Big data and machine learning tools to understand mastitis epidemiology and other topics
| dc.contributor.advisor | Barkema, Herman W. | |
| dc.contributor.advisor | Deardon, Rob | |
| dc.contributor.author | Naqvi, Syed Ali | |
| dc.contributor.committeemember | Williamson, Tyler | |
| dc.contributor.committeemember | Dufour, Simon | |
| dc.date | 2022-02 | |
| dc.date.accessioned | 2021-11-22T19:13:31Z | |
| dc.date.available | 2021-11-22T19:13:31Z | |
| dc.date.issued | 2021-11 | |
| dc.description.abstract | Increased availability of technologies to collect and store individual health data is leading to a growing interest in applying Big Data analytical methodologies to better understand health and disease in both humans and dairy cattle. Data collected through routine observations such as doctor or veterinary visits, milking equipment, or remote sensors can be successfully incorporated to monitor and manage individual and public health, and support operational decision-making on dairy farms. These sources of data also provide an invaluable resource in conducting epidemiological and health research, provided they are appropriately handled during the statistical analysis. In this thesis, 1) data from bacteriological sampling were combined with regularly collected dairy herd improvement (DHI) data to describe udder health in primiparous dairy cattle across Canada; 2) a systematic review and meta-analysis was conducted to synthesize all available research on the effectiveness of pre-calving therapies to improve udder health in primiparous dairy cattle; 3) a model was developed for the detection of clinical mastitis (CM) based on routinely collected data from automated milking systems (AMS); 4) a simulation study assessed the impact of unmeasured heterogeneity in secondary data collected from multiple dairy farms on the performance of a model trained to detect CM onset; 5) the immune fingerprint of children presenting with symptoms of appendicitis are compared by combining emergency department admissions data with results from a multiplex cytokine assay and 6) dietary risk factors for immunological flare-ups in patients with Crohn’s disease are explored by combining patient-reported dietary records with results of a multiplex cytokine assay. Chapter 2 demonstrated that the udder health in Canadian primiparous dairy cows was an issue that needed attention, and chapter 3 demonstrated that pre-calving treatments of different types can be effective at improving udder health in early lactation. Both chapters highlighted the need for routinely collected data to be combined with targeted data collection (monitoring of non-milking dairy cows, culture-based treatment selection) to facilitate targeted management for different parts of a dairy herd. In chapter 5, a deep recurrent neural network (RNN) model was used to detect the onset of CM using regularly collected data from AMS, and chapter 6 demonstrated that predictive performance of deep RNNs is robust to the unmeasured heterogeneity in data collected from multiple farms. Chapter 6 describes how immune response differs between children with abdominal pain symptomatic of appendicitis and provides evidence that data from a multiplex immunoassay conducted on admission may be used to effectively predict disease outcomes. In chapter 7, a similar multiplex immunoassay is used to explore associations between inflammation and diet using food records from patients with Crohn’s disease and demonstrates some of the statistical challenges encountered when working with multiple outcomes and large numbers of explanatory variables. | en_US |
| dc.identifier.citation | Naqvi, S. A. (2021). Big data and machine learning tools to understand mastitis epidemiology and other topics (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/39392 | |
| dc.identifier.uri | http://hdl.handle.net/1880/114131 | |
| dc.language.iso | eng | en_US |
| dc.publisher.faculty | Cumming School of Medicine | en_US |
| dc.publisher.institution | University of Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | en_US |
| dc.subject | dairy | en_US |
| dc.subject | mastitis | en_US |
| dc.subject | machine learning | en_US |
| dc.subject | neural networks | en_US |
| dc.subject | precision medicine | en_US |
| dc.subject.classification | Biostatistics | en_US |
| dc.subject.classification | Veterinary Science | en_US |
| dc.subject.classification | Epidemiology | en_US |
| dc.title | Big data and machine learning tools to understand mastitis epidemiology and other topics | en_US |
| dc.type | doctoral thesis | en_US |
| thesis.degree.discipline | Medicine – Community Health Sciences | en_US |
| thesis.degree.grantor | University of Calgary | en_US |
| thesis.degree.name | Doctor of Philosophy (PhD) | en_US |
| ucalgary.item.requestcopy | true | en_US |