Medical decision support systems

Expertensysteme zur Entscheidungsfindung sind ein Trend in der humanmedizinischen Forschung. Derartige Systeme werden in Zukunft die Arbeit von Medizinern prägen. In dieser Fachstudie werden zunächst einige solcher Systeme, die sich zurzeit in der Entwicklung finden, vorgestellt. Zusätzlich werden Erwartungen und Einschätzungen von Medizinern zu diesen Softwaresystemen dargestellt. Abschließend werden die rechtlichen Anforderungen an ein solches Expertensystem, dessen Hersteller und den Entwicklungsprozess durch den Gesetzgeber präsentiert.The trend in human medicine research is towards decision support systems. Such systems will shape the work of physicians in future. In this subject studie, some of those systems that are currently in development will be presented. In addition, the physicians' expectations and assessments of these software systems are shown. Finally, the legal requirements of a decision support systems, its manufacturer and the development process are depicted

Predicting Pathology in Medical Decision Support Systems in Endoscopy of the Gastrointestinal Tract

Since medical endoscopy is a minimally invasive and relatively painless procedure, allowing to inspect the inner cavities of the human body, endoscopes play an important role in modern medicine. In medica

Особливості проектування систем підтримки лікувальних рішень

Дана стаття висвітлює етапи моделювання, визначення методу та механізму проектування лікувальних систем прийняття рішень, що дає змогу підвищити ефективність виконання поставлених завдань при розробці систем такого типу.Данная статья освещает этапы моделирования, определения метода и механизма проектирования лечебных систем принятия решений, что позволяет повысить эффективность выполнения поставленных задач при разработке систем такого типа.This article highlights the stages of modeling, method identification and mechanism designing of medical decision support systems, which allows increasing the efficiency to perform the tasks in the systems development of this type

Development, Implementation and Evaluation of Medical Decision Support Systems Based on Mortality Prediction Algorithms from an Operations Research Perspective.

Wide implementation of electronic health record systems provides rich data for personalized medicine. One topic of great interest is to develop methods to assist physicians in prognosis for example mortality. While many studies have reported on various new prediction models and algorithms there is relatively little literature on if and how these new prediction methods translate into actual benefits. My dissertation consists of three theses that aims at filling this gap between prognostic predictions and clinical decisions in end-of-life care and intensive care settings. In the first thesis, we develop an approach to using temporal trends in physiologic data as an input into mortality prediction models. The approach uses penalized b-spline smoothing and functional PCA to summarize time series of patient data. we apply the methodology in two settings to demonstrate the value of using the shapes of health data time series as a predictor of patient prognosis. The first application a mortality predictor for advanced cancer patients that can help oncologists decide which patients should stop aggressive treatments and switch to palliative care such as that provided in hospice. The second one is a real-time near term mortality predictor for MICU patients that can work as an early alarm system to guide timely interventions. In the second thesis, we investigate the integration of a prediction algorithm with physician decision making, focusing on the advanced cancer patient setting. We design a retrospective study to compare prognoses made by doctors and those that would be recommended by the IMPAC algorithm developed in Chapter 1. We used the doctor\u27s discharge decision as a proxy of what they predict the patient as dying in 90 days and show that doctor\u27s predictions tend to very conservative. Although IMPAC on its own does not perform better than doctors in terms of precision and recall, we find that IMPAC and doctors identify significantly different group of positive cases. IMPAC and doctors are also good at identifying very different groups of patients in terms of survival time. We propose a new way to augment decisions of doctors with IMPAC. At the same recall, the augment method identifies 43\% more patients close to death than the doctors do. We also estimate potential hospitalizations and hospital length of stays avoided if the doctors use augmented procedure instead of acting on their own beliefs. In the third thesis, we look at the integration of a prediction algorithm with physician decision making, focusing on the ICU setting. We use a POMDP framework to evaluate how decision support systems based on ICU mortality predictions can help physicians allocate time to inspect the patients at highest risk of death. We assume physicians have limited time and seek to optimally allocate it to patients in order to minimize their mortality rate. Physicians can do Bayesian updates on observations of patient health state. A prediction algorithm can augment this process by sending alerts to physicians. We represent the algorithm by an arbitrary point on an ROC curve representing a particular alert threshold. We study two approaches to using the algorithm input: (1) Belief based policy (BBP) that integrates algorithm outputs using Bayesian updating; (2) Alarm triggered policy (ATP) where the physician responds only to the algorithm without updating, and compare them to benchmarks that do not rely on the algorithm at all. By running simulations, we explore how the accuracy of predictions can translate into lower mortality rates

Digital twin information technology for biomedical data complex representation and processing

The paper presents an information technology of digital twin for implementation in healthcare, in particular in e-health and m-health applications. The primary objective of this research is to develop a concept of digital twin information technology for medical decision support systems. The second objective is to analyse various medical data formats and to develop an approach to synchronization of multimodal medical data. The approach proposed in the paper will enable aggregation of multimodal data sequences obtained from a wide range of medical diagnostic equipment with the purpose of a patient’s digital twin creation. The paper presents an analysis of data synchronization possibility and data representation formats for both single-channel and multi-channel biological signals, results of such investigations as blood tests, ultrasound research, magnetic resonance imaging etc.Digital twin technology will enable development of a new generation of medical decision support systems. A digital twin of a patient is a synchronized and aggregated multimodal data set obtained from a wide range of diagnostic medical equipment which is continuously updated and based on a personalized semantic modal of a patient. Since data are obtained from different medical devices and tools in various formats which directly do not fit for data synchronization and aggregation, the format of a file-wrapper that enables storing time characteristics of medical investigations (time stamps) in an evident form. It allows us to simplify a procedure of multimodal data aggregation while creating and continuous updating the digital twin of a patient. The process of digital twin forming includes the following stages: receiving of original data files in a device format (sonographic device, MRI scanner, electrocardiograph etc.), analysis of data and their time stamps, transformation of the original file to the format of a file-wrapper, data synchronization and aggregation, representation of multimodal data in a digital twin format for further storing and processing.The paper presents an information technology of digital twin for implementation in healthcare, in particular in e-health and m-health applications. The primary objective of this research is to develop a concept of digital twin information technology for medical decision support systems. The second objective is to analyse various medical data formats and to develop an approach to synchronization of multimodal medical data. The approach proposed in the paper will enable aggregation of multimodal data sequences obtained from a wide range of medical diagnostic equipment with the purpose of a patient’s digital twin creation. The paper presents an analysis of data synchronization possibility and data representation formats for both single-channel and multi-channel biological signals, results of such investigations as blood tests, ultrasound research, magnetic resonance imaging etc.Digital twin technology will enable development of a new generation of medical decision support systems. A digital twin of a patient is a synchronized and aggregated multimodal data set obtained from a wide range of diagnostic medical equipment which is continuously updated and based on a personalized semantic modal of a patient. Since data are obtained from different medical devices and tools in various formats which directly do not fit for data synchronization and aggregation, the format of a file-wrapper that enables storing time characteristics of medical investigations (time stamps) in an evident form. It allows us to simplify a procedure of multimodal data aggregation while creating and continuous updating the digital twin of a patient. The process of digital twin forming includes the following stages: receiving of original data files in a device format (sonographic device, MRI scanner, electrocardiograph etc.), analysis of data and their time stamps, transformation of the original file to the format of a file-wrapper, data synchronization and aggregation, representation of multimodal data in a digital twin format for further storing and processing.Keywords: digital twin, multimodal data, data synchronization

System Dynamics Simulation Model for Cardiovascular Heart Disease Risk Factors - Smoking and Alcohol Intake

Detecting diseases at early stage can help to overcome and treat them accurately. Identifying the appropriate treatment depends on the method that is used in diagnosing the diseases. The incidence of cardiovascular heart disease (CVD) has been increasing steadily and so too its associated mortality. System Dynamics is appropriate methodology for Modelling and Simulation. The Expert knowledge about risk factors for CVD was elicited through interview and literature search. Two CVD risk factors Smoking and Alcohol Intake were analyzed by the proposed decision support system developed with System Dynamics Simulation software (iThink V9.0.2 ),used for the design, implementation and evaluation of the system. The proposed framework would be particularly useful for researchers in the field but also for medical practitioners and developers of medical decision support systems

Medical data classification using similarity measure of fuzzy soft set based distance measure

Medical data classification plays a crucial role in many medical imaging applications by automating or facilitating the delineation of medical images. A considerable amount of literature has been published on medical images classification based on data mining techniques to develop intelligent medical decision support systems to help the physicians. This paper assesses the performance of a new classification algorithm using similarity measure fuzzy soft set based distance based for numerical medical datasets. The proposed modelling comprises of five phases explicitly: data acquisition, data pre-processing, data partitioning, classification using FussCyier and performance evaluation. The proposed classifier FussCyier is evaluated on five performance matrices’: accuracy, precision, recall, F-Micro and computational time. Experimental results indicate that the proposed classifier performed comparatively better with existing fuzzy soft classifiers

Medical Data Classification Using Similarity Measure of Fuzzy Soft Set Based Distance Measure

Medical data classification plays a crucial role in many medical imaging applications by automating or facilitating the delineation of medical images. A considerable amount of literature has been published on medical images classification based on data mining techniques to develop intelligent medical decision support systems to help the physicians. This paper assesses the performance of a new classification algorithm using similarity measure fuzzy soft set based distance based for numerical medical datasets. The proposed modelling comprises of five phases explicitly: data acquisition, data pre-processing, data partitioning, classification using FussCyier and performance evaluation. The proposed classifier FussCyier is evaluated on five performance matrices’: accuracy, precision, recall, F-Micro and computational time. Experimental results indicate that the proposed classifier performed comparatively better with existing fuzzy soft classifiers