Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

https://www.ahajournals.org/doi/pdf/10.1161/CIR.0000000000000549

2017 AHA/ACC/HRS Guideline for Management of Patients With Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society

Background Despite increasing emphasis on the role of clinical decision-support systems (CDSSs) for improving care and reducing costs, evidence to support widespread use is lacking. Purpose To evaluate the effect of CDSSs on clinical outcomes, health care processes, workload and efficiency, patient satisfaction, cost, and provider use and implementation. Data sources MEDLINE, CINAHL, PsycINFO, and Web of Science through January 2011. Study selection Investigators independently screened reports to identify randomized trials published in English of electronic CDSSs that were implemented in clinical settings; used by providers to aid decision making at the point of care; and reported clinical, health care process, workload, relationship-centered, economic, or provider use outcomes. Data extraction Investigators extracted data about study design, participant characteristics, interventions, outcomes, and quality. Data synthesis 148 randomized, controlled trials were included. A total of 128 (86%) assessed health care process measures, 29 (20%) assessed clinical outcomes, and 22 (15%) measured costs. Both commercially and locally developed CDSSs improved health care process measures related to performing preventive services (n= 25; odds ratio [OR], 1.42 [95% CI, 1.27 to 1.58]), ordering clinical studies (n= 20; OR, 1.72 [CI, 1.47 to 2.00]), and prescribing therapies (n= 46; OR, 1.57 [CI, 1.35 to 1.82]). Few studies measured potential unintended consequences or adverse effects. Limitations Studies were heterogeneous in interventions, populations, settings, and outcomes. Publication bias and selective reporting cannot be excluded. Conclusion Both commercially and locally developed CDSSs are effective at improving health care process measures across diverse settings, but evidence for clinical, economic, workload, and efficiency outcomes remains sparse. This review expands knowledge in the field by demonstrating the benefits of CDSSs outside of experienced academic centers. Primary funding source Agency for Healthcare Research and Quality.

Effect of Clinical Decision-Support Systems: A Systematic Review

Evidence to support widespread use of clinical decision-support systems (CDSSs) is lacking. This review summarizes trials of CDSSs implemented in clinical settings to aid decision making at the poi...

Effect of Clinical Decision-Support Systems

The nephrotic syndrome

This paper presents a new approach to fuzzy rule-based modeling of nonlinear systems from numerical data. The novelty of the approach lies in the way of input partitioning and in the syntax of the rules. This paper introduces interpretable relational antecedents that incorporate local linear interactions between the input variables into the inference process. This modification improves the approximation quality and allows for limiting the number of rules. Additionally, the resulting linguistic description better captures the system characteristics by exposing the interactions between the input variables.

http://ci.louisville.edu/zurada/publications/gaweda.tfs.2003.pdf

Data-driven linguistic modeling using relational fuzzy rules

Background and objectives: The erythropoietic response in hemodialysis patients depends on several physiologic factors. Most epidemiologic studies include the effect of these factors by representing them as confounders. This study tested the hypothesis that iron stores, inflammation, dialysis adequacy, nutritional status, and hyperparathyroidism act as nonlinear effect modifiers of the erythropoietic response and quantified the magnitude of those effects over clinically relevant ranges. Design, setting, participants, & measurements: The following retrospective data from 209 hemodialysis patients receiving Epoetin alfa (Epo) were collected: monthly: predialysis hemoglobin (Hgb), transferrin saturation, serum albumin, dialysis adequacy (Kt/V); quarterly: predialysis serum ferritin and intact parathyroid hormone over a period of 13 to 69 months. The study analyzed the dynamic relationship between hemoglobin and Epo, considering nonlinear effect modification by ferritin, transferrin saturation, Kt/V, albumin, and parathyroid hormone individually. Results: Maximum Hgb response to Epo was achieved for serum ferritin between 350 and 500 ng/ml, transferrin saturation greater than 30%, Kt/V greater than 1.4, and albumin greater than 3.8 g/dl. Hgb sensitivity to Epo decreases by about 30% as parathyroid hormone increases from 0 through 1000 pg/ml. Conclusions: Serum ferritin, transferrin saturation, Kt/V, serum albumin, and intact parathyroid hormone are markers of nonlinear effect modification of the erythropoietic response in hemodialysis patients.

/pdf/iron-inflammation-dialysis-adequacy-nutritional-status-and-2l8gwq381v.pdf

Iron, Inflammation, Dialysis Adequacy, Nutritional Status, and Hyperparathyroidism Modify Erythropoietic Response

https://www.ajkd.org/article/S0272-6386(07)01353-4/pdf

Model Predictive Control of Erythropoietin Administration in the Anemia of ESRD

http://ci.louisville.edu/kerem/evraklar/NNpaper.pdf

2005 Special Issue: Individualization of pharmacological anemia management using reinforcement learning

This work presents a pharmacodynamic population analysis in chronic renal failure patients using Artificial Neural Networks (ANNs). In pursuit of an effective and cost-efficient strategy for drug delivery in patients with renal failure, two different types of ANN are applied to perform drug dose-effect modeling and their performance compared. Applied in a clinical environment, such models will allow for prediction of patient response to the drug at the effect site and, subsequently, for adjusting the dosing regimen.

Adam E. Gaweda

Papers

Data-driven linguistic modeling using relational fuzzy rules

Iron, Inflammation, Dialysis Adequacy, Nutritional Status, and Hyperparathyroidism Modify Erythropoietic Response

Model Predictive Control of Erythropoietin Administration in the Anemia of ESRD

2005 Special Issue: Individualization of pharmacological anemia management using reinforcement learning

Pharmacodynamic population analysis in chronic renal failure using artificial neural networks: a comparative study