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Abstract

The paper discusses different approaches to building a clinical decision support system based on big data. The authors sought to abstain from any data reduction and apply universal teaching and big data processing methods independent of disease classification standards. The paper assesses and compares the accuracy of recommendations among three options: case-based reasoning, simple single-layer neural network, and probabilistic neural network. Further, the paper substantiates the assumption regarding the most efficient approach to solving the specified problem.

Introduction

Providing support to clinical decision-making is one of the most urgent issues in healthcare automation. It has been repeatedly noted in different articles, reports, and forum discussions both in Russia and abroad that medical information system (MIS) introduction requires a considerable extra effort from users/doctors in the first place—to enter primary data into the system. Naturally, doctors expect practical intelligent outcomes from big clinical data accumulated by modern MISs. Handler et al. present the operating paradigm of fifth generation MISs, referred to as “MIS as Mentor.” Malykh et al. adds one more qualitative characteristic to the above paradigm—“MIS as automated mentor.”

It is advisable to abandon the practice of active user dialogs typical of expert systems, involving requests for data that the system considers missing from the user, and substitute the dialog with an automated nonintrusive algorithm that draws its own logical conclusions and generates recommendations in a completely automated manner based on available data, without involving the user in the process. The user may either accept or ignore the system’s prompts and recommendations; however, they will not provoke rejection in users if delivered automatically without requiring a dialog with the system.

To provide a brief qualitative description of this increasing subjectivity of MISs, we have proposed the new term “active MIS” that emphasizes a certain degree of independence from users or subjectivity of the cyber system. Kohane presents the most “balanced” definition of personalized medicine: “personalized medicine is the practice of clinical decision-making such that the decisions made maximize the outcomes that the patient most cares about and minimize those that the patient fears the most, on the basis of as much knowledge about the individual’s state as is available.” This perception of personal medicine is focused on clinical decision-making and once again exhibits the urgency and importance of scientific research in the area. Therefore, building an automated active mentor-type system that provides recommendations regarding treatment and diagnostic activities to the doctor is an urgent practical task.

Butko and Olshansky and Kotov provide a retrospective overview of approaches to building clinical decision support systems. The applied approaches were restricted in many respects by the abilities of computers at that time. Accordingly, there was no such problem as processing big medical data. Technologies have evolved to the point where big medical data (both on individuals and the population in general) collection and accumulation is finally feasible. At the same time, big data processing and intelligent system learning methods were evolving as well. Along with “deep learning,” the term “deep patient” was coined, meaning the opportunity to extract increasingly more complete, deep, and valuable information about patients from big clinical data using deep learning methods.

Malykh et al. mention the possibility of creating national-scale clinical data banks. Herrett et al. provide an example of a database (DB) containing anonymous medical records on primary healthcare services provided. This DB was created by a joint effort of 674 general practitioners and covers over 11.3 million patients in Great Britain.

Decision-making in hospitals has evolved from being opinion-based to being based on sound scientific evidence. This decision-making is recognized as evidence-based practice. Perpetual publication of new evidence combined with the demands of everyday practice makes it difficult for health professionals to keep up to date.M

A large number of publications are devoted to clinical decision support systems (DSSs), including publications in specialized scientific journals (Artificial Intelligence in Medicine, BMC Medical Informatics and Decision Making, International Journal of Medical Informatics, Medical Decision Making, etc.). The work does not aim to give an overview of different approaches to making of decision support systems, referring readers to the original reviews. We can give a few definitions for decision support system from Wikipedia: “Clinical Decision Support systems link health observations with health knowledge to influence health choices by clinicians for improved health care” and “active knowledge systems, which use two or more items of patient data to generate case-specific advice.” No one doubts the feasibility of such systems and that they have a positive impact on professional practice, patient outcomes, length of hospital stay, and hospital costs. The main problem is to find effective approaches to building such systems.

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.