Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- lmproved Diagnosis of Breast Cancer via NLP Analysis of Radiological Reports(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11) Sosa Silva, Patricia Angelli; Tamez Peña, José Gerardo; emimmayorquin; Martínez Ledezma, Emmanuel; Avendaño Davalos, Betzabeth; School of Engineering and Sciences; Campus Monterrey; Santos Díaz, AlejandroToe main objective of this thesis was to evaluate the use of natural language processing (NLP) techniques and machine learning models to improve the specificity of breast cancer diagnosis and reduce false-positive rates using a dataset of radiological reports from Mexican hospitals. Toe methodology involved text preprocessing, feature extraction using NLP techniques and classification using machine learning models for the radiological reports. The preprocessing consisted of lemmatization, stop-word removal, and tokenization. Various NLP techniques were then applied, including bag-of-words, TF-IDF, Word2Vec embeddings, and ClinicalBERT embeddings. These were used as input features for classical machine learning models (Logistic Regression, Random Forest, Extreme Grading Boosting, Naive Bayes, k-Nearest Neighbors, Support Vector Machine and their ensemble) as well as a deep learning LSTM model. The models were trained, calibrated, and evaluated using metrics: AUC, accuracy, precision, recall, specificity and Fl-score. The key findings showed that the ensemble model with Bag-of-words and SVM using TF-IDF vectorized reports achieved the best performance, with an AUC of 0.79, specificity of 0.27 and AUC of 0.80 and specificity of 0.26, respectively. Thess model was able to identify all true positive cases while reducing the number of unnecessary biopsies by 19.49% and 15.08%, respectively. Feature importance analysis revealed that terms like "speculated", "irregular", and "4a category" were critica! for breast cancer classification. In contrast, the deep learning LSTM model performed poorly, with an AUC of only 0.52 and specificity of O. These results demonstrate the potential of NLP and machine learning techniques to enhance the reliability of breast cancer diagnosis and management, reducing the burden of unnecessary medica! procedures on patients and the healthcare system. The theoretical implications include the importance of effective feature engineering and the limitations of deep learning models for this specific task.
- Design and fabrication of bioreactors for tissue engineering(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-06) González Abrego, Ana Valeria; Rodríguez González, Ciro A.; lagdtorre/tolmquevedo; Martínez López, José Israel; Trujillo de Santiago, Grissel; Moisés Álvarez, Mario; School of Engineering and Sciences; Campus Monterrey; Dean, DavidTissue engineering (TE) has provided new techniques to create better tissue models, for study or to solve actual medical problems. Combining TE with design and 3D manufacture techniques can achieve devices that improve actual models. 3D tissue models present a diffusion problem that causes cell death because of the lack of oxygen and nutrients and the concentration of cell waste. Proving flow to the constructs can facilitate perfusion and enhance tissue. To do so, this document presents the designs and prototype development of two bioreactors, with the objective of diminishing necrotic core to create relevant implantable bone tissue and a more realistic breast cancer model. Using DLP and commercially available parts, designs were prototyped and validated.
- Machine learning and cox based benchmarking tool: exploration of survival models associated with chronic degenerative diseases(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-04-30) Orozco Sánchez, Jorge Andrés; TAMEZ PEÑA, JOSE GERARDO; 67337; Tamez Peña, Jose Gerardo; emipsanchez; Trevino Alvarado, Víctor Manuel; Martínez Ledesma, Juan Emmanuel; Martínez Torteya, Antonio; Escuela de Ingeniería y ciencias; Campus Monterreythe present work reports the exploration of the CoxBenchmarking function applied to chronic-degenerative disease datasets associated with survival. CoxBenchmarking implementation is a computer-based benchmarking algorithm that compares the Survival Models that were constructed by several machine learning strategies. It was developed as an extension of FRESA.CAD package and uses its Random Holdout Cross-Validation. CoxBenchmarking provides an algorithm that generates eleven distinct survival models through feature selection of ML-based techniques: 6 wrappers and 5 filters. Besides, the function summarizes the results with tables and graphs by providing a well-ordered data structure and a plot function.
