Rules-based Algorithm Research Articles

Implementation of energy management strategy of hybrid power source for electrical vehicle

Managing the energy is one of the most important issues for the efficiency of vehicular applications using hybrid electric power systems. This paper deals with an analysis of an energy management for hybrid power system, which consists of fuel cell, ultra-capacitor and battery and dedicated to electrical vehicles. Thus, an energy management method is applied in order to split the energy flow between the three used sources. This strategy is based on a combination of three control methods; a fuzzy logic control, a flatness control and rule-based algorithm. For the fuzzy logic, the main idea is inspired from rule-based algorithms except that it is more flexible and less aggressive. The second technic is an innovative control using the flatness property for a Fuel cell/ supercapacitor/ Battery hybrid power system. For the rules based algorithm, it uses a supercapacitor state-of-charge control principle. Then, an experimental validation is done for the energy management method to show the efficiency of the proposed control strategy in term of the well share of the energy between sources to satisfy the load demand. A dSpace real-time card is used for management algorithm implementation and data acquisition. The obtained experimental results prove the efficiency and the validity of the proposed supervisor.

Automated Assessment of Existing Patient's Revised Cardiac Risk Index Using Algorithmic Software.

Previous work in the field of medical informatics has shown that rules-based algorithms can be created to identify patients with various medical conditions; however, these techniques have not been compared to actual clinician notes nor has the ability to predict complications been tested. We hypothesize that a rules-based algorithm can successfully identify patients with the diseases in the Revised Cardiac Risk Index (RCRI). Patients undergoing surgery at the University of California, Los Angeles Health System between April 1, 2013 and July 1, 2016 and who had at least 2 previous office visits were included. For each disease in the RCRI except renal failure-congestive heart failure, ischemic heart disease, cerebrovascular disease, and diabetes mellitus-diagnosis algorithms were created based on diagnostic and standard clinical treatment criteria. For each disease state, the prevalence of the disease as determined by the algorithm, International Classification of Disease (ICD) code, and anesthesiologist's preoperative note were determined. Additionally, 400 American Society of Anesthesiologists classes III and IV cases were randomly chosen for manual review by an anesthesiologist. The sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve were determined using the manual review as a gold standard. Last, the ability of the RCRI as calculated by each of the methods to predict in-hospital mortality was determined, and the time necessary to run the algorithms was calculated. A total of 64,151 patients met inclusion criteria for the study. In general, the incidence of definite or likely disease determined by the algorithms was higher than that detected by the anesthesiologist. Additionally, in all disease states, the prevalence of disease was always lowest for the ICD codes, followed by the preoperative note, followed by the algorithms. In the subset of patients for whom the records were manually reviewed, the algorithms were generally the most sensitive and the ICD codes the most specific. When computing the modified RCRI using each of the methods, the modified RCRI from the algorithms predicted in-hospital mortality with an area under the receiver operating characteristic curve of 0.70 (0.67-0.73), which compared to 0.70 (0.67-0.72) for ICD codes and 0.64 (0.61-0.67) for the preoperative note. On average, the algorithms took 12.64 ± 1.20 minutes to run on 1.4 million patients. Rules-based algorithms for disease in the RCRI can be created that perform with a similar discriminative ability as compared to physician notes and ICD codes but with significantly increased economies of scale.

A Rules-Based Algorithm to Prioritize Poor Prognosis Cancer Patients in Need of Advance Care Planning.

Accurate understanding of the prognosis of an advanced cancer patient can lead to decreased aggressive care at the end of life and earlier hospice enrollment. Our goal was to determine the association between high-risk clinical events identified by a simple, rules-based algorithm and decreased overall survival, to target poor prognosis cancer patients who would urgently benefit from advanced care planning. A retrospective analysis was performed on outpatient oncology patients with an index visit from April 1, 2015, through June 30, 2015. We examined a three-month window for "high-risk events," defined as (1) change in chemotherapy, (2) emergency department (ED) visit, and (3) hospitalization. Patients were followed until January 31, 2017. A total of 219 patients receiving palliative chemotherapy at the University of Chicago Medicine with a prognosis of ≤12 months were included. The main outcome was overall survival, and each "high-risk event" was treated as a time-varying covariate in a Cox proportional hazards regression model to calculate a hazard ratio (HR) of death. A change in chemotherapy regimen, ED visit, hospitalization, and at least one high-risk event occurred in 54% (118/219), 10% (22/219), 26% (57/219), and 67% (146/219) of patients, respectively. The adjusted HR of death for patients with a high-risk event was 1.72 (95% confidence interval [CI] 1.19-2.46, p = 0.003), with hospitalization reaching significance (HR 2.74, 95% CI 1.84-4.09, p < 0.001). The rules-based algorithm identified those with the greatest risk of death among a poor prognosis patient group. Implementation of this algorithm in the electronic health record can identify patients with increased urgency to address goals of care.

A rules-based algorithm to identify patients who would benefit from re-addressing advance care planning.

4 Background: Advance care planning (ACP) should be initiated early and readdressed often for cancer patients. We hypothesize that a rules-based algorithm can predict decreased overall survival, and can be used to target patients who would benefit from readdressing ACP. Methods: We performed a retrospective analysis of 219 patients receiving palliative chemotherapy with leukemia, cholangiocarcinoma, esophageal, gastric, pancreatic, lung or urothelial cancer at the University of Chicago Medicine. Patients were included if they had an index outpatient oncology visit from April 1, 2015 through June 30, 2015. We examined a three-month window from the index visit for a “high-risk event,” defined as: 1. change in chemotherapy 2. emergency department visit 3. hospitalization. Patients were followed from index visit until date of death or last clinical encounter as of January 31, 2017. Each “high-risk event” was treated as a time-varying covariate in a Cox proportional hazards regression model to calculate a hazard ratio of death compared to those without an event. Results: Sixty-seven percent of patients (146/219) experienced at least one high-risk event. A change in chemotherapy regimen, an ED visit, and a hospitalization occurred in 54% (118/219), 10% (22/219) and 26% (57/219) of patients respectively. The hazard ratio of death for patients with at least one high-risk event when compared to those without was 1.72 (95% CI: 1.19-2.46, p=0.003), when adjusted for age, gender, race, marital status, disease type, ECOG, and Charlson score. Inpatient admission independently reached significant for hazard of death. (HR 2.74: 95% CI: 1.84-4.09, p&lt;0.001). Conclusions: The rules-based algorithm identified patients with a greater risk of death. Implementation of this algorithm in the electronic medical record can identify patients with increased urgency to readdress goals of care. [Table: see text]

A rules-based algorithm to identify patients who would benefit from re-addressing advanced care planning.

e21510 Background: Advance care planning (ACP) should be initiated early and readdressed often for cancer patients. We hypothesize that a rules-based algorithm based on occurrence of high-risk events can predict decreased overall survival, and can be used to target patients who would benefit from readdressing ACP. Methods: We performed a retrospective analysis of 221 patients receiving palliative chemotherapy with a diagnosis of leukemia, cholangiocarcinoma, esophageal, gastric, pancreatic, lung or urothelial cancer at the University of Chicago Medicine. Patients were included if they had an index outpatient oncology visit from April 1, 2015 through June 30, 2015. Starting at the date of index visit, we examined a three-month window for a “high-risk event,” defined as: 1. change in chemotherapy 2. emergency department visit 3. hospitalization. Patients were followed from index visit until date of death or last clinical encounter as of January 31, 2017. Each “high-risk event” was treated as a time-varying covariate in a Cox proportional hazards regression model to calculate a hazard ratio of death compared to those without an event. Results: Sixty-six percent of patients (146/221) experienced at least one high-risk event over the 3 month time frame. A change in chemotherapy regimen, an ED visit, and a hospitalization occurred in 53% (118/221), 10% (22/221) and 26% (57/221) of patients respectively. The hazard ratio of death for patients with at least one high-risk event when compared to those without was 1.86 (95% CI: 1.26-2.74, p = 0.002), when adjusted for age, gender, and race. Inpatient admission had the highest hazard of death among the high-risk events (HR 2.52: 95% CI: 1.69-3.76, p &lt; 0.001). Conclusions: The rules-based algorithm identified patients with a greater risk of death. Implementation of this algorithm in the electronic medical record can identify patients with increased urgency to readdress goals of care.

FEAsy: A Sketch-Based Tool for Finite Element Analysis

Freehand sketching is an integral part of early design process. Recent years have seen an increased interest in supporting sketching in computer-based design systems. In this paper, we present finite element analysis made easy (FEAsy), a naturalistic environment for static finite element analysis. This tool allows users to transform, simulate, and analyze their finite element models quickly and easily through freehand sketching. A major challenge here is to beautify freehand sketches, and to this extent, we present a domain-independent, multistroke, multiprimitive method which automatically detects and uses the spatial relationships implied in the sketches for beautification. Further, we have also developed a domain-specific rules-based algorithm for recognizing commonly used symbols in finite element analysis (FEA) and a method for identifying different contexts in finite element modeling through combined interpretation of text and geometry. The results of the user study suggest that our proposed algorithms are efficient and robust. Pilot users found the interface to be effective and easy to use.

Evaluation of Machine Learning and Rules-Based Approaches for Predicting Antimicrobial Resistance Profiles in Gram-negative Bacilli from Whole Genome Sequence Data.

The time-to-result for culture-based microorganism recovery and phenotypic antimicrobial susceptibility testing necessitates initial use of empiric (frequently broad-spectrum) antimicrobial therapy. If the empiric therapy is not optimal, this can lead to adverse patient outcomes and contribute to increasing antibiotic resistance in pathogens. New, more rapid technologies are emerging to meet this need. Many of these are based on identifying resistance genes, rather than directly assaying resistance phenotypes, and thus require interpretation to translate the genotype into treatment recommendations. These interpretations, like other parts of clinical diagnostic workflows, are likely to be increasingly automated in the future. We set out to evaluate the two major approaches that could be amenable to automation pipelines: rules-based methods and machine learning methods. The rules-based algorithm makes predictions based upon current, curated knowledge of Enterobacteriaceae resistance genes. The machine-learning algorithm predicts resistance and susceptibility based on a model built from a training set of variably resistant isolates. As our test set, we used whole genome sequence data from 78 clinical Enterobacteriaceae isolates, previously identified to represent a variety of phenotypes, from fully-susceptible to pan-resistant strains for the antibiotics tested. We tested three antibiotic resistance determinant databases for their utility in identifying the complete resistome for each isolate. The predictions of the rules-based and machine learning algorithms for these isolates were compared to results of phenotype-based diagnostics. The rules based and machine-learning predictions achieved agreement with standard-of-care phenotypic diagnostics of 89.0 and 90.3%, respectively, across twelve antibiotic agents from six major antibiotic classes. Several sources of disagreement between the algorithms were identified. Novel variants of known resistance factors and incomplete genome assembly confounded the rules-based algorithm, resulting in predictions based on gene family, rather than on knowledge of the specific variant found. Low-frequency resistance caused errors in the machine-learning algorithm because those genes were not seen or seen infrequently in the test set. We also identified an example of variability in the phenotype-based results that led to disagreement with both genotype-based methods. Genotype-based antimicrobial susceptibility testing shows great promise as a diagnostic tool, and we outline specific research goals to further refine this methodology.

New rules-based algorithm to improve Arabic stemming accuracy

In the recent past, the world has been witnessing a steady increase in the area of natural language processing owing to the spread of the internet. However, attempts and efforts devoted for Arabic language are still limited. By morphological and semantic properties, Arabic is considered a difficult language in the field of automatic processing. From that perspective, many different approaches were attempted to deal with the morphological variation and the agglutination phenomenon while stemming Arabic texts. Formally, stemming and light-stemming are used to remove irrelevant morphological variations from a given word, and extract its original stem or root. This research introduces a complete new rules-based algorithm. This involves precise removal of affixes based on context-sensitive morphological rules and then deduces the root according to a predefined set of rules. Finally, results show that the accuracy of the proposed algorithm is higher than the two well-known Arabic stemmers.

Lipoprotein computational prediction in spirochaetal genomes

Lipoproteins are of great interest in understanding the molecular pathogenesis of spirochaetes. Because spirochaete lipobox sequences exhibit more plasticity than those of other bacteria, application of existing prediction algorithms to emerging sequence data has been problematic. In this paper a novel lipoprotein prediction algorithm is described, designated SpLip, constructed as a hybrid of a lipobox weight matrix approach supplemented by a set of lipoprotein signal peptide rules allowing for conservative amino acid substitutions. Both the weight matrix and the rules are based on a training set of 28 experimentally verified spirochaetal lipoproteins. The performance of the SpLip algorithm was compared to that of the hidden Markov model-based LipoP program and the rules-based algorithm Psort for all predicted protein-coding genes of Leptospira interrogans sv. Copenhageni, L. interrogans sv. Lai, Borrelia burgdorferi, Borrelia garinii, Treponema pallidum and Treponema denticola. Psort sensitivity (13-35 %) was considerably less than that of SpLip (93-100 %) or LipoP (50-84 %) due in part to the requirement of Psort for Ala or Gly at the -1 position, a rule based on E. coli lipoproteins. The percentage of false-positive lipoprotein predictions by the LipoP algorithm (8-30 %) was greater than that of SpLip (0-1 %) or Psort (4-27 %), due in part to the lack of rules in LipoP excluding unprecedented amino acids such as Lys and Arg in the -1 position. This analysis revealed a higher number of predicted spirochaetal lipoproteins than was previously known. The improved performance of the SpLip algorithm provides a more accurate prediction of the complete lipoprotein repertoire of spirochaetes. The hybrid approach of supplementing weight matrix scoring with rules based on knowledge of protein secretion biochemistry may be a general strategy for development of improved prediction algorithms.

Construction, Training and Clinical Validation of An Interpretation System for Genotypic HIV-1 Drug Resistance Based on Fuzzy Rules Revised by Virological Outcomes

To evaluate whether fuzzy operators can be usefully applied to the interpretation of genotypic HIV-1 drug resistance by experts, and to improve the prediction of salvage therapy outcome by adapting interpretation rules of genotypic resistance on the basis of their association with virological response data. We used a clinical dataset of 231 patients failing highly active antiretroviral therapy (HAART) and starting salvage therapy with baseline resistance genotyping and virological outcomes after 3 and 6 months. A set of rules predicting genotypic resistance was initially derived from an expert (ADL). Rules were implemented using a fuzzy logic approach and the virological outcomes dataset used for the training phase. The resulting algorithm was validated using a separate set of 184 selected patients by correlating the resulting predicted activity with observed virological response at 3 months. For comparison, the expert systems from the drug resistance group of the Agence Nationale de Recherches sur le SIDA (ANRS-AC11) and the algorithm from the Stanford's HIV drug resistance database (Stanford HIVdb) were evaluated on the same set. The starting algorithm had a correlation with virological outcomes of R2=0.06 (P=0.0001). After the training phase the correlation with virological outcomes increased to R2=0.19 (P<0.000001). In the validation set of patients, the activity of the salvage regimen predicted by the fuzzy algorithm was the only variable independently predictive of the 3-month viral load change even after adjusting by the activity predicted by the two expert systems and baseline viral load (for each 10% salvage regimen's activity increase, mean HIV RNA change from baseline: -0.27 log10 copies/ml; 95% CI -0.39, -0.15). Using fuzzy operators in a virological outcomes training database to implement a rules-based algorithm for genotypic resistance interpretation, significant improvements of outcomes prediction were obtained. The resulting algorithm showed an independent predictive capability of virological outcomes over that of two rules-based interpretation algorithms made by experts. Although the system was trained and validated on a limited number of cases, the approach deserves further evaluation.