Online Machine Learning Algorithms Review and Comparison in Healthcare

Currently, the healthcare industry uses Big Data for essential patient care information. Electronic Health Records (EHR) store massive data and are continuously updated with information such as laboratory results, medication, and clinical events. There are various methods by which healthcare data is generated and collected, including databases, healthcare websites, mobile applications, wearable technologies, and sensors. The continuous flow of data will improve healthcare service, medical diagnostic research and, ultimately, patient care. Thus, it is important to implement advanced data analysis techniques to obtain more precise prediction results.Machine Learning (ML) has acquired an important place in Big Healthcare Data (BHD). ML has the capability to run predictive analysis, detect patterns or red flags, and connect dots to enhance personalized treatment plans. Because predictive models have dependent and independent variables, ML algorithms perform mathematical calculations to find the best suitable mathematical equations to predict dependent variables using a given set of independent variables. These model performances depend on datasets and response, or dependent, variable types such as binary or multi-class, supervised or unsupervised.The current research analyzed incremental, or streaming or online, algorithm performance with offline or batch learning (these terms are used interchangeably) using performance measures such as accuracy, model complexity, and time consumption. Batch learning algorithms are provided with the specific dataset, which always constrains the size of the dataset depending on memory consumption. In the case of incremental algorithms, data arrive sequentially, which is determined by hyperparameter optimization such as chunk size, tree split, or hoeffding bond. The model complexity of an incremental learning algorithm is based on a number of parameters, which in turn determine memory consumption

The quantum chemical physics of few-particle atoms and molecules

The many-electron Schrödinger equation for atoms and molecules still remains analytically insoluble after over 90 years of investigation. This has not deterred scientists from developing a large variety of elegant techniques and approximations to workaround this issue and make many-particle quantum calculations computationally tractable. This thesis presents an all-particle treatment of three-particle systems which represent the simplest, most complex, many-particle systems including electron correlation and nuclear motion effects; meaning they provide a close-up view of fundamental particle interaction. Fully-Correlated (FC) energies and wavefunctions are calculated to high accuracy (mJ mol−1 or better for energies); and the central theme of this work is to use the wavefunctions to study fundamental quantum chemical physics. Nuclear motion has not received the same attention as electronic structure theory and this complicated coupling of electron and nuclear motions is studied in this work with the use of intracule and centre of mass particle densities where it is found nuclear motion exhibits strong correlation. A highly accurate Hartree-Fock implementation is presented which uses a Laguerre polynomial basis set. This method is used to accurately calculate electron correlation energies using the Löwdin definition and Coulomb holes by comparing with our FC data. Additionally the critical nuclear charge to bind two electrons within the HF methodology is calculated. A modification to Pekeris’ series solution method is implemented to accurately model excited states of three-particle systems, and adapted to include the effects of nuclear motion along with three Non-Linear variational Parameters (NLPs) to aid convergence. This implementation is shown to produce high accuracy results for singlet and triplet atomic excited S states and the critical nuclear charge to bind two electrons in both spin states is investigated. Geometrical properties of three-particle systems are studied using a variety of particle densities and by determining the bound state stability at the lowest continuum threshold as a function of mass. This enables us to better ascertain what is meant when we define a system as an atom or a molecule

The effect of single nucleotide polymorphisms and metabolic substrates on the cellular distribution of mammalian BK channels.

Humans are approximately 99% similar with inter-individual differences caused in part by single-nucleotide polymorphisms (SNPs), which poses a challenge for the effective treatment of disease. Bioinformatics resources can help to store and analyse gene and protein information to address this challenge, however these resources have limitations, so the collation and biocuration of gene and protein information is required. Using the large conductance calcium- and voltage-activated potassium channel, also known as the Big Potassium (BK) channel as an example, due to its ubiquitous expression and widespread varied role in human physiology, this study aimed to prioritise SNPs with the potential to affect the function of the channel. Using a BK channel resource created with bioinformatics tools and published literature, mSlo SNPs H55Q and G57A, located in the S0-S1 linker, were prioritised and selected for lab-based verification. These SNPs flank three cysteine residues proven to modulate channel cellular distribution via palmitoylation, a reversible process shown to increase protein association with the cell membrane. The SNPs alter the predicted palmitoylation status of C56, one of the cysteine residues located in the S0-S1 linker. The cellular distribution of BK channels incorporating the SNPs was assessed using confocal microscopy and revealed that the direction and magnitude of SNP mimetic cell membrane expression was closely related to the C56 predicted palmitoylation score; a 'C56 palmitoylation pattern' was observed. It was shown that exposure to metabolic substrates glucose, palmitate and oleate modulated SNP-mimetic cellular distribution and could invert the 'C56 palmitoylation pattern', indicating that there is interplay between the metabolic status of the cell and the amino-acid composition of the channel via palmitoylation. The creation of a novel BK channel resource in this thesis highlighted the limitations, and inter-dependency of bioinformatics and lab based experimentation, whilst SNP verification experiments solidified the link between S0-S1 cysteine residues and BK cellular distribution. BK channel function is linked with a number of physiological processes; thus, the potential clinical consequences of the SNPs prioritised in this thesis require further research