-series physiological signals, but can also be combined with constant data such as patient data, preoperative assessment, electronic medical record, and postoperative assessment. Preferably, the machine learning analysis is not limited to time series physiological signals, but can also be combined with intervention event data, such as medication status, perioperative stage data, intake, and output.

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 immediate purpose of the processing method is not a live diagnosis or a health condition, but only a method aimed at processing physiological data acquired by a managed subject as an intermediate result.

The present invention also provides a physiological data display device, comprising 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 for visualizing the physiological optimization management data output by the physiological data optimization analysis module of the collection and management device of the physiological data.

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 for storing the physiological data optimally managed data output by the physiological data optimization analysis module of the physiological data collection and management device.

The present invention also provides a method for displaying physiological data, comprising using a display module to visualize the physiological optimization management data output by the physiological data optimization analysis module of the management device for collecting and managing the physiological data.

The display method according to the present invention includes using the above-mentioned processing method for physiological data, and then visualizing the obtained high-quality physiological data, for example, displaying it on a display device.

Preferably, the visualization or display is an instant display or a statistical display.

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

The nine-area presentation form means that the upper and lower thresholds of different physiological parameters or physiological indicators represented by the x and y axes divide the display area into nine areas to form a nine-area display. For example, the x-axis represents the numerical range of the first physiological parameter or the first physiological index, the y-axis represents the numerical range of the second physiological parameter or the second physiological index, and the first physiological parameter or the first physiological 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 optimal physiological management data, and is recorded as a preferred area.

Further, in addition to the above-mentioned preferred area, according to the physiological parameters or physiological indicators in each area fall above the upper threshold of the threshold, below the lower threshold, or between the upper threshold and the lower threshold, it is divided into: low-high area, low-excellent 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 indexes in each region may be presented in the corresponding region in the form of scattered points.

The display method of dividing the area can facilitate the optimal management of the physiological parameters or physiological indicators of the management object. By defining the threshold to obtain the preferred area, the difference between the physiological parameters or physiological indicators in other areas and the preferred area can be directly identified, and the intervention can be clearly defined. Orientation, 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 display system for physiological data for visualizing the physiological data in the form of single, two, three, four, five, six, seven, eight, nine or more regions render.

The display system according to the present invention includes a display unit, a selection and threshold determination unit for physiological parameters or physiological indicators; The selection and threshold determination unit of the physiological parameter or physiological index is connected with the display unit, and the condition of each physiological parameter or physiological index is presented by the display unit.

Preferably, the presentation may be presented in a two-dimensional form or in 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 selection and threshold determination unit. Further, it may also include a third physiological parameter or physiological index selection and threshold determination unit.

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

According to the display system of the present invention, when the physiological parameter or physiological index selection and threshold determination unit contains the first physiological parameter or physiological index selection and threshold determination unit, the second physiological parameter or physiological index selection and threshold determination unit, as well as the selection of the third physiological parameter or physiological index and the threshold determination unit, the first, second, third physiological parameters or physiological indices and their (threshold) ranges can be in a three-dimensional form ( The stereo coordinate system constructed by the x-axis, y-axis and z-axis) is presented by the display unit.

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

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

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

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

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

According to different application environments and purposes, the above-mentioned technical solutions of the present invention may be diagnostic or therapeutic purposes or non-diagnostic and non-therapeutic purposes.

Beneficial effects:

The processing method, display method and device for physiological data provided by the present invention can obtain the best original physiological data through calculation and comparison according to the management object data, and automatically determine the physiological indicators and parameters required for optimal management. High-quality physiological data is obtained from the massive data of sampling instruments. The acquisition of physiological data is accurate, complete, timely (higher frequency), and can be directly processed by 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.

The technical solutions of the present invention will be described in further detail below with reference to 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 implemented based on the above content of the present invention are covered within the intended protection scope of the present invention.

Unless otherwise stated, 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, 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, an optimization range determination module 3, a sampling instrument connection module 1, a physiological data quality analysis module 4, a physiological data optimization analysis module 5, and a management object data module. group 8;

Wherein, the physiological data baseline value determination module 2 and the optimization range determination module 3 are respectively connected with the sampling instrument connection module 1 and the physiological data optimization analysis module 5;

Management object data module 8, which is used to obtain and/or transmit the data of management objects;

The physiological optimization management display module 6 is used to visualize the physiological optimization management data output by the physiological data optimization analysis module 5 of the collection and management device for physiological data. The storage module 7 is used for storing the physiological data optimal management data output by the physiological data optimization analysis module 5 of the physiological data collection and 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 management object from the sampling instruments.

The physiological data quality analysis module 4 is used for analyzing and screening the quality of the original physiological data output by the sampling instrument connection module 1, so as to obtain the original physiological data to be retained after screening. The sampling instrument connection module 1 transmits the raw physiological data of the management 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 retained after screening is the data with the best quality among the original physiological data output by the connection module 1 of the sampling instrument, that is, "the original physiological data with the best quality" .

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, marital status, past medical history, genetic medical history, etc.), concurrent diseases, surgical history, family history , Taking drugs, allergy history, living habits (tobacco, alcohol, drug use), diagnostic data (laboratory test results, electrocardiogram test results, various imaging test results, ultrasound test results, isotope test results), surgical data (method, surgery 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), etc.

The management object data module 8 is connected with the hospital data 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 both connected to the management object data module 8, and analyze the required optimized physiological data indicators according to the management object data, so that the sampling instrument connection module 1 is optimized according to the Physiological data indicators that transmit the required raw physiological data.

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

The optimized physiological data indicators are 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.

The physiological data optimization analysis module 5 is connected with the physiological data quality analysis module 4 to obtain the original physiological data of the best quality transmitted by the connection and transmission 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 and the physiological data optimization index output by the physiological data quality analysis module 4 and analyzes, and outputs the physiological optimization management data, such as numerical value and / or map.

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 through wired or wireless means. The wired method includes the method through the 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 of each module in the above device is indirectly connected or transmitted by means of the cloud.

Example 2

The physiological data processing method (as shown in Figure 2) includes the following steps: S1: Obtain the original physiological data of the management object from each sampling instrument, and at the same time, obtain the information of the management 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 data, select the parameters corresponding to the optimized physiological index, and the index parameter combination is P = {P ₀₀ , ..., P _0y , ..., P _x0 , ..., P _xy }, eg {BP immediate value below the lower relative threshold, StO ₂ AUC value below the absolute lower threshold, StO ₂ The immediate value higher than the upper limit of the absolute threshold}; S3: According to the required optimized physiological index T={T ₀ , ..., T _x }, search for the supported sampling equipment; through the specific interface of the sampling equipment , obtain the monitoring packet; analyze the original packet through the data agreement of the sampling equipment, and obtain the real-time physiological data OD={OD ₀ , ..., OD _m }, and the data resolution is determined by the transmission frequency of the packet. For example, {StO ₂ _N1:{76%}, MBP_M1:{123^100^…^30mmHg,80mmHg}}; S4: In real-time physiological data OD={OD ₀ , ..., OD _m }, for the corresponding same Use a physiological index, such as StO ₂ _N1 and StO ₂ _C1, to judge its data quality; keep the index with the best quality of physiological data, such as StO ₂ _N1, to obtain the best original physiological data OD'={OD' ₀ , ... , OD' _n }.

The quality of each physiological data is judged according to the data to be collected, missing data, actual collected data, interference data, outlier data, available data, collection time and sampling frequency. Define the data points Ndata to be collected, the actual data points Nrecords, the missing data points Nmiss, the outlier data points Noutlier, the interference data points Nartifacts, the available data points Navailable, the collection time Trecords, and the sampling frequency Frecords; Define the proportion of actual data collected Precords = Nrecords / Ndata; Define the proportion of missing data as 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 is Partifacts, and the proportion of available data is Pavailable, indicating the validity of data records.

Each physiological data quality Q = Q(OD), and the physiological data quality 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 acquisition time in the calculation of data quality index; n*, n'*, n''*, n'''* represent Weight value of sampling frequency in the calculation of data quality index; x*, x''* represent the weight value of the proportion of actual data collected in the calculation of data quality index; x'*, x'''* represent the proportion of missing data The weight value in the calculation of the data quality index; y*, y'* represent the weight value of the proportion of available data in the calculation of the data quality index; y''*, y'''* represent the proportion of outlier data in the data quality The weight value of the index calculation; z''*, z'''* represent the weight value of the interference data ratio in the calculation of the data quality index; Q, Q', Q'', Q''' all represent the quality of the physiological data . The above weight values can be adjusted based on experience and known knowledge in the art.

The data points Ndata should be collected, the actual data points Nrecords are collected, the missing data points Nmiss, the collection time Trecords, and the sampling frequency Frecords are obtained according to the actual statistical situation. For example, for 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.

Interference data judge whether the data has interference through the same indicators or related indicators. Specifically, the method for judging the Nartifact of the interference data point is selected from the following methods: if an abnormal condition occurs in the data, it is judged to be an artifact; the abnormal condition includes exceeding the upper or lower limit of a defined threshold (artifact threshold), exceeding the range defined by the statistical method, and changing the value. , at least one of fitting curve offset, etc.

Outlier data is judged by thresholds of physiological parameters. Specifically, the judgment of the outlier data point Noutlier is made by statistical methods combined with medical professional knowledge, and is selected from any one 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 outlier is exceeded, it is an outlier; the upper and lower limits of the outlier threshold are stipulated according to medical professional common sense or self-judgment, such as human body temperature parameters, and the lower limit of the outlier threshold. is 30, and the upper limit of the outlier threshold is 45; Method 2: Judging 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.; Method 3: Statistical methods are combined with medical professional knowledge. For example, the normal distribution in the statistical method is used for judgment. 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 original 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 original physiological data index (OD', P): {OD' ₀ : {P ₀₀ ... P _0y }, ... OD' _n : {P _n0 ... P _ny }}, anesthesia is obtained after treatment Manage the waveform, value or index of the desired optimized physiological index _IT = F(OD', P) to obtain high-quality physiological data.

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

Example 3

The display system as shown in FIG. 5 includes a display unit 11, a parameter X selection and threshold determination unit 12, a parameter Y selection and threshold determination unit 13; The parameter X selection and threshold determination unit 12 and the parameter Y selection and threshold determination unit 13 are respectively connected to the display unit 11 , and the condition of each physiological parameter or physiological index is presented by the display unit 11 in two-dimensional form.

The above-mentioned display system can visualize the physiological data in the form of nine regions (as shown in Figure 4). Nine-area means that the upper and lower thresholds of different physiological parameters or physiological indicators represented by the x and y axes divide the display area into nine areas to form a nine-area 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 learning optimization 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 falling above the upper threshold, below the lower threshold, or between the upper threshold and the lower threshold, it is divided into: low-high area, low-excellent area, double-low area, Excellent high area, excellent low area, double high area, high excellent area, 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-described embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle 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 scope to determine the 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 instruments 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] is a schematic structural diagram of the device provided in Example 1. [FIG. [Fig. 2] is a schematic flowchart of the physiological data processing method provided in the second embodiment. [FIG. 3] A schematic diagram of the nine-area display method provided in Embodiment 3. [FIG. [Figure 4] is a nine-area display diagram. [FIG. 5] A schematic structural diagram of the nine-area display system provided in Embodiment 3. [FIG.

1: Sampling instrument connection module

2: Physiological data baseline value determination module

3: Optimize the scope to determine the 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 (11)

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 with the sampling instrument connection module and/or the physiological data optimization analysis module, preferably with the sampling instrument connection module and the physiological data optimization analysis module Modules are connected separately. The device according to claim 1, wherein the sampling instrument connection module connects at least two sampling instruments, for example, two or more sampling instruments that are the same or different from each other; Preferably, the sampling instrument connection module obtains the original physiological data of the management object from the sampling instrument; Preferably, 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 to be retained after screening; preferably, the sampling instrument connection module The group can transmit the raw physiological data of the management object acquired by the sampling instruments 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"; More preferably, 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, which may also be referred to as "the original physiological data with the best quality". The device according to claim 1 or 2, wherein the collection and management device further comprises a management object data module, which is used to acquire and/or transmit the data of the management object; Preferably, 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, marital status, past medical history, genetic medical history, etc.), concurrent diseases, surgery History, family history, taking drugs, allergy history, living habits (smoking, drinking, drug use), diagnostic data (laboratory test results, electrocardiogram test results, various imaging test results, ultrasound test 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 life Sign parameters, various respiratory parameters, various brain function monitoring parameters, various tissue perfusion oxygenation parameters, body temperature, inhaled oxygen concentration, carbon dioxide at the end of exhalation), etc.; Preferably, the management object data module is connected with a hospital information system (HIS) to obtain required management object data, such as its data in the perioperative period; Preferably, the management object data module includes an input device, and the management object data is input manually or non-manually. The device according to any one of claims 1 to 3, 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 data according to the management object data. The optimized physiological data index, so that the sampling instrument connection module transmits the required original physiological data according to the optimized physiological data index; Preferably, the physiological data is the physiological data of the management object, such as the physiological data of the management object in the perioperative period (pre-operative, intra-operative and/or post-operative management period), ICU, intensive care, emergency care, etc., or Physiological data in perioperative, non-ICU, non-critical, non-emergency care stages, or in stages not intended for treatment or diagnosis; Preferably, the management object includes, but is not limited to, living human, animal or inanimate human, animal body, specimen or sample; Preferably, the data of the management object include basic data, electronic medical record-related data, surgical data, and/or baseline status; Preferably, the optimized physiological data indicators are 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, etc.; Preferably, the physiological data optimization analysis module is connected to the physiological data quality analysis module to obtain the original physiological data transmitted by the connection and transmission of the physiological data quality analysis module, such as higher quality original physiological data or best quality original physiological data. physiological data; Preferably, 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 data, such as numerical values and/or maps; Preferably, the data collection and management device further comprises one, two or more data storage modules to store the physiological data acquired or transmitted by the data storage modules; Preferably, the connection or transmission can be performed in a wired or wireless manner, preferably a direct connection or transmission via a wired or wireless manner; for example, a wired manner includes a serial port or a network port; or the cloud; Alternatively, the connection or transmission is carried out by means of the cloud, preferably the indirect connection or transmission is carried out through the cloud. A method for processing physiological data, the processing method comprising processing the physiological data using the collection and management device for physiological data according to any one of claim items 1 to 4; Preferably, the processing method calculates the waveform and value of the physiological index for optimal management required for management according to the best original physiological data of the management object and the set of index parameters; Preferably, the optimal raw physiological data is obtained by performing quality analysis on multiple groups of raw physiological data, and/or the set of index parameters is determined from the data of the management object. The processing method according to claim 5, wherein the method may include the following steps: S1: Obtain the raw physiological data of the management object from the sampling instrument, and obtain the information 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={T ₀ , ..., T _x } to be optimally managed, and the corresponding index parameter set P = {P ₀₀ , ..., P _{0y ,} ... , P _{x0 ,} ..., P _xy }; S3: For any physiological index T _i that needs to be optimally managed, automatically search for the required sampling instrument, obtain packets from the required sampling instrument, parse the agreement, and obtain the original physiological Data OD={OD _{0 ,} ..., OD _m }; 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 best original physiological data OD' and the index parameter set P, calculate the physiological index I _T =F(OD', P) for optimal management The waveforms and values obtained by the instrument can obtain higher-quality physiological data; Preferably, in step S3, the analysis protocol includes a data analysis protocol and/or standardization protocol customized by the instrument; Preferably, in step S3, the parsed physiological data Divided into numerical data and waveform data according to the resolution; Preferably, in step S4, the quality of each physiological data is analyzed or calculated according to factors including the integrity of the data record and the validity of the data record; The factors of the integrity of the data record and the validity of the data record include factors such as the data to be collected, the missing data, the actual data, the interference data, the outlier data and the available data; preferably, the interference data passes the same index or has a correlation index. Judging whether there is interference in the data; Preferably, the outlier data is judged by the physiological parameter threshold; Preferably, in step S5, the calculation method includes but is not limited to: AUC; The ratio exceeding the upper and lower absolute thresholds; Exceeding the upper and lower limits of the relative baseline value ratios relative to thresholds; optimized physiological ratios and durations; physiological indicators obtained by other statistical methods; and physiological indicators obtained by machine learning. A physiological data display device, the display device comprising the physiological data collection and management device according to any one of claims 1 to 4, 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 collection and management device of the physiological data; Preferably, the physiological data display device further includes a storage module; Preferably, the storage module is used for storing the physiological data optimally managed data outputted by the physiological data optimization analysis module of the physiological data collection and management device. A method for displaying physiological data, the display method comprises using a display module to visually optimize the physiological data collected by the physiological data of any one of the request items 1 to 4, and the physiological data optimization analysis module outputted by the management device. change; Preferably, after using the physiological data processing method described in claim 5 or 6, the obtained high-quality physiological data is visualized, for example, shown on a display device; Preferably, the visualization or display is an instant display or a statistical display. Application of the collection and management device for physiological data according to any one of claims 1 to 4 and/or the processing method according to claim 5 or 6 in processing and/or displaying physiological data. A device for optimal management of physiology, comprising the device for collecting and managing physiological data according to any one of claims 1 to 4. A method for optimal management of physiology, comprising the method for processing physiological data as described in claim 5 or 6.

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