Medical diagnosis, as I understand it at the moment, is based on the sequence matching of brain memories, and then constantly adjust the priority of various possible diagnoses based on real-world experience. Pen × fun × Pavilion www. ｂｉｑｕｇｅ。 Of course, since the reality is the coupling of multiple symptoms, there may be a certain equilibrium reached by the game, that is, atypical symptoms, at this time, we have to decompose the network to a certain extent, and treat it step by step. There are symptomatic treatment and causal treatment, both of which are path choices for the network.

Diagnosis is actually a derivation of the state of the overall network based on limited information, similar to the construction of Euclidean geometric systems. But the reality is that there are always various reasons why valid intrinsic information is obscured or not taken seriously. Therefore, we can only choose conservative broad-spectrum therapy, and finally determine the true essence according to the progression of the disease.

Infections of the immune system may spread their effects in a layered coupling.

Medical diagnosis is all about challenging the impossible. Because the logic of the possibilities is high-dimensional, it may link seemingly unrelated independent events, which is a recombination of reductionist thinking after the decomposition of the network

Due to the existence of similarity, the direction of logical deduction may be wrong, and in the end, the difference is a millimeter, and the error is a thousand miles. This is often an important reason for today's misdiagnosis. Higher dimensional structures need to be established to avoid this, such as the relative proportions of hierarchies. Because for a unique patient, all low-probability events can have already occurred. For example, the symptoms caused by inflammation are similar to the symptoms of cancer.

The secondary effects may be the real intrinsic effects, which have a great influence on the final diagnosis of the disease, that is, the outcome of the hierarchical game

An electrocardiogram is a projection of a networked body network, and the waves measured by the leads of different parts of the body are a kind of quantification, which we can recombine to form a real network. Such as conduction systems.

Essentially, it is a simulation of the cycles of the body's network, that is, the electrical activity that accompanies the cardiac cycle is recorded.

Specific structures of the heart: sinus node, atrioventricular node, Purkinje fiber, etc. are intrinsic. P waves, QRS complexes, and T waves are a periodic depiction of the activation of the signals of the cardiac cycle (cardiac depolarization, repolarization)

The measurement of cell potential changes, multi-level coupling (cell, tissue and organ) information extraction, is the overall description of the change of ion current (also a network-like movement: 0 phase depolarization, Na+ inflow, -90~+30mV1 phase fast repolarization, K+ outflow, Cl-inflow, 30~0mV2 phase slow repolarization, Ca+ slow inflow, K+ outflow, 0mV or so 3 phase rapid repolarization endstage, K+ fast outflow 4-phase quiescent period, sodium pump Na+, Ca+, Cl-outward, K+ internal transport, restoring ion concentrations inside and outside the cell. is a dynamic competition)

The relationship between the cardiomyocyte potential map and the body surface electrocardiogram is the relationship between the microscopic level of the cell and the macroscopic level of the tissue, just as the motion of particles is infinite, but the macroscopic nature can be reflected in the whole, that is, the distribution function

Galvanic theory: Measure the interaction idealized to the point charge f=kq1q2/r^2 (inversely proportional to the number of cardiomyocytes (myocardial thickness); the position of the probing electrode is inversely proportional to the square of the distance between the cardiomyocytes. It is related to the angle formed by the direction of the probe electrode and the direction of the depolarization of the heart, and the larger the angle, the smaller the potential. ）

ECG vector loop: The annular trajectory composed of the top line of the instantaneous integrated ECG vector in a cardiac cycle is the projection of different dimensions of the network, and the ECG is formed by the three-dimensional cardiac vector loop through two projections.

We record the manifestations of simple diseases as a classic feature: angina, left and right ventricular hypertrophy, insufficient blood supply, and so on. When it comes to the clinic, the complex situation can be regarded as a combined expression of the network

Arrhythmias are specific combinations of networks that do not conform to a specific pattern: 1.arrest2.escape3.escape3.normalrhythm, 4.normal5.tachycardia, 6.flutter, flutter7.fibrillation. Specific homeostasis is required.

Tongue diagnosis is the characteristic manifestation of the body's network, and specific network changes may manifest the characteristics in specific parts. For example, gallstones may cause edema of the tongue. It is worth noting that our two sides of the body can be seen as relatively independent modules. This is related to the developmental process of the embryo.

This is related to the macroscopic phenomenology of traditional Chinese medicine, which is a selective expression of the network "there are all inside, all must be done outside"

The five viscera are relatively independent levels, and their combination can form a network. Just as the five elements are macrocosmic images

Using various optimization algorithms of computers to manage diseases (as a problem with data collocation)

The database can be used to simulate the network of the organism, in which the data is the relationship between the real world, which indicates that we adopt a certain data structure to reduce the overall data redundancy, that is, a relatively independent level. One of the most important relationships for databases. The next step is the operation of the relationship.

Data structures are represented in the form of classes

Set theory is the mathematical basis of databases, and the system of predicate logic is a system of operations that imitates reality.

The storage and retrieval of data (human memory and experience accumulation), the formation of patterns and logical structures that we can understand.

Mapping between hierarchies and hierarchies; The distributed structure allows the network to have a relatively stable state.

The extraction of relations is the selective expression of the Cartesian product between sets, i.e., subsets. That is, the product between the sets forms a certain matrix, the projection of different dimensions is vertical, and the selective expression is the expression of different proportions.

The intersection, union, complement, etc. of set theory are simple addition, subtraction, multiplication, and division, and then how to introduce calculus, that is, for matrix elements (submatrices of matrices), it is necessary to establish a high-dimensional function structure (the addition and subtraction of high-dimensional is equal to the integration of low-dimensional)

Taylor series decomposition, Fourier series, is used for hierarchies

There is a certain direction in the relationship

The basic principle of data learning is through the processing and sorting of probabilities, which is large-scale data computing. The clustering of the network is probabilistic. Fitting of functions, continuous classification, which is path finding for networks, multi-level, alternating feature mapping, subsampling and local averaging of neural networks.

Diagnosis is the search and matching of accurate information, and the effectiveness of diagnosis is positively correlated with the degree of matching.

Knowledge representation, i.e., data structures, are the basis of operations

Measurement is the extraction of the intrinsic information of the network, and we define some criteria to define the boundaries, so that the measurement of the network can be carried out, these standards are relatively independent levels, and their selective expressions (such as the relative proportion m/s of addition, subtraction, multiplication and division) are all kinds of data that we can understand. Different theories can be used at different levels. It is essentially a variety of mathematical processing and understanding of the waves of the signal (Fourier series, time domain, frequency domain).

Standardized, normalized, using a common set of languages. Establish a certain correspondence to the quantification of the organism network. In its mathematical form, I prefer the representation of probabilistic networks. Starting from experience, a hierarchical coupling structure is constructed, which corresponds to dialectical argumentation and treatment, and is continuously revised through sufficient data. The coupling of the signal is its convolution.

Large-scale data processing of the network and construction of quantitative data through complex proportional operations. Visualization is a kind of high-dimensional structural operation, which constructs high-dimensional structures by extracting intrinsic information. (Dimensionality reduction first, then dimensionality upgrading). The level of statistics can make the eigens emerge. This is a regression operation in statistics

The construction of various levels: molecules, cells, tissues, organs, systems, wholes, and different levels are multi-level selective expression. This is the pattern formation of the sequence.

The use of instruments aids in the extraction of intrinsic information. Such as organic lesions. Whereas, functional disease is the selective expression of the network and has not yet exceeded the threshold

Outside the company, see the subtle knowledge, and often change are the characteristics of the network. The overall examination, the combination of four diagnoses and references, and the combination of disease and syndrome are all low-dimensional sequence matching and ascending dimensions of the corresponding network

Disease-syndrome-symptom is a pattern of different levels of the body's network and different levels of the sequence, all of which are multivariate selective expressions. We can only process high-dimensional data, which may have certain omissions, but this is the structure of resource allocation in ancient times, because their computing power is limited, and the upgrading of data will bring a certain amount of information loss. Now we can select data on the basis of low-dimensional operations to improve the accuracy of information matching. This requires a certain algorithm to process, and we process it by decomposing it into multi-level data, and then comprehensively consider it, that is, extract the essence of the information. The collection of low-dimensional data can determine various indices by relative proportions

Normalization of various concepts, followed by fuzzy logic processing. Decomposing the network into microscopic levels allows for greater accuracy in the computation of data.

Complex systems are like black boxes, and the relationship between input and output is selectively expressed, which requires the comprehensive action of each system. Systems biology must be made up of multi-layered data synthesis to describe the overall framework in the language of mathematics, and to accumulate experience to make quantitative descriptions and predictions.

Using the similarity of the layers, the sensation can provide meaningful data, such as cold and heat. Then there is the general condition of the human body: sweating (multi-level coupling), excretory function, diet, sleep, etc., and determine the location of change. This is multi-level information extraction. Then there is the construction of the model, which builds a certain model based on different measurements of a particular standard.

The symptoms of TCM are a new perspective and new level of understanding of diseases, which is relatively macroscopic and phenomenic, and conforms to the human understanding model. The combination is a high-dimensional structure. The compatibility of drugs is also available. The understanding of sublayers can be built into a system similar to that of Newtonian mechanics, because this is a new classification method that may be able to be described in mathematical language. The classification of diseases in Western medicine is relatively macroscopic, and can continue to be refined into different symptom types, which also has a certain distribution.

Big data, multi-level data is represented in the form of a matrix, like the pixels of a screen. This is followed by a matching operation with the classical data entered in advance, and the degree is determined based on its similarity. Eigen-based calculations are the most efficient, i.e., they carry more information and have greater specificity. Cluster analysis, using statistics to establish correlations between different symptoms

A disease is a change in the overall network that exceeds a certain threshold, leading to an outbreak of the network, that is, an emergent nature

Based on the multi-level complex concept network representation principle, the knowledge of group structure finds that meaningful information is the intrinsic path of the network, which is a distribution. Multiple levels rely on different dimensions of data understanding

Digitalization, through multi-means measurement, provides computational data for decision-making models.

Quantification, relative proportionality, degree. The correlation between handprints and chromosomal disorders is one example

Meridians are network intrinsic and a statistical result, with no corresponding biological structure, and regression models can be established through multi-level electrophysiological measurements. Then the first is the measurement of electrical signals, which requires extremely fine sensors to collect tiny waves for data processing, because the periodicity of life can be expected to have an impact on the fluctuations of the measurement, and of course the pathological situation is also, but this is precisely the direction of application.

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