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

[[alternative]]Mining Demand Chain Knowledge for Collaboration Design and New Product Development

計畫編號：NSC94-2416-H032-001研究期間：200508~200607研究經費：396,000[[abstract]]一般而言，整個製造與商業的運作流程中，資訊流、金流及實體物流的傳遞，大多依 循供應鏈管理(Supply Chain Management)的模式，而上游製造商面對末端顧客需求的同 時，因為資訊流動的落差，所以必須加入本身對於該產品的經驗值來加以預測。相對 地，在供應鏈中越往上遊走，變異性越增大的現象就是所指的「長鞭效應(Bullwhip Effect)」(Dejonckheere et al., 2004) 。但是，隨著生活水準的提升以及製造能力的進步， 過去這種「樣少量多」的生產模式，正被「量少樣多」「求新求變」的商業模式所取代， 意謂者供應鏈的體系，無法完全滿足顧客在這方面的需求。因此以需求端為導向的生 產、製造、銷售、以及產品/設計開發的需求鏈管理 (Demand Chain Management)模式 因而應運而生(Willem et al., 2002)。我國自1920 年代起自行車產業即略具規模，同時 在政府刻意並大力輔導及協助下，1980 年代外銷量首次超越日本，奠定我國自行車產 業在全世界舉足輕重的角色。以巨大機械主力品牌「捷安特」為例，已在全球成為家 喻戶曉的自行車代名詞之一。巨大機械每年提撥大筆經費於研發團隊，在產品材質上 絞盡腦汁，並且在行銷通積極佈局。然而，銷售通路的顧客與產品知識是否充分反映 市場的需求？產品的設計與產品線的規劃，是否能夠將顧客與通路的知識結合？以及 產品在設計與開發的階段，能否將顧客與通路的知識，轉化成企業的知識資產，並在 新產品發展（New Product Development）時，能將這些知識運用在企業與需求端的協 同設計（Collaborative Design）？因此，本研究運用資料探勘 (Data Mining)的技術， 發掘自行車使用者(含同一家庭不同使用者)、產品(含同一家庭不同產品)、通路(含維 修點)、以及個案公司的產品開發知識，結合協同設計的概念，將使用者的需求與產品 的設計，轉化成產品與服務。同時，將顧客特質、地理因素、消費者偏好及市場區隔 等知識，設計成電子型錄以及提供通路行銷的紙本型錄，將新產品開發的知識，運用 於產品線設計(Product Line Design)以及產品創新(Product Innovation)。[[sponsorship]]行政院國家科學委員

Public Health Nursing Clinical Expertise as a Component of Evidence-Based Care

Evidence-based care in public health nursing is meant to guide practice through the integration of research evidence, clinical expertise, and client preference. Minimal study has explored clinical expertise, including what it means to be an expert, how practice wisdom informs decision-making, and whether experiential knowledge can be transformed to the benefit of others. Eight public health nurses, all of whom were highly experienced in providing home visiting services to high risk, pregnant and parenting families, participated in this qualitative inquiry. Interviews were conducted with the goal of exploring, describing, and understanding practice expertise from the perspective of those who know it best. The intended outcome of this research was to establish a source of practical knowledge that could be used in the recruitment and orientation of newly hired nursing staff. Data analysis was guided by a series of sequenced processes described by Colaizzi (1978). Five themes emerged from the data including: 1) public health nursing practice is derived from academic and experiential learning, 2) the knowledge of clinical experts contributes to the practice of others, 3) public health nursing expertise can be described through a collection of certain characteristics, 4) evidence-based nursing isn’t well understood in community health settings, nor are processes fully incorporated into practice; and 5) critical steps of evidence-based care may not fully translate to public health nursing practice. Evidence-based public health nursing practice is a relatively young and as such, there is a need for expanded research, knowledge development, and the creation of meaningful and applicable methods of adapting processes to better reflect the realities of practice

Symptom Experience and Influenza-Like Illness in a Military Population

AIMS: The primary objective of this study was to identify if symptom presentation expressed over the course of an influenza-like illness (ILI) can predict virus type by use of unsupervised machine learning. The secondary objective was to describe clinical characteristics of strain specific coronavirus. Finally, examine the psychometric properties of the Canadian Acute Respiratory Illness and Flu Scale (CARIFS). BACKGROUND: ILI outbreaks have been a significant source of non-battle injury among military personnel. Many different viruses cause ILI, and it is difficult to determine which virus is causing the illness. Recent studies have examined the etiology and epidemiology of ILIs. Other studies have examined influenza virus symptom severity either a dichotomous or liner-sum analysis. No studies to the researcher’s knowledge have examined ILI symptoms through an unsupervised learning analysis, and few studies have examined self-reported outpatient ILI reported symptoms over an extended time frame. METHODS: This is a secondary analysis of data collected over a four year period by the Acute Respiratory Infection Consortium (ARIC), from an otherwise healthy military population. The symptom data was captured on visit days and by a symptom diary patients filled out at home using a symptom severity instrument designed for this study. FINDINGS: Clustering by unsupervised machine learning was unable to predict virus type based on physical symptom presentation over the course of ILI. It did identify patient attributes, like sex and age that caused patients to experience symptoms differently. Additionally, clinical similarities and differences were noted between the four common human coronavirus strains. The strain HKU1 tended to have higher systemic symptom scores and higher gastrointestinal symptom severity score over the course of illness when compared to the other strains. Finally, the psychometric properties of CARIFS revealed many strengths and limitations for its use in research. The CARIFS should be reexamined using current knowledge of symptom management to increase the validity of the instrument. IMPLICATIONS: The results demonstrated how individuals experience physical symptoms differently making it difficult to predict the viral strain causing ILI. Future research should focus on the development of symptom instruments using the theoretical underpinnings of the symptom management theory

Agent-based Modeling of Nursing Opinion Leadership: A Philosophic Analysis and Exemplar Case of a New Theory Development Tool for Nursing.

The purpose of this dissertation was to: examine agent-based modeling (ABM), a new methodological tool, from a nursing philosophy standpoint; evaluate its disciplinary fit; and use the tool for creating and testing a model of nursing opinion leadership. First, in a philosophic analysis of ABM, recurrent themes concerning the use of ABM in multidisciplinary research were identified. These themes (heterogeneity, dynamics, adaptation, emergence, and the metaphorical use of the term “bridge” to describe ABM) were examined from various philosophical positions in nursing. The ABM themes were found to be compatible with multiple philosophic viewpoints within nursing. Further analysis, linking the recurrent themes with nursing metanarratives via exemplars from nursing systems research, revealed that ABM is a methodological tool that is congruent with nursing values. Next, a model of nursing opinion leadership, derived from two philosophic theories of belief formation was developed. The resulting model was then programmed as an ABM. Simulated data, obtained from model execution, depicted opinion leadership as a dynamic process that develops under conditions of uncertainty when credible individuals are available to act as opinion resources. Overall, this dissertation demonstrated the usefulness of ABM as a methodological tool for theory development in nursing.Ph.D.NursingUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61586/1/fauve_1.pd