A diabetes syndrome element diagnosis system and signal data processing method and system

Abstract

This invention belongs to the field of medical technology and discloses a diagnostic system for diabetes syndrome elements and a signal data processing method. Patients submit prescriptions through the prescription application module and complete a questionnaire based on their actual situation in the questionnaire completion module. The system, through its analysis module, automatically summarizes symptoms based on the patient's choices using a syndrome element measurement diagnostic method, calculates the weight of each syndrome element, and intelligently analyzes to derive syndrome element results at levels one, two, and three. Medical personnel complete the "Syndrome Name Diagnosis" and "Treatment Suggestions" based on the analysis results. The prescription management module records the information of the prescription creator and reviewer and generates managed prescriptions. The printing module prints and distributes the prescriptions to the patients. This system achieves automated analysis through sophisticated algorithms, providing patients with accurate treatment plans.

Description

Technical Field

[0001] This invention belongs to, but is not limited to, the field of medical technology, and particularly relates to a diagnostic system for diabetes mellitus and a method for processing signal data. Background Technology

[0002] Traditional methods of diagnosing diabetes are subjective, and different doctors may make different judgments on the same patient’s syndrome based on their own experience and understanding. Moreover, the complexity of diabetes syndrome makes the diagnosis process more difficult, requiring comprehensive consideration of multiple factors, including symptoms, tongue appearance, pulse, etc. The current diabetes syndrome differentiation software on the market has functional limitations and cannot fully meet clinical needs. Therefore, a diabetes syndrome differentiation diagnostic system is needed to explore the location of the disease and the nature of the pathogenic factors by exploring the TCM syndromes, syndrome elements, and syndrome types of diabetic patients, so as to provide ideas for the prevention and treatment of diabetes. (2) Based on the patient’s options on the scale, the system automatically calculates the weight of each syndrome element of the patient through a precise algorithm, and intelligently analyzes it to give the patient’s syndrome element results of level one, two, and three. All calculations are automatically completed by the system. (3) By obtaining the patient’s syndrome elements through the diabetes syndrome differentiation scale, the patient’s syndrome type is determined, and then a precise diagnosis and treatment plan is provided for the patient.

[0003] The published document CN108198626A proposes a method and system for establishing a TCM intelligent consultation form for diabetes. By constructing a consultation meta-model, the traditional "observation, auscultation and olfaction, inquiry" information is abstracted, and the patient's self-reported information is pre-diagnosed and scored accordingly, thereby realizing TCM intelligent pre-screening of diabetes.

[0004] While the system can collect and quantify symptom information in a structured manner, it does not incorporate key metabolic indicators (such as FINS, HOMA-IR, TG, LDL, etc.) or TCM syndrome element analysis, thus failing to provide syndrome elements and diagnostic patterns. Furthermore, the system primarily focuses on abstracting medical history items and pre-diagnostic scores, without generating TCM prescription suggestions based on specific syndrome element combinations. This lack of clinically applicable treatment guidance hinders truly personalized TCM intervention and a closed-loop clinical feedback system. Summary of the Invention

[0005] To address the problems existing in the prior art, the present invention provides a diagnostic system for diabetes mellitus.

[0006] This invention is implemented as follows: a diagnostic system for diabetes mellitus, comprising:

[0007] The patient data acquisition module is used to obtain basic patient information, vital signs data and metabolic indicators, including but not limited to fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), fasting insulin (FINS), insulin resistance index (HOMA-IR), pancreatic β-cell function index (HOMA-β), fasting C-peptide (FCP), blood lipids (TG, LDL, TC) and other indicators.

[0008] The syndrome element identification and classification analysis module is connected to the questionnaire filling module. Based on the preset syndrome element integration rule and expert knowledge base, the system automatically classifies the syndrome elements of the disease location (such as spleen, liver, kidney) and the syndrome elements of the disease nature (such as dampness, heat, yin deficiency, etc.), and completes the syndrome element characteristic analysis and classification diagnosis in combination with the patient's metabolic indicators.

[0009] The prescription management and generation module is used to manage the prescription generation process, including prescription application, reviewer information entry, prescription writing and archiving;

[0010] The output module is used to display or print the diagnostic report and personalized intervention plan in graphic and textual form for reference by both doctors and patients and for subsequent intervention.

[0011] Furthermore, the analysis module incorporates a linear weighted model to quantify the multi-dimensional symptom information obtained by the questionnaire completion module;

[0012] The linear weighted model combines various symptom indicators According to the corresponding weighting coefficients The summation is performed to form a comprehensive score S, calculated using the following formula:

[0013]

[0014] in, This represents the numerical results of multi-dimensional symptom indicators, including initial cause, chills and fever, sweating, nature of pain, sleep and mood. The corresponding weighting coefficient is indicated by the analysis module, which makes a preliminary determination of the distribution of each syndrome element of the patient based on the comprehensive score S.

[0015] After calculating the comprehensive score and logistic regression probability, the analysis module further refines the symptom element scores using a fuzzy inference algorithm; this fuzzy inference algorithm is applied to each symptom indicator. Set the corresponding fuzzy membership function. The fuzzy sets corresponding to each indicator are combined and processed.

[0016] The final output is a fuzzy comprehensive assessment of the patient's syndrome elements such as "Yin deficiency", "Yang deficiency" and "phlegm and blood stasis".

[0017] Furthermore, the analysis module combines a logistic regression model to perform probability estimation of the patient's symptom indicators across various dimensions. The logistic regression model converts the comprehensive score S into the probability of occurrence of specific symptom elements using the following formula. :

[0018]

[0019] in, and The regression coefficients are obtained in advance through training with sample data. The analysis module determines the patient's tendency to have different syndromes based on the value of the probability p.

[0020] The analysis module also uses a mathematical model combining principal component analysis (PCA) and cluster analysis to perform multidimensional dimensionality reduction and clustering operations on the obtained comprehensive scores, logistic regression probabilities, and fuzzy evaluation results. By performing K-means or hierarchical clustering on multiple principal components, it identifies the cluster centers of patients under different combinations of syndrome elements and outputs the syndrome element distribution characteristics of the subdivided groups and related prescription suggestions.

[0021] Furthermore, the diagnostic method for elemental measurement specifically includes:

[0022] Weighting of symptoms (mild, moderate, severe): For the chief complaint or severe symptom, multiply the weight by 1.5; for moderate symptom, multiply the weight by 1; for mild symptom, multiply the weight by 0.7. Generally, 20 is used as a universal threshold. That is, when the sum of the contributions of each symptom to each syndrome element reaches or exceeds 20, these syndrome elements can be diagnosed. The severity of the syndrome element can be distinguished based on the sum of the weights: if the total weight is less than 14, the diagnosis of that syndrome element is not valid; if the total weight is 14... 20. This evidence element belongs to level I (primary, relatively minor); the total weight is 21. 30 indicates that the syndrome element belongs to level II (secondary, obvious); if the total weight value is >30, the syndrome element belongs to level III (tertiary, severe). In clinical application, firstly, each symptom, sign, and other medical data of the patient is weighted and summed (including subtraction of negative values) according to the suggested syndrome elements to determine the total weight value of each syndrome element, thereby making a judgment on the syndrome element; then, syndrome elements that exceed (or are higher than) the threshold are organically combined to form a complete syndrome element diagnosis.

[0023] Another objective of this invention is to provide a method for diagnosing diabetes mellitus based on the aforementioned diabetes mellitus diagnostic system, the method specifically comprising:

[0024] S1: The patient submits a prescription request and the prescription is prepared; the patient completes a questionnaire based on their actual situation;

[0025] S2: Based on the selections made by the patient in the questionnaire module, the system automatically summarizes the patient's symptoms according to the syndrome element measurement diagnosis method, and automatically calculates the syndrome element distribution of each score segment according to the calculation logic. Based on the analysis results, medical personnel fill in the "syndrome name diagnosis" and "treatment suggestions".

[0026] S3: The doctor fills in the names of the creator and reviewer to form the prescription; the doctor then prints out the prepared prescription and distributes it to the patient.

[0027] Another object of the present invention is to provide a computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the method for diagnosing diabetic factors.

[0028] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method for diagnosing diabetic factors.

[0029] Another objective of this invention is to provide an information data processing terminal for implementing the diabetes mellitus diagnostic system.

[0030] Based on the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solution to be protected by this invention are as follows:

[0031] The "Diabetes Syndrome Differentiation Software" (1) explores the location and nature of the pathogenic factors in the disease by studying the TCM syndromes, syndrome elements, and syndrome types of diabetic patients, providing ideas for the prevention and treatment of diabetes. (2) Based on the patient's options on the scale, the system automatically calculates the weight of each syndrome element of the patient through a precise algorithm, and performs intelligent analysis to give the patient's syndrome element results at levels one, two, and three. All calculations are automatically completed by the system. (3) By obtaining the patient's syndrome elements through the Diabetes Syndrome Differentiation Software, the system can determine the patient's syndrome type and provide the patient with a precise treatment plan.

[0032] Based on *Internal Medicine of Traditional Chinese Medicine* edited by Zhou Zhongying et al. (China Traditional Chinese Medicine Press, Beijing, 2010) and *Syndrome Differentiation* by Zhu Wenfeng, and after expert review by associate chief physicians and above, 12 categories and 126 symptoms were extracted. Initial causes: irregular diet, aggravated by activity or fatigue, and related to emotional state; chills and fever: frequent aversion to wind, frequent chills, coldness in the epigastrium, abdomen, lower back, and back, severe coldness in the lower limbs, bone-steaming fever, paroxysmal hot flashes, preference for coolness and aversion to heat, preference for warmth and aversion to cold; sweating: spontaneous sweating, night sweats, spontaneous sweating, excessive sweating without discomfort; pain: headache, chest pain, rib pain, epigastric and abdominal pain, muscle pain, back pain, lower back pain, limb pain, finger and toe pain, dysmenorrhea; nature of pain: distending pain, colic, dull pain, empty pain, burning pain. Cold pain, aching pain, dull pain, pain relieved by pressure, pain aggravated by pressure; Head and body discomfort: dizziness, heaviness in the head, foreign body sensation in the throat, palpitations, chest tightness, rib distension, epigastric fullness, stomach discomfort, abdominal distension, fatigue, a feeling of heaviness in the lower back, a feeling of upward rushing of qi, body aches and heaviness, weakness in the lower back and knees, numbness in the limbs and skin, itchy skin, dry eyes, blurred vision; Sleep and emotions: insomnia, vivid dreams, easy awakening, excessive sleepiness, prone to sadness and crying, irritability, timidity and easily startled, depression; Appetite Taste: Thirst, thirst without desire to drink, poor appetite, loss of appetite, chronic poor food intake, tasteless food, postprandial fullness, hunger without desire to eat, easy hunger after eating more, bitter taste, bland taste, bad breath, sweet taste, sticky taste, belching; Bowel movements: Thirst, thirst without desire to drink, poor appetite, loss of appetite, chronic poor food intake, tasteless food, postprandial fullness, hunger without desire to eat, easy hunger after eating more, bitter taste, bland taste, bad breath, sweet taste, sticky taste, belching; Physical signs: Sallow complexion, dull complexion, pale complexion, Dark complexion, red complexion, drooping eyelids, dark circles under the eyes, thin body, obese body, edema, dry skin, skin pigmentation; Tongue appearance: pale tongue, pale and swollen tongue, red tongue, dark red tongue, cracked tongue, tongue with spots, tongue with teeth marks, varicose veins under the tongue, white tongue coating, yellow tongue coating, yellow and white tongue coating, greasy tongue coating, peeled tongue coating, slippery tongue coating, dry tongue coating; Pulse appearance: floating pulse, deep pulse, thin pulse, wiry pulse, slippery pulse, hesitant pulse, soft pulse, intermittent pulse, rapid pulse, slow pulse, weak pulse.

[0033] This software identifies syndrome elements based on diabetes symptoms, thus visualizing the information of these elements. Developed based on a syndrome element-based diagnostic system, this software utilizes syndrome elements as its core diagnostic theory. Its key feature is the identification of syndrome elements based on symptoms, forming a diagnostic thinking process of "symptom (syndrome) syndrome element syndrome name" by combining syndrome elements. This feature fully embodies the essence of diagnostic thinking, simplifying complex concepts and reflecting the process of clinical diagnostic thinking in Traditional Chinese Medicine. It can more objectively, accurately, and scientifically reflect the essence of the disease. Furthermore, the scales used in this software can quantify symptoms (syndromes). The total weight of syndrome elements can be determined based on the different weights of each syndrome on the syndrome elements. Threshold distinctions are also available for the identified syndrome elements, allowing for the organic integration of high-threshold syndrome elements to form a complete syndrome name diagnosis. This objectively reflects the severity of the condition and has significant clinical diagnostic and treatment value.

[0034] By using this software for analysis, accurate syndrome information can be obtained. For example, if the analysis of a patient shows that the third-level syndrome is spleen and qi deficiency, and the second-level syndrome is dampness, it can be inferred that the patient has spleen deficiency and dampness excess syndrome, with spleen deficiency as the main factor. The dampness excess is due to spleen deficiency, and the treatment should focus on strengthening the spleen and replenishing qi, supplemented by removing dampness. Because this software can objectively reflect the nature of the disease in diabetic patients, it can guide the treatment of traditional Chinese medicine and has significant clinical diagnostic and treatment significance. Attached Figure Description

[0035] Figure 1 This is a structural diagram of a diabetes mellitus diagnostic system provided in an embodiment of the present invention;

[0036] Figure 2 This is a schematic diagram of the prescription application module provided in an embodiment of the present invention;

[0037] Figure 3 is a schematic diagram of the questionnaire filling module provided in an embodiment of the present invention;

[0038] Figure 3A This is a schematic diagram illustrating the origin of the problem in the questionnaire completion module;

[0039] Figure 3B A diagram illustrating cold and heat patterns in the questionnaire completion section;

[0040] Figure 3C This is a diagram illustrating sweating in the questionnaire completion module; Figure 3D A diagram illustrating the location of pain in the questionnaire completion section; Figure 3E A diagram illustrating the nature of pain in the questionnaire completion module; Figure 3F A diagram illustrating head and body discomfort in the questionnaire completion section; Figure 3G A diagram illustrating sleep-related emotions in the questionnaire's completion section; Figure 3H A diagram illustrating dietary preferences in the questionnaire completion section; Figure 3I A diagram illustrating urination and defecation in the questionnaire completion module; Figure 3J A diagram illustrating vital signs in the questionnaire completion module; Figure 3K This is a diagram of a tongue in the questionnaire completion module; Figure 3L This is the first view of the pulse diagnosis interface in the questionnaire completion module. Figure 3M The second view of the pulse diagnosis interface in the questionnaire completion module; Figure 4 This is a schematic diagram of the analysis module provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the prescription management module provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of the printing module provided in an embodiment of the present invention. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0042] Example 1: Patient identification and intervention suggestion generation based on "spleen-dampness" syndrome elements

[0043] A 45-year-old male patient with type 2 diabetes mellitus (T2DM) had a body mass index (BMI) of 29 and presented with symptoms such as loss of appetite, heaviness in the limbs, and sticky stools. His fasting plasma glucose (FPG) was 6.9 mmol / L, his fecal insufficiency index (FINS) was 18 μU / mL, his HOMA-β was elevated, and his triglyceride (TG) level was significantly elevated. After completing the Diabetes Syndrome Scale questionnaire, the system automatically identified the primary pathological syndrome as "spleen" and the pathological syndrome as "dampness." The analysis module indicated that the patient exhibited typical characteristics of "spleen deficiency and dampness retention," and the system automatically recommended a traditional Chinese medicine formula focusing on strengthening the spleen and resolving dampness, generating an intervention prescription for the doctor to review and adjust.

[0044] Example 2: Early warning mechanism based on "liver-heat" syndrome elements

[0045] A 52-year-old female patient with type 2 diabetes mellitus (T2DM) presented with symptoms including irritability, bitter taste in the mouth, constipation, and dark yellow urine. Her BMI was 27, HbA1c was 8.2%, FINS was elevated, and HOMA-IR and LDL were significantly elevated. The system automatically identified the primary syndrome element as "liver" in location and "heat" in nature using a questionnaire. Combined with biochemical indicators, it determined that she had a tendency towards insulin resistance. The system provided the physician with treatment recommendations based on the principle of soothing the liver and clearing heat, along with lifestyle adjustment suggestions, including emotional regulation and dietary control.

[0046] like Figures 1 to 6 As shown, an embodiment of the present invention provides a diagnostic system for diabetes mellitus, the system comprising:

[0047] The prescription application module is used by patients to apply for and create prescriptions.

[0048] The questionnaire module is connected to the prescription application module, allowing patients to complete the questionnaire according to their actual situation.

[0049] The analysis module is connected to the questionnaire completion module. Based on the selections made by the patient in the questionnaire completion module, the system automatically summarizes the patient's symptoms according to the syndrome element measurement diagnosis method, and automatically calculates the syndrome element distribution of each score segment according to the calculation logic. Based on the analysis results, medical personnel fill in the "syndrome name diagnosis" and "treatment suggestions".

[0050] The prescription management module, connected to the analysis module, is used to fill in the creator and reviewer information to generate a prescription management module.

[0051] The printing module, connected to the prescription management module, is used to print out prepared prescriptions and distribute them to patients.

[0052] The analysis module has a built-in linear weighted model to quantify the multi-dimensional symptom information obtained by the questionnaire filling module;

[0053] The linear weighted model combines various symptom indicators According to the corresponding weighting coefficients The summation is performed to form a comprehensive score S, calculated using the following formula:

[0054]

[0055] in, This represents the numerical results of multi-dimensional symptom indicators, including initial cause, chills and fever, sweating, nature of pain, sleep and mood. The corresponding weighting coefficient is indicated by the analysis module, which makes a preliminary determination of the distribution of each syndrome element of the patient based on the comprehensive score S.

[0056] After calculating the comprehensive score and logistic regression probability, the analysis module further refines the symptom element scores using a fuzzy inference algorithm; this fuzzy inference algorithm is applied to each symptom indicator. Set the corresponding fuzzy membership function. The fuzzy sets corresponding to each indicator are combined and processed.

[0057] The final output is a fuzzy comprehensive assessment of the patient's syndrome elements such as "Yin deficiency", "Yang deficiency" and "phlegm and blood stasis".

[0058] The analysis module further incorporates a logistic regression model to estimate the probability of each dimension of the patient's symptoms. The logistic regression model converts the comprehensive score S into the probability of occurrence of a specific symptom using the following formula. :

[0059]

[0060] in, and The regression coefficients are obtained in advance through training with sample data. The analysis module determines the patient's tendency to have different syndromes based on the value of the probability p.

[0061] The analysis module also uses a mathematical model combining principal component analysis (PCA) and cluster analysis to perform multidimensional dimensionality reduction and clustering operations on the obtained comprehensive scores, logistic regression probabilities, and fuzzy evaluation results. By performing K-means or hierarchical clustering on multiple principal components, it identifies the cluster centers of patients under different combinations of syndrome elements and outputs the syndrome element distribution characteristics of the subdivided groups and related prescription suggestions.

[0062] The questionnaire includes 12 dimensions and 126 items, such as the initial cause, chills and fever, sweating, location of pain, nature of pain, discomfort in the head and body, sleep and mood, food taste, urination and defecation, physical signs, tongue appearance and pulse.

[0063] The diabetes syndrome diagnosis system provided in this invention automates the process from patient information collection to prescription generation through the collaborative work of multiple modules. First, the prescription application module allows patients to apply for prescriptions and fill in relevant information within the system. After submitting the application, the system guides the patient to the questionnaire completion module. In this module, patients complete the questionnaire based on their actual situation. The questionnaire covers 12 dimensions, including the initial cause, chills and fever, sweating, pain location, pain nature, headache and body discomfort, sleep and mood, food preferences, bowel movements, physical signs, tongue appearance, and pulse, totaling 126 items. These items comprehensively assess the patient's symptoms, providing basic data for subsequent syndrome diagnosis.

[0064] After the patient completes the questionnaire, the system will enter the analysis module. This module automatically summarizes the patient's choices in the questionnaire using a syndrome element measurement diagnostic method and calculates the distribution of different syndrome elements in each score range using the system's preset calculation logic. Based on these calculation results, medical personnel can view the analysis report generated by the system, which details the score and distribution of each syndrome element. Medical personnel then fill in the "Syndrome Name Diagnosis" and "Treatment Suggestions" based on these analysis results, providing a basis for subsequent treatment plans.

[0065] Next, the system manages the generated prescriptions through the prescription management module. This module allows medical personnel to fill in the information of the prescription creator and reviewer, and to perform necessary reviews and modifications on the prescription content to ensure the accuracy and standardization of the prescriptions. After the prescription management is completed, the prescription is saved by the system and ready for the next step of processing.

[0066] Finally, the prescription is processed through the printing module. The system transfers the approved prescriptions from the prescription management module to the printing module and generates printable prescription documents. Medical personnel can print out the prescription and distribute it to the patient, or send it to the patient digitally, facilitating further treatment. This series of processes, through the operation of the automated system, greatly improves the efficiency of diagnosis and prescription management, while also ensuring the accuracy and consistency of diagnostic results.

[0067] The specific methods for symptom measurement and diagnosis include:

[0068] Measurement values ​​of syndrome elements and common symptoms:

[0069] Refer to the diagnostic criteria for syndrome elements and the measurement values ​​of common symptoms in "Syndrome Element Differentiation".

[0070] Spleen: Frequent diarrhea = 8, chronic poor appetite = 6, postprandial fullness = 5, abdominal distension = 5, sticky mouth = 6, undigested food in stool = 10, sweet taste in mouth = 6, bland taste in mouth = 4, fatigue and weakness = 5, feeling of heaviness in the chest = 8, drooping eyelids = 5, drowsiness = 6, obesity = 5, body aches and heaviness = 4.

[0071] Liver: Hypochondriac pain = 12, hypochondriac distension = 8, emotional depression = 8, irritability = 5, dry eyes = 4, blurred vision = 4, dizziness = 4, numbness of limbs and skin = 7, bitter taste in mouth = 4, wiry pulse = 4.

[0072] Kidney: Soreness and weakness of the lower back and knees = 7, lower back pain = 7, frequent urination at night = 10, frequent urination = 8, undigested food in stool = 8, edema = 8, dark complexion = 7, weak pulse = 6.

[0073] Stomach: Abdominal pain = 10, epigastric distension = 8, belching = 8, stomach discomfort = 8, excessive hunger = 8, lack of appetite = 8, postprandial epigastric distension = 6, chronic poor appetite = 4, loss of appetite = 4, halitosis = 4, irregular eating habits = 4.

[0074] Large intestine: Frequent constipation = 10, abdominal pain = 4, abdominal bloating = 4, frequent flatulence = 4, sticky and uncomfortable stools = 5.

[0075] Yin deficiency: Night sweats = 9, bone steaming fever = 6, paroxysmal hot flashes = 4, frequent constipation = 6, dry eyes = 5, insomnia = 4, dreaminess = 4, excessive appetite and hunger = 2, cracked tongue = 5, peeled tongue coating = 4, thready pulse = 4, rapid pulse = 4.

[0076] Yang deficiency: Frequent aversion to cold = 8, severe coldness in the lower limbs = 8, preference for warmth and aversion to cold = 8, coldness in the stomach, abdomen, waist and back = 7, undigested food in stool = 10, frequent diarrhea = 6, frequent urination at night = 6, spontaneous sweating = 6, cold pain = 5, edema = 5, pale and swollen tongue = 5, slow pulse = 5.

[0077] Fever: Thirst = 4, Yellow urine = 4, Red tongue = 5, Yellow coating = 6, Yellow and white tongue coating = 4, Slippery pulse = 4, Rapid pulse = 4, Red face = 4.

[0078] Dampness: Body soreness and heaviness = 8, obesity = 6, drowsiness = 6, sticky mouth = 8, abdominal distension = 4, soreness = 5, chest tightness = 2, heaviness in the head = 2, teeth marks on the tongue = 3, greasy tongue coating = 8, soft pulse = 6, slippery pulse = 4.

[0079] Phlegm: foreign body sensation in the throat = 6, obesity = 8, chest tightness = 4, dull pain = 4, greasy tongue coating = 6, slippery pulse = 4, body aches and heaviness = 2, numbness in the limbs and skin = 2, sticky mouth = 3.

[0080] Qi deficiency: spontaneous sweating = 8, fatigue and weakness = 5, feeling of qi sinking = 5, frequent aversion to wind = 4, long-term poor appetite = 4, frequent diarrhea = 5, abdominal distension = 4, frequent urination = 5, nocturia = 5, palpitations = 3, edema = 3, pale complexion = 4, pale tongue = 3, thready pulse = 3, weak pulse = 2.

[0081] Blood stasis: Pain aggravated by pressure = 3, dysmenorrhea = 6, dark complexion = 6, skin pigmentation = 3, limb pain = 3, finger and toe pain = 3, spots on the tongue = 8, varicose veins under the tongue = 6, choppy pulse = 8.

[0082] Qi stagnation: distending pain = 9, hypochondriac distension = 8, abdominal distension = 5, epigastric and abdominal distension = 4, emotional depression = 8, belching = 5, frequent flatulence = 6, hypochondriac pain = 4, epigastric and abdominal pain = 4, abdominal pain = 4, colic = 7, foreign body sensation in the throat = 6, wiry pulse = 4.

[0083] Food stagnation: Irregular diet = 10, poor appetite = 3, epigastric distension = 3, undigested food in stool = 3, sticky and uncomfortable stool = 4, greasy tongue coating = 2, slippery pulse = 4.

[0084] Weighting of symptoms (mild, moderate, severe): For the chief complaint or severe symptom, multiply the weight by 1.5; for moderate symptom, multiply the weight by 1; for mild symptom, multiply the weight by 0.7. Generally, 20 is used as a universal threshold. That is, when the sum of the contributions of each symptom to each syndrome element reaches or exceeds 20, these syndrome elements can be diagnosed. The severity of the syndrome element can be distinguished based on the sum of the weights: if the total weight is less than 14, the diagnosis of that syndrome element is not valid; if the total weight is 14... 20. This evidence element belongs to level I (primary, relatively minor); the total weight is 21. 30 indicates that the syndrome element belongs to level II (secondary, obvious); if the total weight value is >30, the syndrome element belongs to level III (tertiary, severe). In clinical application, firstly, each symptom, sign, and other medical data of the patient is weighted and summed (including subtraction of negative values) according to the suggested syndrome elements to determine the total weight value of each syndrome element, thereby making a judgment on the syndrome element; then, syndrome elements that exceed (or are higher than) the threshold are organically combined to form a complete syndrome element diagnosis.

[0085] This invention provides a method for diagnosing diabetes mellitus based on the aforementioned diabetes mellitus diagnostic system. The method specifically includes:

[0086] S1: The patient submits a prescription request and the prescription is prepared; the patient completes a questionnaire based on their actual situation;

[0087] S2: Based on the selections made by the patient in the questionnaire module, the system automatically summarizes the patient's symptoms according to the syndrome element measurement diagnosis method, and automatically calculates the syndrome element distribution of each score segment according to the calculation logic. Based on the analysis results, medical personnel fill in the "syndrome name diagnosis" and "treatment suggestions".

[0088] S3: The doctor fills in the names of the creator and reviewer to form the prescription; the doctor then prints out the prepared prescription and distributes it to the patient.

[0089] This invention provides a computer device, which includes a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method for diagnosing diabetic factors.

[0090] This invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the diabetes mellitus diagnostic method.

[0091] This invention provides an information data processing terminal for implementing the diabetes symptom diagnosis system.

[0092] Example 1: Remote Health Monitoring System for Diabetic Patients

[0093] In a remote health monitoring system for diabetic patients, signal data processing methods are used to monitor and analyze the patients' daily health data. Patients collect physiological data such as blood glucose, heart rate, blood pressure, and body temperature in real time via portable devices, and transmit this data to the system's main control module. The main control module first uses a Kalman filter algorithm to denoise the time-series data of heart rate and blood pressure to reduce the impact of possible fluctuations and outliers. Subsequently, the system applies a Bayesian fusion algorithm to fuse the data of blood glucose, heart rate, blood pressure, and body temperature to generate a comprehensive health status index. This index is input into a random forest model for analysis, and the system assesses the patient's health status based on the model's calculation results. If the system detects that the patient's health status is poor or has a deteriorating trend, the early warning module will generate an alert and notify the patient and their attending physician via SMS or an application so that appropriate management measures can be taken, such as adjusting medication dosage or arranging emergency examinations.

[0094] It should be noted that embodiments of the present invention can be implemented using hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented using hardware circuitry such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., or using software executed by various types of processors, or using a combination of the above-described hardware circuitry and software, such as firmware.

[0095] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A diagnostic system for diabetes mellitus, characterized in that, include: The patient data acquisition module is used to obtain basic patient information, vital signs data, and metabolic indicators; The intelligent questionnaire filling module is connected to the patient data collection module and is used to collect structured symptom information of patients based on the syndrome element differentiation theory. The syndrome element identification and classification analysis module is connected to the intelligent questionnaire filling module. It is used to analyze the patient's symptom information based on the preset syndrome element scoring rules and expert knowledge base, identify the syndrome elements of the location and nature of the disease, and complete the syndrome element classification diagnosis in combination with the metabolic indicators. The intervention suggestion module is connected to the syndrome element identification and classification analysis module and is used to generate corresponding TCM intervention plans based on the patient's syndrome element type. The prescription management and generation module is used to manage the prescription application, review, and prescription generation process; The output module is used to output diagnostic reports and personalized intervention plans.

2. The diagnostic system for diabetic symptom markers according to claim 1, characterized in that, The intelligent questionnaire completion module is used to collect structured electronic questionnaire data from patients based on "Zheng Su Bian Zheng Xue" (Syndrome Differentiation of Syndromes). The questionnaire includes multiple clinical dimensions such as the cause of the disease, cold and heat manifestations, sweating, pain characteristics, head and body symptoms, sleep and mood, dietary preferences, bowel and bladder function, tongue appearance, and pulse, and contains multiple standardized clinical symptom items. The symptom items are standardized according to the "National Standard of the People's Republic of China: Syndrome Section of Clinical Diagnosis and Treatment Terminology in Traditional Chinese Medicine" and "Zheng Su Bian Zheng Xue," and traditional Chinese medicine complex symptoms are decomposed to make each symptom variable independent and without overlap, so as to avoid the same symptom being counted repeatedly or cross-judged. At the same time, the intelligent questionnaire completion module quantifies each symptom into four levels: none, mild, moderate, and severe, corresponding to values ​​of 0, 1, 2, and 3, respectively, to form quantitative symptom data that can be used by the syndrome identification and classification analysis module to calculate the contribution of symptoms to diagnosis, and to provide basic data for subsequent syndrome point analysis and syndrome classification diagnosis.

3. The diagnostic system for diabetes mellitus according to claim 1, characterized in that, The analysis module has a built-in linear weighted model to quantify the multi-dimensional symptom information obtained by the questionnaire filling module; The linear weighted model combines various symptom indicators According to the corresponding weighting coefficients The summation is performed to form a comprehensive score S, calculated using the following formula: in, This represents the numerical results of multi-dimensional symptom indicators, including initial cause, chills and fever, sweating, nature of pain, sleep and mood. The corresponding weighting coefficient is indicated by the analysis module, which makes a preliminary determination of the distribution of each syndrome element of the patient based on the comprehensive score S. After calculating the comprehensive score and logistic regression probability, the analysis module further refines the symptom element scores using a fuzzy inference algorithm; this fuzzy inference algorithm is applied to each symptom indicator. Set the corresponding fuzzy membership function. The fuzzy sets corresponding to each indicator are combined and processed. The final output is a fuzzy comprehensive assessment result of the patient's syndrome elements such as "Yin deficiency", "Yang deficiency" and "phlegm and blood stasis".

4. The diabetic symptom diagnostic system according to claim 1, characterized in that, The analysis module further incorporates a logistic regression model to estimate the probability of each dimension of the patient's symptoms. The logistic regression model converts the comprehensive score S into the probability of occurrence of a specific symptom using the following formula. : in, and The regression coefficients are obtained in advance through training with sample data. The analysis module determines the patient's tendency to have different syndromes based on the value of the probability p. The analysis module also uses a mathematical model combining principal component analysis (PCA) and cluster analysis to perform multidimensional dimensionality reduction and clustering operations on the obtained comprehensive scores, logistic regression probabilities, and fuzzy evaluation results. By performing K-means or hierarchical clustering on multiple principal components, it identifies the cluster centers of patients under different combinations of syndrome elements and outputs the syndrome element distribution characteristics of the subdivided groups and related prescription suggestions.

5. The diabetic symptom diagnostic system according to claim 1, characterized in that, The questionnaire module includes a multi-dimensional questionnaire covering 12 dimensions, including cause, chills and fever, sweating, location of pain, nature of pain, discomfort in the head and body, sleep and mood, food preferences, bowel movements, physical signs, tongue appearance, and pulse. These dimensions are further subdivided into 126 items, allowing patients to fill in the questionnaire in detail according to their actual situation.

6. The diabetic symptom diagnostic system according to claim 1, characterized in that, The diagnostic method using the analysis module includes the following steps: For each symptom, sign, and condition data, a weighted sum is calculated based on the suggested syndrome elements to determine the total weight of each syndrome element. A threshold is set, and when the sum of the contributions of each symptom to each syndrome element reaches or exceeds the set threshold, it is diagnosed as the corresponding syndrome element. The severity of a syndrome is determined by the sum of its weights. If the total weight is less than 14, the diagnosis for that syndrome is invalid. If the total weight is between 14 and 20, the syndrome is classified as Grade I. If the total weight is between 21 and 30, the syndrome is classified as Grade II. If the total weight is greater than 30, the syndrome is classified as Grade III.

7. The diabetic symptom diagnostic system according to claim 1, characterized in that, The prescription management module has access control functionality to ensure that only authorized medical personnel can operate it; at the same time, the module supports modification, review, and rejection of prescriptions to form a complete management process.

8. The diabetic symptom diagnostic system according to claim 1, characterized in that, The printing module can format and print approved prescriptions for easy reading and execution by patients, and supports sending prescriptions electronically to patients' electronic devices.

9. The diagnostic system for diabetic symptom markers according to claim 1, characterized in that, The system also includes a database module for storing patient-completed questionnaire data, analysis results, management prescriptions, and treatment suggestions. It also supports data backup, retrieval, and sharing functions for medical personnel to refer to in subsequent diagnosis and treatment.

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