Disclosure of Invention
The invention aims at overcoming the technical defects in the prior art and provides a clinical nutrition digital diagnosis and treatment method and system.
The technical scheme adopted for realizing the purpose of the invention is as follows:
a method of clinical nutrition digital diagnostics comprising the steps of:
the database stores nutritional status and disease diagnosis and treatment information of historical patients, venous energy value and nutrient proportion information in clinical treatment, EN nutrition scheme result information formed by man-machine interaction in the individual diagnosis and treatment process of clinicians and nutritionists, and corresponding objective effect data;
according to the inputted disease diagnosis and treatment information of the current patient, the database automatically learns by utilizing an artificial intelligence algorithm, and an EN nutrition scheme which is consistent with the disease diagnosis and treatment information of the current patient and has the optimal objective effect is matched.
The disease diagnosis and treatment information comprises weight, sex, age, height, body temperature, human body composition analysis index, disease information, disease specific index change, stress coefficient and EN contraindications of the patient.
Wherein the EN contraindications include digestive tract bearing capacity and fluid volume bearing capacity.
After a theoretical nutrition prescription is formed through a preliminary rule of an intelligent nutrition prescription generation system, a doctor combines disease information, disease specific index change, stress coefficients, human body component analysis indexes and EN contraindications of a patient to select a target energy value, a nutrition type and various nutrition similar duty ratios, PN energy values are deducted, and then a nutritional engineer confirms or adjusts and modifies the obtained EN energy values within a certain range.
Preferably, the certain range is 20 to 150%.
Wherein the nutritional status indicator comprises pre-albumin, C-reactive protein, hemoglobin, lymphocyte count, total white blood cells, and platelet count.
Wherein the disease-specific index comprises blood ammonia, blood creatinine, blood urea nitrogen BUN), blood potassium, blood sodium, blood sugar, blood lipid, alanine aminotransferase ALT, total bilirubin Bil, prothrombin time PT, alkaline phosphatase che, urine volume, 24-hour urine protein quantification, creatine kinase CK, aspartic aminotransferase AST and lactate dehydrogenase LDH.
The invention also aims to provide a clinical nutrition digital diagnosis and treatment system, which comprises:
the server is provided with a database, and the database stores nutrition states and disease diagnosis and treatment information of historical patients, venous energy value and nutrient proportion information in clinical treatment, and human-computer interaction in the personalized diagnosis and treatment process of clinicians and nutritionists to form EN nutrition scheme result information and objective effect data;
and the computer is in communication connection with the server and is used for inputting the disease diagnosis and treatment information of the current patient, receiving the disease diagnosis and treatment information of the current patient from the database of the server, utilizing an artificial intelligence algorithm to autonomously learn, matching the output EN nutrition scheme which is consistent with the disease diagnosis and treatment information of the current patient and has the optimal objective effect, and displaying the EN nutrition scheme on a display interface.
Preferably, the computer is connected with an on-line weighing device, a human body component analysis device and a hospital medical record system which are controlled in a wireless mode.
The invention can make clinical nutrition diagnosis and treatment exert the due auxiliary clinical treatment effect, shorten the recovery period of patients, reduce the death rate of diseases and lighten the family members and social burden of patients. By constructing an intelligent platform for cross fusion of clinical medicine and nutrition, the system can play a role in high efficiency in no matter in a special period (epidemic situation) or daily life.
Detailed Description
The invention is described in further detail below with reference to the drawings and the specific examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The invention aims to carry out accurate individualized nutrition diagnosis and treatment and clinical effect objective evaluation on malnutritional patients, automatically store relevant information such as clinical nutrition diagnosis and treatment process and effect into a database or a server, upload cloud after information security filtering, accumulate the information into big data of clinical nutrition diagnosis and treatment and effect evaluation thereof, and carry out automatic operation, induction and summarization by a set artificial intelligent algorithm to finally form an accurate individualized clinical nutrition diagnosis and treatment scheme for the malnutritional patients.
As shown in fig. 1, the clinical nutrition digital diagnosis and treatment method of the present invention comprises the following steps:
the database stores nutritional status and disease diagnosis and treatment information of historical patients, venous energy value and nutrient proportion information in clinical treatment, EN nutrition scheme result information formed by man-machine interaction in the individual diagnosis and treatment process of clinicians and nutritionists, and corresponding objective effect data;
according to the inputted disease diagnosis and treatment information of the current patient, the database automatically learns by utilizing an artificial intelligence algorithm, and an EN nutrition scheme which is consistent with the disease diagnosis and treatment information of the current patient and has the optimal objective effect is matched.
The disease diagnosis and treatment information comprises weight, sex, age, height, body temperature, human body composition analysis index, disease information, disease specific index change, stress coefficient and EN contraindications of the patient.
Wherein the EN contraindications include digestive tract bearing capacity and fluid volume bearing capacity.
After a theoretical nutrition prescription is formed through a preliminary rule of an intelligent nutrition prescription generation system, a doctor combines disease information, disease specific index change, stress coefficients, human body component analysis indexes and EN contraindications of a patient to select a target energy value, a nutrition type and various nutrition similar duty ratios, PN energy values are deducted, and then a nutritional engineer confirms or adjusts and modifies the obtained EN energy values within a certain range.
In the above technical solution, the human body component analysis device detects a patient, and the output parameters include: body FAT percentage (FAT%), body FAT mass, muscle mass, body moisture content (TBW), protein, inorganic salt, body weight, target body weight, body weight to be increased or decreased, body FAT mass and muscle mass, basal Metabolic Rate (BMR), body Mass Index (BMI), FAT mass in each part, FAT percentage, muscle mass (right leg, left leg, right arm, left arm, trunk), and the like.
In the above technical solution, the database information includes:
1. patient information: disease area, bed number, age, sex, height, weight, body temperature, and stress coefficient.
2. Nutritional status and disease information: clinical and nutritional diagnostics, and screening for test results associated therewith; wherein, the nutrition index (commonality) comprises: pre-albumin, C-reactive protein, hemoglobin, lymphocyte count, total white blood cells, platelet count.
Disease-specific indicators include blood ammonia, blood creatinine, blood Urea Nitrogen (BUN), blood potassium, blood sodium, blood glucose, blood lipid, alanine Aminotransferase (ALT), total bilirubin (Bil), prothrombin Time (PT), alkaline phosphatase (che), urine volume, 24-hour urine protein quantification, creatine Kinase (CK), aspartate Aminotransferase (AST), lactate Dehydrogenase (LDH), and the like. Scoring information for each different disease species.
3. Venous energy value and nutrient proportion information in clinical treatment. PN (parenteral nutrition) information includes sugars: 10% glucose, 5% glucose, 10% glucose MG3 as a compound electrolyte, 5% glucose sodium chloride, 1% sodium potassium magnesium calcium glucose, 10% fructose (Feng Hai energy), 10% fructose (prikang). Amino acids (proteins): 20% alanylglutamine, 40 amino acid of low molecular weight dextran, 10% compound amino acid (anping), 11.4% compound amino acid 18 AA-II (11.4), 8.5% compound amino acid 18 AA-II (8.5), 4.26% compound amino acid 3AA,5.59% compound amino acid 9AA,6.74% pediatric compound amino acid 18AA. Fat: fatty milk amino acid glucose [ carpin ], 20% medium/long chain fatty milk (C6-24), 20% medium/long chain fatty milk (C8-24 v), and the like.
4. The human-computer interaction track and the result information formed by clinical and nutritional disciplines fusion in the personalized diagnosis and treatment process of clinicians and nutritionists are repeatedly discussed and communicated by nutritional and clinical (ICU) specialists by referring to nutritional treatment guidelines of nearly 3 years at home and abroad, and the preliminary rule of the human-computer exchange intelligent nutritional prescription generation system which is approved by both clinicians and nutritionists is formed. According to the Harris-Benedict formula: male bei: 66.4730+13.751 body weight (kg) +5.0033 height (cm) -6.7550 age (years) =kcal. Female bei: 655.0955+9.463 weight (kg) +1.8496 height (cm) -4.6755 age (years) =kcal. The average national demand value is 12.5% lower than the formula result value, and the theoretical energy demand value of the patient is obtained.
And starting a man-machine fusion exchange mechanism, and adjusting the doctor within the range of 20-150% of a theoretical value according to the acquired accurate information and the patient illness state and digestion bearing capacity. The method comprises the following steps:
s1, starting comprehensive operation according to a preliminary rule, and displaying theoretical energy value of a patient and total energy and type for doctors to refer, namely, the ratio (%) of three nutrients of carbohydrate, protein and fat.
S2, a doctor performs nutrition risk screening and determines whether EN contraindications exist according to the illness state, wherein the EN contraindications comprise digestive tract bearing capacity, liquid bearing capacity and the like, and a target energy value and type (three nutrient duty ratio) are selected.
S3, the system comprehensively calculates the total energy (PN+EN) and the type, PN energy value in clinical treatment and the type formed naturally, and displays the EN energy value and the type (man-machine interaction) to be executed.
S4, confirming or modifying (range is less than or equal to 10%) by the nutritionist, and confirming again (human-to-human interaction) by the clinician.
S5, the two parties confirm to enter a manual or automatic disposal machine. The clinical nutrition digital diagnosis and treatment system fuses respective emphasis points of a clinician and a nutritionist on a nutrition prescription forming mode to form discipline crossing.
Therefore, a novel mode of integrating disciplines, man-machine interaction and intelligent generation of EN accurate nutrition prescriptions is realized, and audience quantity and efficiency of nutrition diagnosis and treatment are improved.
5. The information of the patient and the clinical nutrition digital diagnosis and treatment process and the prognosis situation thereof are stored to form a database, wherein the database comprises the differences of the weight, the analysis and change of human body components, the nutrition index and the disease specific index of the patient, the actual nutrition prescription, the theoretical and target energy values and the proportion, and the database is stored in association with the nutrition and the disease prognosis of the patient.
6. According to clinical needs, the clinical nutrition digital diagnosis and treatment process and relevant information of the change are screened, keywords observed at different angles are set, regular data are obtained through circulation operation, and continuously updated nutrition prescription generation rules are formed through autonomous learning.
The artificial intelligence algorithm can correlate the nutrition common index and the specific index change of the whole disease before and after the patient with the nutrition diagnosis and treatment process and effect, thereby obtaining the best nutrition diagnosis and treatment scheme of patients with different disease types, disease periods, illness states, different ages and sexes.
Such as training learning in combination with the AdaBoost algorithm and decision tree algorithm. The AdaBoost algorithm refers to a classifier with a poor classification effect as a weak classifier and a classifier with a good classification effect as a strong classifier. The basic principle of the Adaboost algorithm is to reasonably combine a plurality of weak classifiers (the weak classifiers generally adopt a single-layer decision tree) to form a strong classifier. Adaboost adopts the iterative idea, only one weak classifier is trained in each iteration, and the trained weak classifier participates in the next iteration. That is, in the nth iteration, there are a total of N weak classifiers, where N-1 is previously trained, and various parameters thereof are no longer changed, the nth classifier is trained this time. The relation of the weak classifiers is that the Nth weak classifier is more likely to divide data which are not divided into the previous N-1 weak classifiers, and the final classification output is to see the comprehensive effect of the N classifiers.
As shown in fig. 2, the present invention further provides a clinical nutrition digital diagnosis and treatment system, comprising:
the server is provided with a database, and the database stores nutrition states and disease diagnosis and treatment information of historical patients, venous energy value and nutrient proportion information in clinical treatment, and human-computer interaction in the personalized diagnosis and treatment process of clinicians and nutritionists to form EN nutrition scheme result information and objective effect data;
and the computer is in communication connection with the server and is used for inputting the disease diagnosis and treatment information of the current patient, receiving the disease diagnosis and treatment information of the current patient from the database of the server, utilizing an artificial intelligence algorithm to autonomously learn, matching the output EN nutrition scheme which is consistent with the disease diagnosis and treatment information of the current patient and has the optimal objective effect, and displaying the EN nutrition scheme on a display interface.
The computer has input device including display terminal, keyboard or mouse or touch screen interface input device, corresponding processing program software is installed in the device for inputting corresponding search parameter or key word on the configured program search interface, and receiving corresponding information for searching inputted by external device, such as patient weight, human body composition information, etc. filled in corresponding position for inputting, can be configured to have corresponding selection interface for inputting information, such as by pull-down menu, user can select corresponding input information on the program interface, and the device is convenient to use, and can be configured with result display module for displaying corresponding matched EN nutrition scheme with optimal objective effect in one area of the display terminal.
Preferably, the computer is connected with a wireless controlled on-line weighing device, a human body component analysis device and a nutrition processing device. Wherein the database can be connected with a hospital medical record system to collect and update the data information quantity.
The on-line weighing device can be connected with a sickbed, is arranged on the sickbed, realizes on-line weighing of patients, transmits obtained information to a computer for storage or processing, and can analyze on-line human body components of the patients and transmits analysis junctions to the computer for processing.
Wherein, human component analysis device is through detecting the patient, and output parameter includes: body FAT percentage (FAT%), body FAT mass, muscle mass, body moisture content (TBW), protein, inorganic salt, body weight, target body weight, body weight to be increased or decreased, body FAT mass and muscle mass, basal Metabolic Rate (BMR), body Mass Index (BMI), FAT mass in each part, FAT percentage, muscle mass (right leg, left leg, right arm, left arm, trunk), and the like.
The nutrition treatment device is a nutrition treatment machine and is used for automatically configuring corresponding nutrients according to the output EN nutrition scheme under the control of the computer, and automatically packaging and providing the corresponding nutrients for patients. The method can be configured to send a treatment signal to the nutrition treatment device according to the output EN nutrition scheme by a computer, and the nutrition treatment device can be automatically packaged according to the EN nutrition scheme by the nutrition treatment device, or the nutrition treatment device can be manually controlled to be operated according to the EN nutrition scheme to package the nutrition.
According to the invention, a clinical nutrition digital diagnosis and treatment equipment system is established, a doctor starts a clinical nutrition treatment basic mode of man-machine interaction and discipline intersection and fusion with a dietician interaction, and a simple, accurate and individual nutrition prescription is provided for a patient suffering from the severe disease by comprehensively considering theoretical, target, PN and EN energy values, carbohydrates, proteins and fat proportions, patient energy and liquid bearing capacity, so that the nutrition diagnosis and treatment audience quantity is effectively expanded.
The invention utilizes the clinical nutrition digital diagnosis and treatment equipment system to divide clinicians and nutritionists into a plurality of groups on the clinical nutrition diagnosis and treatment of severe patients, and excites the mutual learning and scientific exploration interests of the clinicians and the nutritionists. The method is characterized in that the possibility of changing the current situation that all basic indexes of nutrition used at present are based on foreign standards including special medical food formula principles and the like is explored, a shared platform is formed by storing a database, and the obtained optimal schemes of nutrition diagnosis and treatment of patients with different disease types, disease periods, illness states and different ages and sexes are circularly learned and corrected through an artificial intelligence algorithm, so that the method lays a foundation for independently making the nutrition diagnosis and treatment standard of China.
The invention utilizes the characteristic that the wireless control can finish accurate nutrition diagnosis and treatment outside the disease area, so that the invention can realize high-efficiency and accurate nutrition treatment effect no matter in a special period (epidemic situation) or daily life, and the overall treatment effect of patients is improved.
The invention solves the problems that clinical nutrition personnel are insufficient and talents are seriously deficient to influence clinical nutrition development: the system plays a role of introducing a high-tech nutrition diagnosis and treatment system, thereby establishing a basic mode of starting a clinical nutrition digital diagnosis and treatment equipment system which is intersected and fused by disciplines of man-machine interaction and interaction with a dietician by a doctor, providing a simple, accurate and individual nutrition prescription for comprehensively considering theory, targets, PN and EN energy values, carbohydrates, proteins, fat proportions, patient energy and liquid bearing capacity for a severe patient, and effectively expanding nutrition diagnosis and treatment audience without increasing personnel.
The invention solves the problems of incomplete clinical nutrition diagnosis and treatment research data and doubtful nutrition treatment effect evaluation indexes: a complete database of accurate individualized clinical nutrition diagnosis and treatment processes and objective effects is established, the nutrition diagnosis and treatment effects are objectively evaluated in aspects of nutrition indexes, disease clinical indexes, lean body mass and the like which are recorded dynamically, and the nutrition diagnosis and treatment effects and the relevant information of patients are stored in a server, so that the relevant nutrition and disease diagnosis and treatment information of patients suffering from severe malnutrition and the nutrition diagnosis and treatment processes and objective effects are stored, and relevant shared big data are formed.
According to the invention, by establishing an artificial intelligent algorithm system, the accumulated clinical nutrition diagnosis and treatment process and the big data formed by objective effect information storage are processed by artificial intelligent algorithms at different angles, a brand-new clinical nutrition diagnosis and treatment mode is created by means of the artificial intelligent algorithms, including autonomous learning, induction summarization and the like, so that nutrition diagnosis and treatment rules or schemes of severe patients with malnutrition are obtained, and a foundation is laid for developing comprehensive clinical nutrition research in China.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.