Academic literature on the topic 'Predictive lead scoring'
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Journal articles on the topic "Predictive lead scoring"
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Jacheć, Wojciech, Anna Polewczyk, Maciej Polewczyk, Andrzej Tomasik, and Andrzej Kutarski. "Transvenous Lead Extraction SAFeTY Score for Risk Stratification and Proper Patient Selection for Removal Procedures Using Mechanical Tools." Journal of Clinical Medicine 9, no. 2 (January 28, 2020): 361. http://dx.doi.org/10.3390/jcm9020361.
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Background: To ensure the safety and efficacy of the increasing number of transvenous lead extractions (TLEs), it is necessary to adequately assess the procedure-related risk. Methods: We analyzed potential clinical and procedural risk factors associated with 2049 TLE procedures. The TLEs were performed between 2006 and 2016 using only simple tools for lead extraction. Logistic regression analysis was used to develop a risk prediction scoring system for TLEs. Results: Multivariate analysis showed that the sum of lead dwell times, anemia, female gender, the number of procedures preceding TLE, and removal of leads implanted in patients under the age of 30 had a significant influence on the occurrence of major complications during a TLE. This information served as a basis for developing a predictive SAFeTY TLE score, where: S = sum of lead dwell times, A = anemia, Fe = female, T = treatment (previous procedures), Y = young patients, and TLE = transvenous lead extraction. In order to facilitate the use of the SAFeTY TLE Score, a simple calculator was constructed. Conclusion: The SAFeTY TLE score is easy to calculate and predicts the potential occurrence of procedure-related major complications. High-risk patients (scoring more than 10 on the SAFeTY TLE scale) must be treated at high-volume centers with surgical backup.
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Wang, Yan, and Zhisheng Wu. "The establishment of a stroke-associated pneumonia predictive scoring system." Neurology Asia 26, no. 3 (September 2021): 485–90. http://dx.doi.org/10.54029/2021xdx.
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Background & Objective: Stroke-associated pneumonia (SAP) is a common complication of ischemic stroke, increasing the length of hospital stay and costs, and affecting prognosis. This study aimed to determine the incidence of SAP, investigate the risk factors that lead to SAP to facilitate a more targeted response to the prevention of SAP. Methods: A retrospective study was performed to analyze the factors that predict SAP in an acute stroke population from a university affiliated hospital in Fujian, China. A SAP risk score table was constructed. Results: A total of 1,016 patients with acute cerebral infarction were enrolled. The incidence of SAP was 13.58%. Multivariate regression analysis found that age, NIHSS, GCS scores, dysphagia, heart failure, creatinine, and proton pump inhibitors (PPIs) use were independently associated with SAP. Based on the data, a SAP risk score table was constructed with age > 75 years -2 points, NIHSS ≥ 16 -2 points, GCS score ≤ 8 -1.5 points, dysphagia - 5 points, heart failure - 1.5 points, creatinine - 1 point, PPIs use - 1.5 points, a total of 14.5 points. The optimal value was 3 points. Conclusions: Age, NIHSS, GCS score, dysphagia, heart failure, creatinine, and PPIs use were predictive of SAP.
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MacCann, Carolyn, Gerald Matthews, Moshe Zeidner, and Richard D. Roberts. "PSYCHOLOGICAL ASSESSMENT OF EMOTIONAL INTELLIGENCE: A REVIEW OF SELF‐REPORT AND PERFORMANCE‐BASED TESTING." International Journal of Organizational Analysis 11, no. 3 (March 1, 2003): 247–74. http://dx.doi.org/10.1108/eb028975.
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This article provides a review and conceptual comparison between self‐report and performance‐based measures of emotional intelligence. Analyses of reliability, psychometric properties, and various forms of validity lead to the conclusion that self‐report techniques measure a dispositional construct, that may have some predictive validity, but which is highly correlated with personality and independent of intelligence. Although seemingly more valid, performance‐based measures have certain limitations, especially when scored with reference to consensual norms, which leads to problems of skew and restriction of range. Scaling procedures may partially ameliorate these scoring weaknesses. Alternative approaches to scoring, such as expert judgement, also suffer problems since the nature of the requisite expertise is unclear. Use of experimental paradigms for studying individual differences in information‐processing may, however, inform expertise. Other difficulties for performance‐based measures include limited predictive and operational validity, restricting practical utility in organizational settings. Further research appears necessary before tests of E1 are suitable for making real‐life decisions about individuals.
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Prelevic, Rade, Miroslav Stojadinovic, Dejan Simic, Aleksandar Spasic, and Nikola Petrovic. "Scoring system development for prediction of extravesical bladder cancer." Vojnosanitetski pregled 71, no. 9 (2014): 851–57. http://dx.doi.org/10.2298/vsp130814040p.
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Background/Aim. Staging of bladder cancer is crucial for optimal management of the disease. However, clinical staging is not perfectly accurate. The aim of this study was to derive a simple scoring system in prediction of pathological advanced muscle-invasive bladder cancer (MIBC). Methods. Logistic regression and bootstrap methods were used to create an integer score for estimating the risk in prediction of pathological advanced MIBC using precystectomy clinicopathological data: demographic, initial transurethral resection (TUR) [grade, stage, multiplicity of tumors, lymphovascular invasion (LVI)], hydronephrosis, abdominal and pelvic CT radiography (size of the tumor, tumor base width), and pathological stage after radical cystectomy (RC). Advanced MIBC in surgical specimen was defined as pT3-4 tumor. Receiving operating characteristic (ROC) curve quantified the area under curve (AUC) as predictive accuracy. Clinical usefulness was assessed by using decision curve analysis. Results. This single-center retrospective study included 233 adult patients with BC undergoing RC at the Military Medical Academy, Belgrade. Organ confined disease was observed in 101 (43.3%) patients, and 132 (56.7%) had advanced MIBC. In multivariable analysis, 3 risk factors most strongly associated with advanced MIBC: grade of initial TUR [odds ratio (OR) = 4.7], LVI (OR = 2), and hydronephrosis (OR = 3.9). The resultant total possible score ranged from 0 to 15, with the cut-off value of > 8 points, the AUC was 0.795, showing good discriminatory ability. The model showed excellent calibration. Decision curve analysis showed a net benefit across all threshold probabilities and clinical usefulness of the model. Conclusion. We developed a unique scoring system which could assist in predicting advanced MIBC in patients before RC. The scoring system showed good performance characteristics and introducing of such a tool into daily clinical decision-making may lead to more appropriate integration of perioperative chemotherapy. Clinical value of this model needs to be further assessed in external validation cohorts.
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Aggarwal, Amulya, Alok V. Mathur, Ram K. Verma, Megha Gupta, and Dheeraj Raj. "Comparison of BISAP and Ranson’s score for predicting severe acute pancreatitis and establish the validity of BISAP score." International Surgery Journal 7, no. 5 (April 23, 2020): 1473. http://dx.doi.org/10.18203/2349-2902.isj20201854.
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Background: Pancreatitis can lead to serious complications with severe morbidity and mortality. So an early, quick and accurate scoring system is necessary to stratify the patients according to their severity so as to enable early initiation of required management and care. Scoring system commonly used have some drawbacks. This study aimed to compare bedside index for severity in acute pancreatitis (BISAP) and Ranson’s score to predict severe acute pancreatitis and establish the validity of a simple and accurate clinical scoring system for stratifying patients.Methods: This is a prospective comparative study on 100 patients diagnosed with acute pancreatitis admitted in department of general surgery. Parameters included in the BISAP and Ranson’s criteria were studied at the time of admission and after 48 hours. Result of these two were compared with that of revised Atlanta classification.Results: As per the BISAP score, the sensitivity and specificity were 95.8 % (95% CI, 76.8-99.8), 94.7 % (95% CI, 86.3-98.3) whereas positive likelihood ratio, negative likelihood ratio 18.21 (95% CI, 6.9-47.44), 0.04 (95% CI, 0.01-0.30) and accuracy was 95 % (95% CI, 88.72%-98.36%). On using Ranson’s score, the sensitivity and specificity were 91.6 (95% CI, 71.5-98.5) and 89.4 (95% CI, 79.8-95) with a positive predictive value 8.71 (95% CI, 4.47-18.96) and negative predictive value of 0.09 (95% CI, 0.02-0.35) and accuracy of 90% (95% CI, 82.38%-95.10%)..Conclusions: BISAP score outperformed Ranson’s score in terms of Sensitivity and specificity of prediction of severe pancreatitis. The authors recommend inclusion of BISAP Scoring system in standard treatment protocol of management of acute pancreatitis.
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Bontempi, Luca, Antonio Curnis, Paolo Della Bella, Manuel Cerini, Andrea Radinovic, Lorenza Inama, Francesco Melillo, et al. "The MB score: a new risk stratification index to predict the need for advanced tools in lead extraction procedures." EP Europace 22, no. 4 (February 22, 2020): 613–21. http://dx.doi.org/10.1093/europace/euaa027.
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Abstract Aims A validated risk stratification schema for transvenous lead extraction (TLE) could improve the management of these procedures. We aimed to derive and validate a scoring system to efficiently predict the need for advanced tools to achieve TLE success. Methods and results Between November 2013 and March 2018, 1960 leads were extracted in 973 consecutive TLE procedures in two national referral sites using a stepwise approach. A procedure was defined as advanced extraction if required the use of powered sheaths and/or snares. The study population was a posteriori 1:1 randomized in derivation and validation cohorts. In the derivation cohort, presence of more than two targeted leads (odds ratio [OR] 1.76, P = 0.049), 3-year-old (OR 3.04, P = 0.001), 5-year-old (OR 3.48, P < 0.001), 10-year-old (OR 3.58, P = 0.008) oldest lead, implantable cardioverter-defibrillator (OR 3.84, P < 0.001), and passive fixation lead (OR 1.91, P = 0.032) were selected by a stepwise procedure and constituted the MB score showing a C-statistics of 0.82. In the validation group, the MB score was significantly associated with the risk of advanced extraction (OR 2.40, 95% confidence interval 2.02-2.86, P < 0.001) and showed an increase in event rate with increasing score. A low value (threshold = 1) ensured 100% sensibility and 100% negative predictive value, while a high value (threshold = 5) allowed a specificity of 92.8% and a positive predictive value of 91.9%. Conclusion In this study, we developed and tested a simple point-based scoring system able to efficiently identify patients at low and high risk of needing advanced tools during TLE procedures.
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Chua, Siang Li, and Wai Leng Chow. "Development of predictive scoring model for risk stratification of no-show at a public hospital specialist outpatient clinic." Proceedings of Singapore Healthcare 28, no. 2 (August 20, 2018): 96–104. http://dx.doi.org/10.1177/2010105818793155.
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Aim: No-shows are patients who miss scheduled specialist outpatient clinic (SOC) appointments. A predictive scoring model for the risk stratification of no-shows was developed to improve the utilisation of resources. Method: The administrative records of new SOC appointments for subsidised patients in 2013 were analysed. Univariate analysis was performed on 16 variables comprising patient demographics, appointment/visit records and historical outpatient records. Multiple logistic regression (MLR) was applied to determine independent risk factors of no-shows. The adjusted parameter estimates from MLR were used to develop a predictive model for risk stratification of no-show. Model validation was performed using 2014 data. Result: Out of 75,677 appointments in 2013, 28.6% were no-shows. Univariate analysis showed that 11 variables were associated with no-shows. Six variables (age, race, specialty, lead time, referral source, previous visit status) remained independently associated with no-shows in the MLR model, and their odds ratios were used to develop the weighted predictive scoring model. Weighted scores were 0 to 19, and five levels of no-show risk were derived: extremely low (score: 0–4; odds ratio (OR): 1.0); low (5–6; OR: 2.5); medium (7–8; OR: 5.6); high (9–10; OR: 9.2); and extremely high (11–19; OR: 16.7). The predictive ability of the model was tested using receiver operation curve analysis, where the area under curve (AUC) was 72%. AUC remained at 72% upon validation with 2014 data. Conclusion: The prediction model developed using only administrative data was robust and can be used for the risk stratification of SOC no-show for better resource utilisation to improve access to care.
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Adachi, Kazuhide, Takeshi Kawase, Kazunari Yoshida, Takahito Yazaki, and Satoshi Onozuka. "ABC Surgical Risk Scale for skull base meningioma: a new scoring system for predicting the extent of tumor removal and neurological outcome." Journal of Neurosurgery 111, no. 5 (November 2009): 1053–61. http://dx.doi.org/10.3171/2007.11.17446.
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Object Surgery for skull base meningiomas (SBMs) can lead to complications because these lesions are difficult to approach and can involve cranial nerves and arteries. The authors propose a scoring system to evaluate the relative risks and benefits of surgical treatment of SBMs. Methods The authors used a 2-step process to construct their scale. First, they derived significant predictive variables from retrospective data on 132 SBM cases treated surgically (primary surgeries only) between May 2000 and December 2005. Next, they validated the predictive accuracy of their scoring system in 60 consecutive cases treated surgically between January 1995 and April 2000, including both primary and repeated surgeries. Finally, they investigated the effect of the surgery on the patients' preoperative symptoms for consecutive cases treated surgically between January 1995 and December 2005, including both primary surgeries and retreatments. Results Five items that predicted surgical risk were identified: 1) tumor attachment size; 2) arterial involvement; 3) brainstem contact; 4) central cavity location; and 5) cranial nerve group involvement. The authors named their scoring system the ABC Surgical Risk Scale, after the initial letters of these items. Each factor was assigned a score of 0–2 points, and an additional point was added for previous surgical treatment or for radiation, giving a possible total score of 12 points. On average, the scoring system allocated 2 points for gross-total resections, 6.1 points for near-total resections, and 9 points for subtotal resections, with significant differences between groups. For cases scoring ≥ 8 points, the percentage of cases showing neurological deterioration postoperatively exceeded the percentage showing improvement. Conclusions The authors conclude that this scoring system can be used to predict the extent of tumor removal and that the scores reflect the surgical risk.
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Tazenkova, Olga Andreevna. "Application of Credit Risk Scoring Methods in Corporate Borrower Monitoring." Russian Digital Libraries Journal 24, no. 4 (September 12, 2021): 689–709. http://dx.doi.org/10.26907/1562-5419-2021-24-4-689-709.
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A method for assessing the risk of default of a corporate borrower at the monitoring stage based on a scoring assessment is proposed. This paper provides proof of the hypothesis that scoring methods for assessing credit risks can be used not only at the stage of initial assessment of a potential borrower when making a decision on lending, but also at the stage of its monitoring when accompanying a transaction. Monitoring is a periodic review of the credit quality of the corporate borrower with whom the loan agreement is concluded. This is done for the purpose of timely detection of negative signals, as well as timely response to threatening trends in the borrower's activities. Some credit institutions save on monitoring by relying on the decision-making system, considering it flawless. However, this saving can be a fatal mistake, since many things change over time during the "life" of the enterprise. This is facilitated by both external factors (political, economic) and internal (incorrect development strategy of the organization, inability to assess its own credit capabilities, unscrupulous counterparties). The proposed method is a system of automatic risk signals that have been tested for predictive ability, excluding manual procedures. The proposed solution includes markers (risk signals) that have a predictive ability above average, which can lead to a default of the corporate borrower. In addition, color marking is applied – red, yellow, green, which allows you to visualize the criticality of the identified risk signal depending on the predictive ability - a visual representation of the borrower's risks in order to facilitate interpretation. The analysis of the developed method showed how much it is possible to speed up the monitoring process, which will allow for a prompt response to the identified risk signals, as well as to predict the likely deterioration of the borrower's credit quality in the loan or guarantee portfolio without compromising the quality of risk assessment.
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Suppramote, Orawan, Prapatsara Pongpunpisand, Kanlaya Ladkam, and Somkiat Rujirawat. "A novel risk score for prediction of hypersensitivity reactions in cancer patients receiving carboplatin: Retrospective observational analysis." Journal of Clinical Oncology 34, no. 3_suppl (January 20, 2016): e282-e282. http://dx.doi.org/10.1200/jco.2016.34.3_suppl.e282.
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e282 Background: Hypersentitivity reactions (HSRs) from carboplatin are high incidence and most severity in Chulabhorn hospital. These reactions are associated with several causes including patient factors and experience in drug used. A reliable and valid tool for evaluated risk of HSRs before started carboplatin infusion should lead to prevent or decrease severity of the reactions. We innovated risk score to screen patient at high risk of HSRs. Methods: From October 2013 to September 2014, all cancer patients who received carboplatin in Chulabhorn hospital were included. A retrospective study design to developed risk scoring system for prediction of patients at high risk of carboplatin hypersensitivity called “Hypersensitivity risk score”. The hypersensitivity risk score was calculated for all patients receiving carboplatin and data for carboplatin hypersensitivity were obtained from medical records. Expected and observed HSRs were analyzed by using receiver operating characteristic (ROC) curve. Results: Seventy-three cancer patients received carboplatin and five (7%) patients had HSRs. Our scoring algorithm based on cancer type, number of carboplatin retreatment, duration between each retreatment, and number of carboplatin infusions prior to first reaction. All significant predictors were weighted into points and categorized to risk group which ranged from 0 to 8 . The ROC analysis for hypersensitivity risk score indicated good predictive accuracy with an area under the curve of 0.96 (95 %CI: 0.91-1.00). Data showed high sensitivity (80%) and specificity (94.85%) for a risk score cut-off of 4. The hypersensitivity risk score clearly differentiated the low (0-1), intermediate (2-3) and intermediate-high (4-5) and high (6-8) risk patients. Conclusions: The hypersensitivity risk score is a simple scoring system with high predictive value and differentiates low versus high risk patients. This score should be used for screen high risk of hypersensitivity reactions in patients receiving carboplatin.
Dissertations / Theses on the topic "Predictive lead scoring"
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Etminan, Ali. "Prediction of Lead Conversion With Imbalanced Data : A method based on Predictive Lead Scoring." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176433.
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An ongoing challenge for most businesses is to filter out potential customers from their audience. This thesis proposes a method that takes advantage of user data to classify po- tential customers from random visitors to a website. The method is based on the Predictive Lead Scoring method that segments customers based on their likelihood of purchasing a product. Our method, however, aims to predict user conversion, that is predicting whether a user has the potential to become a customer or not. Six supervised machine learning models have been used to carry out the classifica- tion task. To account for the high imbalance in the input data, multiple resampling meth- ods have been applied to the training data. The combination of classifier and resampling method with the highest average precision score has been selected as the best model. In addition, this thesis tries to quantify the effect of feature weights by evaluating some feature ranking and weighting schemes. Using the schemes, several sets of weights have been produced and evaluated by training a KNN classifier on the weighted features. The change in average precision obtained from the original KNN (without weighting) is used as the reference for measuring the performance of ranking and weighting schemes.
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Pereira, Rita Mafalda Magalhães. "Building a predictive lead scoring model for contact prioritization : the case of HUUB." Master's thesis, 2021. http://hdl.handle.net/10400.14/34877.
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In the last decades, machine learning has become quite popular for solving business problems, as it often delivers high-quality and efficient solutions. Moreover, the amount of data collected by companies has grown substantially, which has contributed to this trend. Companies do not have enough resources to contact every lead, so contact prioritization is essential. Lead scoring supports this task, by assigning a value to each lead based on his actions or characteristics. Even though it is expected that lead scoring contributes to higher conversion rates, there is still very few literature on how to use machine learning to automate this process. This dissertation shows how to combine historical data from Customer Relationship Management platforms and supervised learning to develop a lead scoring model for companies. The approach followed is based on the CRISP-DM method, where several tools were used, such as HubSpot, Microsoft Power BI and RStudio. The classification model proposed is a decision tree that predicts the leads’ conversion outcome (Won or Postpone), developed using the CART algorithm and data from a logistics company – HUUB. The main findings of this project conclude that machine learning can be used to develop a lead scoring model to perform contact prioritization. However, there are several factors, especially data-related, that should be taken into consideration, since they may impact the model’s performance. Lastly, a suggestion for future research is to develop an experiment to compare the results of manual and automated lead scoring, to assess if machine learning actually provides a superior alternative to the manual approach.Nas últimas décadas, o machine learning tornou-se bastante popular para resolver problemas organizacionais, já que tende a produzir soluções eficientes e de alta qualidade. Adicionalmente, a quantidade de dados colecionados pelas empresas cresceu substancialmente, o que contribuiu para esta tendência. As empresas não têm recursos suficientes para contactar todos os leads, pelo que é essencial priorizá-los. O lead scoring apoia esta tarefa, ao atribuir um valor para cada lead baseado nas suas ações ou características. Embora seja expectável que o lead scoring contribua para melhores taxas de conversão, ainda é escassa a literatura acerca da automatização deste processo através do machine learning. Esta dissertação expõe como combinar supervised learning e dados históricos de sistemas de Customer Relationship Management para desenvolver um modelo de lead scoring para empresas. A abordagem baseia-se no método CRISP-DM, onde diversas ferramentas foram usadas, nomeadamente o HubSpot, o Microsoft Power BI e o RStudio. O modelo de classificação proposto é uma árvore de decisão que prevê o desfecho de conversão dos leads, desenvolvido com o algoritmo CART e dados de uma empresa de logística – a HUUB. As principais descobertas deste projeto concluem que é viável utilizar o machine learning para desenvolver um modelo de lead scoring para priorizar os contactos. Contudo, há fatores que devem ser tidos em conta, especialmente relacionados com os dados, já que podem impactar o desempenho do modelo. Por fim, sugere-se para pesquisa futura o desenvolvimento de um estudo experimental que compare os resultados do lead scoring automatizado e manual, de forma a avaliar se o machine learning é de facto a melhor alternativa.
Book chapters on the topic "Predictive lead scoring"
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Chua, Siang Li, and Wai Leng Chow. "Use of Predictive and Simulation Models to Develop Strategies for Better Access Specialists Care." In Advances in Medical Technologies and Clinical Practice, 109–36. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0047-7.ch007.
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No-shows are patients who miss scheduled Specialist Outpatient Clinic (SOC) appointments. No-shows can impact patients' access to care and appointment lead time. This chapter describes a data-driven strategy of improving access to specialist care through first developing a stratified predictive scoring model to identify patients at risk of no-shows; second, studying the impact of a dynamic overbooking strategy that incorporates the use of the no-show prediction model using discrete event simulation (DES) on lead time. Seventeen variables related to new SOC appointments for subsidized patients in 2016 were analyzed. Multiple logistic regression (MLR) found eight variables independently associated with no-shows with area under receiver operation curve (AUC) 70%. The model was tested and validated. DES model simulated the appointment overbooking strategy as applied to the top highest volume specialties and concluded that lead time of Specialty 1 and 2 can be shortened by 27.5 days (49% improvement) and 21.3 (33%) respectively.
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"Amputation." In Standards for the Management of Open Fractures, edited by Simon Eccles, Bob Handley, Umraz Khan, Iain McFadyen, Jagdeep Nanchahal, and Selvadurai Nayagam, 111–24. Oxford University Press, 2020. http://dx.doi.org/10.1093/med/9780198849360.003.0012.
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The decision to amputate rather than reconstruct a severely injured limb (‘mangled extremity’) has historically been one of the most difficult choices faced by a trauma surgeon. The surgeon’s responsibility is heightened by the knowledge that delayed or incorrect decision-making may lead to worse outcomes. Unfortunately, hard data upon which to base reliable decisions remain elusive. A prospective analysis of the use of scoring systems including the Limb Salvage Index, the Predictive Salvage Index, the Hanover Fracture Scale, and the NISSSA (Nerve injury, Ischaemia, Soft-tissue contamination, Skeletal damage, Shock, Age) and MESS (Mangled Extremity Severity Score) scores did not validate the clinical utility of any of the scoring algorithms.
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Poluru, Ravi Kumar, Bharath Bhushan, Basha Syed Muzamil, Praveen Kumar Rayani, and Praveen Kumar Reddy. "Applications of Domain-Specific Predictive Analytics Applied to Big Data." In Advances in Business Information Systems and Analytics, 289–306. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-4999-4.ch016.
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Performing prediction on every domain belonging to industry/firm is measured as effective management. This prediction helps the firm effectively manage human power and other resources, which leads to good productivity. In this chapter, the authors discuss applications where predictive analytics are applied. The applications are as follows: evaluation of customer lifetime value used in retail industry, customer churn management in the telecommunication sector, credit scoring in banking, sentiment analysis on product reviews to understand the customer opinion, clinical decision support systems, news analytics, and social media analytics. They conclude the application areas of predictive analytics will drive the research community towards developing novel methods for handling big data.
Conference papers on the topic "Predictive lead scoring"
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Muhlbauer, W. Kent, Derek Johnson, Elaine Hendren, and Steve Gosse. "A New Generation of Pipeline Risk Algorithms." In 2006 International Pipeline Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/ipc2006-10178.
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While the previous generation of scoring-type algorithms have served us (the industry) well, the associated technical compromises can be troublesome in today’s environment of increasing regulatory and public oversight. Risk analyses often become the centerpiece of any legal, regulatory, or public proceedings. This prompts the need for analysis techniques that can produce risk estimates anchored in absolute terms, such as “consequences per mile year”. Accordingly, a new generation of algorithms has been developed to meet today’s needs without costly re-vamping of previously collected data or increasing the costs of risk analysis. A simple re-grouping of variables into categories of “exposure”, “mitigation”, and ‘resistance’, along with a few changes in the mathematics of combining variables, transitions older scoring models into the new approach. The advantages of the new algorithms are significant since they: • are more intuitive and predictive, • better model reality, • lead to better risk management decisions by distinguishing between unmitigated exposure to a threat, mitigation effectiveness, and system resistance, • eliminate the need for unrealistic and troublesome reweighting or balancing of variables for changes such as new technologies, • offer flexibility to present results in either absolute (probabilistic) terms or relative terms, depending on the user’s needs. The challenge is to accomplish these without losing the advantages of earlier approaches. One of the intent of the new algorithms is to avoid overly-analytic techniques that often accompany more absolute quantifications of risk. This paper will showcase this new generation of algorithms to better suit the changing needs of risk analysis within the pipeline industry.