scholarly journals Loss Reserving Models: Granular and Machine Learning Forms

Risks ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 82 ◽  
Author(s):  
Taylor
Keyword(s):  
Machine Learning ◽  
Future Development ◽  
Learning Models ◽  
The Future ◽  
Loss Reserving ◽  
Recent Developments ◽  
History Of ◽  
Further Development ◽  
Machine Learning Models

The purpose of this paper is to survey recent developments in granular models and machine learning models for loss reserving, and to compare the two families with a view to assessment of their potential for future development. This is best understood against the context of the evolution of these models from their predecessors, and the early sections recount relevant archaeological vignettes from the history of loss reserving. However, the larger part of the paper is concerned with the granular models and machine learning models. Their relative merits are discussed, as are the factors governing the choice between them and the older, more primitive models. Concluding sections briefly consider the possible further development of these models in the future.

scholarly journals Risks Special Issue on “Granular Models and Machine Learning Models”

Risks ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 1 ◽  
Author(s):  
Greg Taylor
Keyword(s):  
Machine Learning ◽  
Learning Models ◽  
Special Issue ◽  
Loss Reserving ◽  
Machine Learning Models

It is probably fair to date loss reserving by means of claim modelling from the late 1960s [...]

scholarly journals Machine Learning Prediction of Incidence of Alzheimer’s Disease Using Large-Scale Administrative Health Data

2019 ◽  
Author(s):  
Ji Hwan Park ◽  
Han Eol Cho ◽  
Jong Hun Kim ◽  
Melanie Wall ◽  
Yaakov Stern ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Risk Prediction ◽  
Clinical Features ◽  
Large Scale ◽  
Health Data ◽  
Learning Models ◽  
Administrative Health Data ◽  
History Of ◽  
Machine Learning Models

AbstractNationwide population-based cohort provides a new opportunity to build a completely automated risk prediction model based on individuals’ history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer’s disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N=40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness, and socio-demographics. To define incident AD two operational definitions were considered: “definite AD” with diagnostic codes and dementia medication (n=614) and “probable AD” with only diagnosis (n=2,026). We trained and validated a random forest, support vector machine, and logistic regression to predict incident AD in 1,2,3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on “definite AD” and “probable AD” outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age, and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.

scholarly journals A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4745 ◽  
Author(s):  
Dana-Mihaela Petroșanu ◽  
George Căruțașu ◽  
Nicoleta Luminița Căruțașu ◽  
Alexandru Pîrjan
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Scientific Literature ◽  
The Body ◽  
Learning Models ◽  
Smart Buildings ◽  
Building Management ◽  
Sensor Devices ◽  
Recent Developments ◽  
Machine Learning Models

Lately, many scientists have focused their research on subjects like smart buildings, sensor devices, virtual sensing, buildings management, Internet of Things (IoT), artificial intelligence in the smart buildings sector, improving life quality within smart homes, assessing the occupancy status information, detecting human behavior with a view to assisted living, maintaining environmental health, and preserving natural resources. The main purpose of our review consists of surveying the current state of the art regarding the recent developments in integrating supervised and unsupervised machine learning models with sensor devices in the smart building sector with a view to attaining enhanced sensing, energy efficiency and optimal building management. We have devised the research methodology with a view to identifying, filtering, categorizing, and analyzing the most important and relevant scientific articles regarding the targeted topic. To this end, we have used reliable sources of scientific information, namely the Elsevier Scopus and the Clarivate Analytics Web of Science international databases, in order to assess the interest regarding the above-mentioned topic within the scientific literature. After processing the obtained papers, we finally obtained, on the basis of our devised methodology, a reliable, eloquent and representative pool of 146 papers scientific works that would be useful for developing our survey. Our approach provides a useful up-to-date overview for researchers from different fields, which can be helpful when submitting project proposals or when studying complex topics such those reviewed in this paper. Meanwhile, the current study offers scientists the possibility of identifying future research directions that have not yet been addressed in the scientific literature or improving the existing approaches based on the body of knowledge. Moreover, the conducted review creates the premises for identifying in the scientific literature the main purposes for integrating Machine Learning techniques with sensing devices in smart environments, as well as purposes that have not been investigated yet.

scholarly journals Vec2SPARQL: integrating SPARQL queries and knowledge graph embeddings

2018 ◽  
Author(s):  
Maxat Kulmanov ◽  
Senay Kafkas ◽  
Andreas Karwath ◽  
Alexander Malic ◽  
Georgios V Gkoutos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Research Group ◽  
Query Language ◽  
Structured Data ◽  
Learning Models ◽  
Multiple Endpoints ◽  
Link Type ◽  
Recent Developments ◽  
Description Framework ◽  
Machine Learning Models

AbstractRecent developments in machine learning have lead to a rise of large number of methods for extracting features from structured data. The features are represented as a vectors and may encode for some semantic aspects of data. They can be used in a machine learning models for different tasks or to compute similarities between the entities of the data. SPARQL is a query language for structured data originally developed for querying Resource Description Framework (RDF) data. It has been in use for over a decade as a standardized NoSQL query language. Many different tools have been developed to enable data sharing with SPARQL. For example, SPARQL endpoints make your data interoperable and available to the world. SPARQL queries can be executed across multiple endpoints. We have developed a Vec2SPARQL, which is a general framework for integrating structured data and their vector space representations. Vec2SPARQL allows jointly querying vector functions such as computing similarities (cosine, correlations) or classifications with machine learning models within a single SPARQL query. We demonstrate applications of our approach for biomedical and clinical use cases. Our source code is freely available at https://github.com/bio-ontology-research-group/vec2sparql and we make a Vec2SPARQL endpoint available at http://sparql.bio2vec.net/.

scholarly journals On the genealogy of machine learning datasets: A critical history of ImageNet

2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110359
Author(s):  
Emily Denton ◽  
Alex Hanna ◽  
Razvan Amironesei ◽  
Andrew Smart ◽  
Hilary Nicole
Keyword(s):  
Machine Learning ◽  
Critical Discourse Analysis ◽  
Critical Discourse ◽  
Learning Models ◽  
Large Computer ◽  
Artificial Intelligence Research ◽  
Critical History ◽  
History Of ◽  
Ongoing Development ◽  
Machine Learning Models

In response to growing concerns of bias, discrimination, and unfairness perpetuated by algorithmic systems, the datasets used to train and evaluate machine learning models have come under increased scrutiny. Many of these examinations have focused on the contents of machine learning datasets, finding glaring underrepresentation of minoritized groups. In contrast, relatively little work has been done to examine the norms, values, and assumptions embedded in these datasets. In this work, we conceptualize machine learning datasets as a type of informational infrastructure, and motivate a genealogy as method in examining the histories and modes of constitution at play in their creation. We present a critical history of ImageNet as an exemplar, utilizing critical discourse analysis of major texts around ImageNet’s creation and impact. We find that assumptions around ImageNet and other large computer vision datasets more generally rely on three themes: the aggregation and accumulation of more data, the computational construction of meaning, and making certain types of data labor invisible. By tracing the discourses that surround this influential benchmark, we contribute to the ongoing development of the standards and norms around data development in machine learning and artificial intelligence research.

scholarly journals Machine-Learning Models: The Future of Predictive Analytics in Neurosurgery

Neurosurgery ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. E3-E4 ◽  
Author(s):  
G Damian Brusko ◽  
John Paul G Kolcun ◽  
Michael Y Wang
Keyword(s):  
Machine Learning ◽  
Predictive Analytics ◽  
Learning Models ◽  
The Future ◽  
Machine Learning Models

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

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