Decision Making: A Computer-Science and Information-Technology Viewpoint

We address the phenomenon of decision making from the viewpoint of computer science and information technology. The basic question from this viewpoint is: what can the computer offer to decision makers and how it can support their work? Therefore, the main issue is to provide support to people who make complex decisions. In this article, we first present the taxonomy of disciplines that are concerned with methodological and operational aspects of decision support. At the main level, we distinguish between decision sciences, which are concerned with human decision making, and decision systems, which address computer decision making. This is followed by basic definitions related to decision processes and their components. We also describe properties that characterise different classes of decision problems. In the main part of the article, we present three prevailing approaches to decision support and give illustrative examples of their application: decision analysis, operational research, and decision support systems. Finally, we make a short overview of the area of decision systems and its achievements.decision making, decision sciences, decision support, decision analysis, decision systems

Estimation of minimum sample size for identification of the most important features: a case study providing a qualitative B2B sales data set

An important task in machine learning is to reduce data set dimensionality, which in turn contributes to reducing computational load and data collection costs, while improving human understanding and interpretation of models. We introduce an operational guideline for determining the minimum number of instances sufficient to identify correct ranks of features with the highest impact. We conduct tests based on qualitative B2B sales forecasting data. The results show that a relatively small instance subset is sufficient for identifying the most important features when rank is not important

Decision Making: A Computer-Science and Information-Technology Viewpoint

We address the phenomenon of decision making from the viewpoint of computer science and information technology. The basic question from this viewpoint is: what can the computer offer to decision makers and how it can support their work? Therefore, the main issue is to provide support to people who make complex decisions. In this article, we first present the taxonomy of disciplines that are concerned with methodological and operational aspects of decision support. At the main level, we distinguish between decision sciences, which are concerned with human decision making, and decision systems, which address computer decision making. This is followed by basic definitions related to decision processes and their components. We also describe properties that characterise different classes of decision problems. In the main part of the article, we present three prevailing approaches to decision support and give illustrative examples of their application: decision analysis, operational research, and decision support systems. Finally, we make a short overview of the area of decision systems and its achievements

Number of Instances for Reliable Feature Ranking in a Given Problem

Background: In practical use of machine learning models, users may add new features to an existing classification model, reflecting their (changed) empirical understanding of a field. New features potentially increase classification accuracy of the model or improve its interpretability. Objectives: We have introduced a guideline for determination of the sample size needed to reliably estimate the impact of a new feature. Methods/Approach: Our approach is based on the feature evaluation measure ReliefF and the bootstrap-based estimation of confidence intervals for feature ranks. Results: We test our approach using real world qualitative business-to-business sales forecasting data and two UCI data sets, one with missing values. The results show that new features with a high or a low rank can be detected using a relatively small number of instances, but features ranked near the border of useful features need larger samples to determine their impact. Conclusions: A combination of the feature evaluation measure ReliefF and the bootstrap-based estimation of confidence intervals can be used to reliably estimate the impact of a new feature in a given problem

Ranking of business process simulation tools with DEX/QQ hierarchical decision model

The omnipresent need for optimisation requires constant improvements of companies’ business processes (BPs). Minimising the risk of inappropriate BP being implemented is usually performed by simulating the newly developed BP under various initial conditions and “what-if” scenarios. An effectual business process simulations software (BPSS) is a prerequisite for accurate analysis of an BP. Characterisation of an BPSS tool is a challenging task due to the complex selection criteria that includes quality of visual aspects, simulation capabilities, statistical facilities, quality reporting etc. Under such circumstances, making an optimal decision is challenging. Therefore, various decision support models are employed aiding the BPSS tool selection. The currently established decision support models are either proprietary or comprise only a limited subset of criteria, which affects their accuracy. Addressing this issue, this paper proposes a new hierarchical decision support model for ranking of BPSS based on their technical characteristics by employing DEX and qualitative to quantitative (QQ) methodology. Consequently, the decision expert feeds the required information in a systematic and user friendly manner. There are three significant contributions of the proposed approach. Firstly, the proposed hierarchical model is easily extendible for adding new criteria in the hierarchical structure. Secondly, a fully operational decision support system (DSS) tool that implements the proposed hierarchical model is presented. Finally, the effectiveness of the proposed hierarchical model is assessed by comparing the resulting rankings of BPSS with respect to currently available results

Use of supporting software tool for decision-making during low-probability severe accident management at nuclear power plants

In the project NARSIS – New Approach to Reactor Safety ImprovementS – possible advances in safety assessment of nuclear power plants (NPPs) were considered, which also included possible improvements in the field of management of low probability accident scenarios. As a part of it, a supporting software tool for making decisions under severe accident management was developed. The mentioned tool, named Severa, is a prototype demonstration-level decision supporting system, aimed for the use by the technical support center (TSC) while managing a severe accident, or for the training purposes. Severa interprets, stores and monitors key physical measurements during accident sequence progression. It assesses the current state of physical barriers: core, reactor coolant system, reactor pressure vessel and containment. The tool gives predictions regarding accident progression in the case that no action is taken by the TSC. It provides a list of possible recovery strategies and courses of action. The applicability and feasibility of possible action courses in the given situation are addressed. For each action course, Severa assesses consequences in terms of probability of the containment failure and estimated time window for failure. At the end, Severa evaluates and ranks the feasible actions, providing recommendations for the TSC. The verification and validation of Severa has been performed in the project and is also described in this paper. Although largely simplified in its current state, Severa successfully demonstrated its potential for supporting accident management and pointed toward the next steps needed with regard to further advancements in this fiel

Use of supporting software tool for decision-making during low-probability severe accident management at nuclear power plants

In the project NARSIS – New Approach to Reactor Safety ImprovementS – possible advances in safety assessment of nuclear power plants (NPPs) were considered, which also included possible improvements in the field of management of low probability accident scenarios. As a part of it, a supporting software tool for making decisions under severe accident management was developed. The mentioned tool, named Severa, is a prototype demonstration-level decision supporting system, aimed for the use by the technical support center (TSC) while managing a severe accident, or for the training purposes. Severa interprets, stores and monitors key physical measurements during accident sequence progression. It assesses the current state of physical barriers: core, reactor coolant system, reactor pressure vessel and containment. The tool gives predictions regarding accident progression in the case that no action is taken by the TSC. It provides a list of possible recovery strategies and courses of action. The applicability and feasibility of possible action courses in the given situation are addressed. For each action course, Severa assesses consequences in terms of probability of the containment failure and estimated time window for failure. At the end, Severa evaluates and ranks the feasible actions, providing recommendations for the TSC. The verification and validation of Severa has been performed in the project and is also described in this paper. Although largely simplified in its current state, Severa successfully demonstrated its potential for supporting accident management and pointed toward the next steps needed with regard to further advancements in this fiel

Primarni retroperitonealni sarkomi: preživetje bolnikov, zdravljenih na Onkološkem inštitutu Ljubljana, referenčnem centru za sarkome

Izhodišča: Retroperitonealni sarkomi so izredno redki, zato naj zdravljenje bolnikov z retroperitonealnimi sarkomi poteka v referenčnem centru. Temeljno zdravljenje je kirurško. Priporočen tip operacije je kompartment resekcija. Metode: Onkološki inštitut Ljubljana je edini referenčni center za sarkome v Sloveniji. V raziskavo so bili vključeni bolniki s primarnim lokaliziranim retroperitonealnim sarkomom, zdravljeni pri nas v obdobju od januarja 1999 do junija 2020. Opredelili smo preživetje, kakovost kirurškega zdravljenja in zaplete. Rezultati: Vključenih je bilo 100 bolnikov. Srednja starost je bila 62 let. Srednja velikost tumorja je bila 21,5 cm. Najpogostejši histološki podtip je bil dediferenciran liposarkom (39 %). Kompartment resekcija je bila opravljena v 24 %, multivisceralna resekcija pa v 25 %. Zaplete po posegu je imelo po klasifikaciji Clavien-Dindo stopnje 3a ali višje 29 % bolnikov, pri 58,6 % (17/29) je bila potrebna ponovna operacija. Zgodnja in pozna smrtnost po operaciji sta bili 3 % in 5 %. Srednji čas sledenja je bil 55,1 mesecev. 5-letno celokupno preživetje je bilo 67,8 %. Kumulativna verjetnost za lokalno ponovitev bolezni po 5 letih je bila 16,9 %, za oddaljene zasevke pa 21,4 %. Ocena ASA in izguba krvi med operacijo sta bila neodvisna napovedna dejavnika celokupnega preživetja. Zaključek: Retroperitonealni sarkomi sodijo med redke vrste raka. Naši rezultati zdravljenja bolnikov z retroperitonealnimi sarkomi so zelo dobri in primerljivi z rezultati drugih referenčnih centrov iz tujine. Potrjujejo tudi ključno vlogo referenčnega centra pri obravnavi in zdravljenju teh bolnikov

A personal decision support system for heart failure management (HeartMan) : study protocol of the HeartMan randomized controlled trial

Background: Heart failure (HF) is a highly prevalent chronic disease, for which there is no cure available. Therefore, improving disease management is crucial, with mobile health (mHealth) being a promising technology. The aim of the HeartMan study is to evaluate the effect of a personal mHealth system on top of standard care on disease management and health-related quality of life (HRQoL) in HF. Methods: HeartMan is a randomized controlled 1:2 (control: intervention) proof-of-concept trial, which will enrol 120 stable ambulatory HF patients with reduced ejection fraction across two European countries. Participants in the intervention group are equipped with a multi-monitoring health platform with the HeartMan wristband sensor as the main component. HeartMan provides guidance through a decision support system on four domains of disease management (exercise, nutrition, medication adherence and mental support), adapted to the patient's medical and psychological profile. The primary endpoint of the study is improvement in self-care and HRQoL after a six-months intervention. Secondary endpoints are the effects of HeartMan on: behavioural outcomes, illness perception, clinical outcomes and mental state. Discussion: HeartMan is technologically the most innovative HF self-management support system to date. This trial will provide evidence whether modern mHealth technology, when used to its full extent, can improve HRQoL in HF