Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Probabilistic machine learning for predictions and causal discovery in health informatics. |
| Συγγραφείς: |
Nazaret, Achille Oscar Romeo |
| Έτος έκδοσης: |
2025 |
| Συλλογή: |
Columbia University: Academic Commons |
| Θεματικοί όροι: |
Artificial intelligence, Medical sciences, Bioinformatics, Machine learning, Medical informatics, Health--Information services, Medicine--Information technology, Probabilities--Computer programs, Genomics, Nucleotide sequence--Computer programs, Acute myeloid leukemia |
| Περιγραφή: |
This thesis is about probabilistic machine learning methods for predictive modeling, causal discovery, and applications in health informatics. Probabilistic models have achieved remarkable performance in text and image modeling, often predicting answers to textual or visual questions better than human experts. But can these methods succeed as well on health data questions? Health data are not limited to text or images -- they include tabular datasets from electronic health records, high-dimensional genomic measurements from single-cell sequencing, or time series from wearable devices. Health questions are not only about predicting an answer alone -- they involve explaining those predictions and discovering unknown relations between variables. This thesis addresses these challenges. It introduces methods that improve probabilistic prediction on common health data types and scale causal discovery to the thousands of variables encountered in genomic datasets. It then develops interpretable generative models for concrete health applications like cancer research with single-cell gene expression and heart health research with wearable time series. These methods are designed for non-experts in machine learning, with software implementations that require little to no tuning. The thesis is organized into four parts, with the first two focusing on broadly applicable methodologies and the last two on concrete health applications of the previous methods. The first part focuses on probabilistic predictions, the task of estimating the full distribution of a target variable given other variables. This task is critical in health applications to quantify uncertainty, compute risk, or detect anomalies. We introduce two methods: treeffuser and unbounded depth neural networks (UDNs).Treeffuser provides probabilistic predictions for tabular data using a diffusion model parameterized by gradient-boosted trees. In contrast, UDNs are deep neural networks. They provide probabilistic predictions as a mixture of outputs from all of their ... |
| Τύπος εγγράφου: |
thesis |
| Γλώσσα: |
English |
| DOI: |
10.7916/2fse-kv24 |
| Διαθεσιμότητα: |
https://doi.org/10.7916/2fse-kv24 |
| Αριθμός Καταχώρησης: |
edsbas.BAD42952 |
| Βάση Δεδομένων: |
BASE |