Can AI Increase Patient Safety or Reduce Morbidity and

Author: Zen and the Art of Computer Programming

1 Introduction

At present, human beings' fear of robots is intensifying, and people believe that they will bring huge catastrophic consequences to society. To address this problem, AI has been increasingly used in the medical field. By monitoring patients in real time and implementing corresponding treatment strategies, AI can improve patients' sense of security in life and death. But at the same time, there are also some questions: Can AI really improve the health of patients?

In this article, we will review human efforts, explore whether the potential of AI can transform the medical industry, and how to evaluate the prospects of this emerging field. In the process, we will elaborate on the role and limitations of AI in medical care, and explore the game process between humans and AI. Finally, we will propose how to ensure that AI can produce optimal health effects.

2. Basic concepts and terminology

First, we need to understand the definitions and concepts of related terms such as AI, medical care, and diseases.

Intelligence: refers to all phenomena, abilities or characteristics related to perception or using wisdom to do something. It includes research results from neuroscience, psychology, cognitive science, linguistics, mathematics and other disciplines. Intellectual deficiencies due to various reasons can lead to the occurrence of mental illness or behavioral diseases and other diseases.
Artificial Intelligence (AI): An intelligent computer system based on calculation, imitation, learning, and self-update models. The main features are: (1) Simulating human natural reactions; (2) Humans can process non-material information quickly; (3) High degree of intelligence.
Medical treatment: Medical treatment is the process of providing medical services to patients using life and health technology, surgical techniques, drugs, etc. Medical technology, pharmaceutical technology, basic medical knowledge, clinical experience and other contents are integrated with each other.
Disease: refers to physical, psychological, morbidity, death and other illnesses of the human body or tissues caused by abnormal conditions such as environmental pollution, genetic factors, cancer, infectious diseases, endocrine disorders, etc. Generally speaking, diseases are the result of many factors working together.
Patient: refers to the person receiving medical treatment, that is, the person receiving diagnosis and treatment.
Medicine: Medicine is the scientific research, experiment and application of human life and health. Its purpose is to better prevent, control and cure diseases, and to optimize the lifestyle of disease patients. It involves doctors, nurses, pharmacists, nutritionists, neuroscientists, biologists, forensic doctors, radiologists, midwives, anesthesiologists and other medical disciplines.
Epidemiology: Epidemiology starts from a single case and uses statistical methods to study the outbreak virus in a group or a certain area and the diseases it causes. It is closely related to public health, health policy, people's health, and economic development.
Antibiotics: Antibiotics are chemicals that kill bacteria and promote an immune response within cells, and prevent viruses from replicating.

3. Core algorithm principles and specific operation steps

(1) Algorithm introduction

1. Support Vector Machine (SVM)

SVM is a support vector machine, a binary classification algorithm that can perform linear or nonlinear segmentation of data. The basic idea behind SVM is to find a hyperplane such that there is a maximum separation between two types of data. In this way, new input samples can be easily divided into two categories.

During the training phase of SVM, the algorithm selects two different samples as support vectors, and all other samples lie outside the boundaries of these two support vectors. The algorithm then searches for the straight line that maximizes the distance from the support vector.

2. Random Forest (RF)

Random forest is an ensemble method that takes the form of multiple trees, each tree segmenting a different feature. During the training phase, each tree is trained on previously generated random data samples. Finally, the random forest will output the conclusions of multiple trees, and finally take the data classification determined by the majority vote.

The advantages of random forest are that it is easy to handle multi-dimensional data, adapts to outliers of data, and has wide applicability.

3. Gradient Boosting (GBM)

(2) Specific operation steps

During the training phase of SVM, two different data samples need to be selected as support vectors, and all other samples are located outside the boundaries of these two support vectors. The algorithm then searches for the straight line that maximizes the distance from the support vector.

Random forest generates several decision trees during the training phase, and then combines these trees into a model. For any data sample to be predicted, the random forest model only needs to be run once to give the corresponding classification result.

The gradient boosting algorithm continuously adds new weak models iteratively during the training phase to achieve the purpose of improving the model. The specific steps are as follows:

Initialize weights to be uniformly distributed;
For each sample, solve for its negative gradient (that is, the derivative of the loss function on its output value);
Update the model parameters so that the predicted value of the next iteration sample on the current model is more accurate than the last predicted value;
Repeat steps 2 to 3 until the model converges or the iteration conditions are met.

4. Specific code examples

(1) SVM training process code example

from sklearn import svm
import numpy as np
X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
y = np.array([1, 1, 2, 2])
clf = svm.SVC(kernel='linear', C=1.0).fit(X, y)
print(clf.predict([[2., 2.], [-1., -2.]])) # Output: [2 1]

(2) Random Forest training process code example

from sklearn.ensemble import RandomForestClassifier
import pandas as pd
import numpy as np
df = pd.read_csv('train.csv')
X = df.iloc[:, :-1].values
y = df['target'].values
rf = RandomForestClassifier()
rf.fit(X, y)
test = pd.read_csv('test.csv').values
result = rf.predict(test)

(3) Gradient Boosting training process code example

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.ensemble import GradientBoostingClassifier
import matplotlib.pyplot as plt
data = load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(
    data.data, data.target, test_size=0.2, random_state=42)
gbc = GradientBoostingClassifier().fit(X_train, y_train)
plt.plot(range(1, len(gbc.estimators_) + 1),
         gbc.train_score_.mean(axis=1))
plt.xlabel('n_estimators')
plt.ylabel('Training Accuracy')
plt.show()

5. Future development trends and challenges

With the development of biomedical technology, the rapid spread of the new coronavirus epidemic, the emergence of multiple modes of infection, people's attention to epidemic prevention and control measures and other factors, the raging momentum of the new coronavirus is still significant. People hope to use technological means to reduce the risk of new coronavirus infection as much as possible to protect the basic rights and interests of a healthy life. However, at present, the development of science and technology still lags far behind the development of diseases. Therefore, it remains to be seen and tracked on how to ensure that AI can produce optimal health effects.

On the other hand, the application of AI in the medical field is still in its infancy. In practical applications, the problems to be solved are still very complex, such as data quality, cost of annotating data, time consumption of training models, privacy protection, etc. To ensure the healthy effects of AI, more long-term tracking and practice are needed.

6. Appendix Frequently Asked Questions and Answers

Q1: Please briefly describe the principles and ideas of the SVM algorithm?

SVM (Support Vector Machine), the Chinese name is Support Vector Machine, is a binary classification algorithm. Its principle is to find a hyperplane so that there is a maximum distance between two types of data. In other words, it is to find a straight line or hypersurface that can separate the data points, so that the internal distance between the two types of data is maximized and the external distance is minimized. The algorithm idea of SVM is to find the optimal hyperplane by solving the convex quadratic programming problem, so as to maximize the distance between two categories and minimize the internal distance of the two categories.

Q2: Please briefly describe the principles and ideas of the random forest algorithm?

Random Forest is a machine learning method that can be used for both classification and regression tasks. It takes the form of multiple trees, each segmenting different features. During the training phase, each tree is trained on previously generated random data samples. Finally, the random forest will output the conclusions of multiple trees, and finally take the data classification determined by the majority vote. The algorithm idea of random forest is to build multiple decision trees and complete classification by combining multiple trees.

Q3: Please briefly describe the principles and ideas of the gradient boosting algorithm?

Gradient boosting algorithm is a branch of machine learning and a way to enhance the model. By gradually adding new weak models, a serial ensemble is formed. Continuously improve the performance of the base learner through iterative methods. The algorithm idea of the gradient boosting algorithm is to use the prediction results of the previous model to correct the prediction error rate of the current model again, thereby increasing the overall prediction accuracy rate.