Machine learning models for screening and diagnosis of infections

Abstract

Millions of people around the globe die or are severely burdened every year at the hands of infections. These infections can occur in wounds on the surface of the body, often after surgery. They also occur inside the body as a result of hazardous contact with infectious pathogens. Many of the victims of infections reside in developing countries and have little access to proper diagnostic resources. As a result, a large portion of these infection victims go without diagnosis until the effects of the infection are severely life-threatening. My research group has focused on developing tools to aid in disease screening for patients in developing areas over the past seven years. For this thesis project, I developed a Logistic Regression model that screens for infections in surgical site wounds using features extracted from visible light images of the wounds. The extracted features convey information about the texture and color of the wound in the LAB color space.

This model was able to achieve nearly perfect classification results on a testing set of 143 patients who were part of a clinical study conducted on C-section patients at clinical facilities in rural Rwanda. Given the outstanding results of this model, our group is looking to incorporate it in a mobile screening application for surgical site infections that is currently being developed. I also built a framework for extracting features to be used in diagnosing infectious pulmonary diseases from thermal images of patients' faces. The extracted features capture information about temperature statistics in different regions of the face. This framework was tested on a small group of patients who participated in a study being conducted by our partners at the NIH. To test the framework, I used the features it extracted from each image as input for a Logistic Regression classifier that predicted whether or not the image subject had an infectious pulmonary disease.

This model achieved an average accuracy of 87.10% and AUC of 0.8125 on a testing set of 32 thermal facial images. These results seem motivating as a preliminary assessment of the power of the extracted thermal features. We plan on expanding the framework to utilize the features with more advanced models and larger datasets once the workers in the study have been able to screen more patients. Finally, I conducted an experiment analyzing gender and socioeconomic bias that may be present in previous models used by our group to screen patients for pulmonary diseases (COPD, asthma, and AR). The experiment observed the effects of training a model on a set of patients that is demographically skewed towards a majority group on the model's testing performance on patients of all groups (majority, minority, and all patients).

This experiment uncovered no significant biases in a model trained and evaluated on datasets of patients screened in previous and current studies conducted by partners of our group. These results were positive, but our group is still interested in finding additional ways to ensure that data collected for our research does not encode unwanted biases against members of any demographic groups that our tools may be utilized by.