Dissertation/ Thesis
Probabilistic machine learning for predictions and causal discovery in health informatics.
| Title: | Probabilistic machine learning for predictions and causal discovery in health informatics. |
|---|---|
| Authors: | Nazaret, Achille Oscar Romeo |
| Publication Year: | 2025 |
| Collection: | Columbia University: Academic Commons |
| Subject Terms: | 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 |
| Description: | 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 ... |
| Document Type: | thesis |
| Language: | English |
| DOI: | 10.7916/2fse-kv24 |
| Availability: | https://doi.org/10.7916/2fse-kv24 |
| Accession Number: | edsbas.BAD42952 |
| Database: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://doi.org/10.7916/2fse-kv24# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
|---|---|
| Header | DbId: edsbas DbLabel: BASE An: edsbas.BAD42952 RelevancyScore: 892 AccessLevel: 3 PubType: Dissertation/ Thesis PubTypeId: dissertation PreciseRelevancyScore: 891.935607910156 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Probabilistic machine learning for predictions and causal discovery in health informatics. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nazaret%2C+Achille+Oscar+Romeo%22">Nazaret, Achille Oscar Romeo</searchLink> – Name: DatePubCY Label: Publication Year Group: Date Data: 2025 – Name: Subset Label: Collection Group: HoldingsInfo Data: Columbia University: Academic Commons – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+sciences%22">Medical sciences</searchLink><br /><searchLink fieldCode="DE" term="%22Bioinformatics%22">Bioinformatics</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+informatics%22">Medical informatics</searchLink><br /><searchLink fieldCode="DE" term="%22Health--Information+services%22">Health--Information services</searchLink><br /><searchLink fieldCode="DE" term="%22Medicine--Information+technology%22">Medicine--Information technology</searchLink><br /><searchLink fieldCode="DE" term="%22Probabilities--Computer+programs%22">Probabilities--Computer programs</searchLink><br /><searchLink fieldCode="DE" term="%22Genomics%22">Genomics</searchLink><br /><searchLink fieldCode="DE" term="%22Nucleotide+sequence--Computer+programs%22">Nucleotide sequence--Computer programs</searchLink><br /><searchLink fieldCode="DE" term="%22Acute+myeloid+leukemia%22">Acute myeloid leukemia</searchLink> – Name: Abstract Label: Description Group: Ab Data: 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 ... – Name: TypeDocument Label: Document Type Group: TypDoc Data: thesis – Name: Language Label: Language Group: Lang Data: English – Name: DOI Label: DOI Group: ID Data: 10.7916/2fse-kv24 – Name: URL Label: Availability Group: URL Data: https://doi.org/10.7916/2fse-kv24 – Name: AN Label: Accession Number Group: ID Data: edsbas.BAD42952 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.BAD42952 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.7916/2fse-kv24 Languages: – Text: English Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Medical sciences Type: general – SubjectFull: Bioinformatics Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Medical informatics Type: general – SubjectFull: Health--Information services Type: general – SubjectFull: Medicine--Information technology Type: general – SubjectFull: Probabilities--Computer programs Type: general – SubjectFull: Genomics Type: general – SubjectFull: Nucleotide sequence--Computer programs Type: general – SubjectFull: Acute myeloid leukemia Type: general Titles: – TitleFull: Probabilistic machine learning for predictions and causal discovery in health informatics. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nazaret, Achille Oscar Romeo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2025 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
| ResultId | 1 |