THE LOGISTIC CURVE AND THE CENSUS COUNT OF 1940

A Novel Automatic Audiometric System Design Based on Machine Learning Methods Using the Brain's Electrical Activity Signals

This study uses machine learning to perform the hearing test (audiometry) processes autonomously with EEG signals. Sounds with different amplitudes and wavelengths given to the person tested in standard hearing tests are assigned randomly with the interface designed with MATLAB GUI. The person stated that he heard the random size sounds he listened to with headphones but did not take action if he did not hear them. Simultaneously, EEG (electro-encephalography) signals were followed, and the waves created in the brain by the sounds that the person attended and did not hear were recorded. EEG data generated at the end of the test were pre-processed, and then feature extraction was performed. The heard and unheard information received from the MATLAB interface was combined with the EEG signals, and it was determined which sounds the person heard and which they did not hear. During the waiting period between the sounds given via the interface, no sound was given to the person. Therefore, these times are marked as not heard in EEG signals. In this study, brain signals were measured with Brain Products Vamp 16 EEG device, and then EEG raw data were created using the Brain Vision Recorder program and MATLAB. After the data set was created from the signal data produced by the heard and unheard sounds in the brain, machine learning processes were carried out with the PYTHON programming language. The raw data created with MATLAB was taken with the Python programming language, and after the pre-processing steps were completed, machine learning methods were applied to the classification algorithms. Each raw EEG data has been detected by the Count Vectorizer method. The importance of each EEG signal in all EEG data has been calculated using the TF-IDF (Term Frequency-Inverse Document Frequency) method. The obtained dataset has been classified according to whether people can hear the sound. Naïve Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms have been applied in the analysis. The algorithms selected in our study were preferred because they showed superior performance in ML and succeeded in analyzing EEG signals. Selected classification algorithms also have features of being used online. Naïve Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms were used. In the analysis of EEG signals, Light Gradient Strengthening Machine (LGBM) was obtained as the best method. It was determined that the most successful algorithm in prediction was the prediction of the LGBM classification algorithm, with a success rate of 84%. This study has revealed that hearing tests can also be performed using brain waves detected by an EEG device. Although a completely independent hearing test can be created, an audiologist or doctor may be needed to evaluate the results.

Development of practical triage methods for critical trauma patients: machine-learning algorithm for evaluating hybrid operation theatre entry of trauma patients (THETA)

PURPOSE

Hybrid operating rooms benefit patients with severe trauma but have a prerequisite of significant resources. This paper proposes a practical triage method to determine patients that should enter the hybrid operating room considering a limited availability of medical resources.

METHODS

This retrospective observational study was conducted using the database from the Japan Trauma Data Bank comprising information collected between January 2004 and December 2018. A machine-learning-based triage algorithm was developed using the baseline demographics, injury mechanisms, and vital signs obtained from the database. The analysis dataset comprised information regarding 117,771 trauma patients with an abbreviated injury scale (AIS) > 3. The performance of the proposed model was compared against those of other statistical models [logistic regression and classification and regression tree (CART) models] while considering the status quo entry condition (systolic blood pressure < 90 mmHg).

RESULTS

The proposed trauma hybrid-suite entry algorithm (THETA) outperformed other pre-existing algorithms [precision-recall area under the curve: THETA (0.59), logistic regression model (0.22), and classification and regression tree (0.20)].

CONCLUSION

A machine-learning-based algorithm was developed to triage patient entry into hybrid operating rooms. Although the validation in a prospective multicentre arrangement is warranted, the proposed algorithm could be a potentially useful tool in clinical practice.

An alternative formulation for a delayed logistic equation

We derive an alternative expression for a delayed logistic equation, assuming that the rate of change of the population depends on three components: growth, death, and intraspecific competition, with the delay in the growth component. In our formulation, we incorporate the delay in the growth term in a manner consistent with the rate of instantaneous decline in the population given by the model. We provide a complete global analysis, showing that, unlike the dynamics of the classical logistic delay differential equation (DDE) model, no sustained oscillations are possible. Just as for the classical logistic ordinary differential equation (ODE) growth model, all solutions approach a globally asymptotically stable equilibrium. However, unlike both the logistic ODE and DDE growth models, the value of this equilibrium depends on all of the parameters, including the delay, and there is a threshold that determines whether the population survives or dies out. In particular, if the delay is too long, the population dies out. When the population survives, i.e., the attracting equilibrium has a positive value, we explore how this value depends on the parameters. When this value is positive, solutions of our DDE model seem to be well approximated by solutions of the logistic ODE growth model with this carrying capacity and an appropriate choice for the intrinsic growth rate that is independent of the initial conditions.

Logistic curve fitting and parameter estimation using nonlinear noniterative least-squares regression analysis

A microcomputer program has been developed for the fitting of the logistic curve to biological, medical, and other experimental data. In addition to supplying estimates for all of the logistic curve parameters, the program provides the fitted result for each input datum thus allowing for the immediate assessment of the logistic curve and detection of possible outliers.