scholarly journals Developing a Machine Learning Tool for Dynamic Cancer Treatment Strategies

2020 ◽  
Vol 34 (10) ◽  
pp. 13742-13743
Author(s):  
Jiaming Zeng
Keyword(s):  
Machine Learning ◽  
Decision Analysis ◽  
Cancer Treatment ◽  
Causal Inference ◽  
Medical Records ◽  
Treatment Strategies ◽  
Decision Support Tool ◽  
Patient Characteristics ◽  
Cancer Therapies ◽  
Support Tool

With the rising number and complexity of cancer therapies, it is increasingly difficult for clinicians to identity an optimal combination of treatments for a patient. Our research aims to provide a decision support tool to optimize and supplant cancer treatment decisions. Leveraging machine learning, causal inference, and decision analysis, we will utilize electronic medical records to develop dynamic cancer treatment strategies that advice clinicians and patients based on patient characteristics, medical history, and etc. The research hopes to bridge the understanding between causal inference and decision analysis and ultimately develops an artificial intelligence tool that improves clinical outcomes over current practices.

scholarly journals Clinical prediction of thrombectomy eligibility: A systematic review and 4-item decision tree

2018 ◽  
Vol 14 (5) ◽  
pp. 530-539 ◽  
Author(s):  
Gaia T Koster ◽  
T Truc My Nguyen ◽  
Erik W van Zwet ◽  
Bjarty L Garcia ◽  
Hannah R Rowling ◽  
...  
Keyword(s):  
Decision Tree ◽  
Prediction Model ◽  
External Validation ◽  
Facial Asymmetry ◽  
Decision Support Tool ◽  
Patient Characteristics ◽  
Group Lasso ◽  
Lasso Regression ◽  
Vessel Occlusion ◽  
Support Tool

Background A clinical large anterior vessel occlusion (LAVO)-prediction scale could reduce treatment delays by allocating intra-arterial thrombectomy (IAT)-eligible patients directly to a comprehensive stroke center. Aim To subtract, validate and compare existing LAVO-prediction scales, and develop a straightforward decision support tool to assess IAT-eligibility. Methods We performed a systematic literature search to identify LAVO-prediction scales. Performance was compared in a prospective, multicenter validation cohort of the Dutch acute Stroke study (DUST) by calculating area under the receiver operating curves (AUROC). With group lasso regression analysis, we constructed a prediction model, incorporating patient characteristics next to National Institutes of Health Stroke Scale (NIHSS) items. Finally, we developed a decision tree algorithm based on dichotomized NIHSS items. Results We identified seven LAVO-prediction scales. From DUST, 1316 patients (35.8% LAVO-rate) from 14 centers were available for validation. FAST-ED and RACE had the highest AUROC (both >0.81, p < 0.01 for comparison with other scales). Group lasso analysis revealed a LAVO-prediction model containing seven NIHSS items (AUROC 0.84). With the GACE (Gaze, facial Asymmetry, level of Consciousness, Extinction/inattention) decision tree, LAVO is predicted (AUROC 0.76) for 61% of patients with assessment of only two dichotomized NIHSS items, and for all patients with four items. Conclusion External validation of seven LAVO-prediction scales showed AUROCs between 0.75 and 0.83. Most scales, however, appear too complex for Emergency Medical Services use with prehospital validation generally lacking. GACE is the first LAVO-prediction scale using a simple decision tree as such increasing feasibility, while maintaining high accuracy. Prehospital prospective validation is planned.

scholarly journals Site Selection for Joint Logistics over the Shore (JLOTS) Operations Using Multi- Objective Decision Analysis

2016 ◽  
Vol 4 (2) ◽  
pp. 88-95
Author(s):  
Mike Evans ◽  
Mike Lami ◽  
Brendan Madarasz ◽  
Benjamin Smith ◽  
Chris Green
Keyword(s):  
Decision Analysis ◽  
Site Selection ◽  
Decision Support Tool ◽  
Military Operations ◽  
Support Tool ◽  
Multi Objective ◽  
Selection Protocol ◽  
Operational Criteria ◽  
A Site ◽  
Development Center

As the U.S. military faces an increasing need to deploy across a range of military operations and environments, the ability to establish and sustain logistics support remains a major challenge. The Engineer Research and Development Center is currently developing the Planning Logistics Analysis Network System (PLANS), a decision support tool, to facilitate strategic and operational logistics planning. This paper describes a site selection protocol for logistics operations occurring without a suitable port, commonly referred to as Logistics over-the Shore (LOTS) operations. The model uses multi- objective decision analysis techniques to weight different operational criteria to determine the best overall site for logistics over the shore operations. This tool will enhance the time and accuracy in determining an optimal site that meets the decision maker’s specific operational needs.

Predictive performance of the American College of Surgeons universal risk calculator in neurosurgical patients

2018 ◽  
Vol 128 (3) ◽  
pp. 942-947 ◽  
Author(s):  
Sasha Vaziri ◽  
Jacob Wilson ◽  
Joseph Abbatematteo ◽  
Paul Kubilis ◽  
Saptarshi Chakraborty ◽  
...  
Keyword(s):  
Predictive Performance ◽  
Decision Support Tool ◽  
Patient Characteristics ◽  
Risk Calculator ◽  
Discriminative Performance ◽  
Surgical Risk ◽  
Support Tool ◽  
Acs Nsqip ◽  
C Statistic ◽  
Neurosurgical Patients

OBJECTIVEThe American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) universal Surgical Risk Calculator is an online decision-support tool that uses patient characteristics to estimate the risk of adverse postoperative events. Further validation of this risk calculator in the neurosurgical population is needed; therefore, the object of this study was to assess the predictive performance of the ACS NSQIP Surgical Risk Calculator in neurosurgical patients treated at a tertiary care center.METHODSA single-center retrospective review of 1006 neurosurgical patients treated in the period from September 2011 through December 2014 was performed. Individual patient characteristics were entered into the NSQIP calculator. Predicted complications were compared with actual occurrences identified through chart review and administrative quality coding data. Statistical models were used to assess the predictive performance of risk scores. Traditionally, an ideal risk prediction model demonstrates good calibration and strong discrimination when comparing predicted and observed events.RESULTSThe ACS NSQIP risk calculator demonstrated good calibration between predicted and observed risks of death (p = 0.102), surgical site infection (SSI; p = 0.099), and venous thromboembolism (VTE; p = 0.164) Alternatively, the risk calculator demonstrated a statistically significant lack of calibration between predicted and observed risk of pneumonia (p = 0.044), urinary tract infection (UTI; p < 0.001), return to the operating room (p < 0.001), and discharge to a rehabilitation or nursing facility (p < 0.001). The discriminative performance of the risk calculator was assessed using the c-statistic. Death (c-statistic 0.93), UTI (0.846), and pneumonia (0.862) demonstrated strong discriminative performance. Discharge to a rehabilitation facility or nursing home (c-statistic 0.794) and VTE (0.767) showed adequate discrimination. Return to the operating room (c-statistic 0.452) and SSI (0.556) demonstrated poor discriminative performance. The risk prediction model was both well calibrated and discriminative only for 30-day mortality.CONCLUSIONSThis study illustrates the importance of validating universal risk calculators in specialty-specific surgical populations. The ACS NSQIP Surgical Risk Calculator could be used as a decision-support tool for neurosurgical informed consent with respect to predicted mortality but was poorly predictive of other potential adverse events and clinical outcomes.

Analysis of oral complications related to cancer therapy

2013 ◽  
Vol 9 (2) ◽  
Author(s):  
Antonio Eugenio Magnabosco Neto ◽  
Fernando Henrique Westphalen
Keyword(s):  
Oral Cavity ◽  
Cancer Treatment ◽  
Medical Records ◽  
Adverse Reactions ◽  
The Body ◽  
Cancer Therapies ◽  
Oral Complications ◽  
Dose Limitation ◽  
Number Of Patients

Introduction: The side effects and adverse reactions related to cancer therapies may cause significant alterations in the oral cavity, discomfort or even severe pain in parts of the body, patient’s nutritional deficiency, delay in the administration of oncologic drugs or dose limitation, an increase of hospitalization time and of the related expenses, as well as a decrease in the patient’s quality of life. Objective: The purpose of this study was to determine the deleterious effects of cancer therapies in the oral cavity. Material and methods: Data was gathered from medical records of the treatment of 643 cancer patients at the São José Hospital, in Joinville, state of Santa Catarina, from January to September 2012. Among the records selected for this study, 59.41% were female patients, with a mean age of 51 to 60 years. Results: Oral complications were reported in 72.47% of the patients, and the complication with the highest prevalence was mucositis (14.62%) followed by dry mouth (10.58%). Most of the patients had not received dental care prior to the cancer treatment, and no dental record was found. Conclusions: Several different oral disorders were reported as a result of cancer treatment, and a significant number of patients needed dental evaluation prior to the treatment.

scholarly journals Accuracy of telephone triage in patients suspected of transient ischaemic attack or stroke: A cross-sectional study

2020 ◽  
Author(s):  
Daphne Carmen Erkelens ◽  
Frans H. Rutten ◽  
Loes T. Wouters ◽  
L. Servaas Dolmans ◽  
Esther de Groot ◽  
...  
Keyword(s):  
Clinical Outcomes ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Decision Support Tool ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Telephone Triage ◽  
Cross Sectional ◽  
Support Tool ◽  
Ischaemic Attack

Abstract Background: The Netherlands Triage Standard (NTS) is a widely used decision support tool for telephone triage at Dutch out-of-hours primary care services (OHS-PC), which, however, has never been validated against clinical outcomes. We aimed to determine the accuracy of the NTS urgency allocation for patients with neurological symptoms suggestive of a transient ischaemic attack (TIA) or stroke, with the clinical outcomes TIA, stroke, and other (neurologic) life-threatening events (LTEs) as the reference.Method: A cross-sectional study of telephone triage recordings of patients with neurological symptoms calling the OHS-PC between 2014 and 2016.The allocated NTS urgencies were derived from the electronic medical records of the OHS-PC. The clinical outcomes were retrieved from the electronic medical records of the patients’ own general practitioners. The accuracy of a high NTS urgency allocation (medical help within three hours) was calculated in terms of sensitivity, specificity, positive and negative predictive values (PPV and NPV) with the clinical outcomes TIA/stroke/other LTEs as the reference.Results: Of 1,269 patients, 635 (50.0%) received the diagnosis TIA/stroke (34.2% TIA/minor stroke, 15.8% major ischaemic or haemorrhagic stroke), and 4.8% other LTEs. For TIA/stroke/other LTEs, the sensitivity and specificity of the NTS urgency allocation were 0.72 (95%CI 0.68-0.75) and 0.48 (95%CI 0.43-0.52), and the PPV and NPV were 0.62 (95%CI 0.60-0.64) and 0.58 (95%CI 0.54-0.62).Conclusions: The NTS decision support tool used in Dutch OHS-PC performed poor to moderately regarding safety (sensitivity) and efficiency (specificity) in allocating adequate urgencies to patients with and without TIA/stroke/other LTEs.

scholarly journals Endometrial Cancer Patients: A Cohort Previous to Changes in Tumour Behaviour and Treatment Strategies

2011 ◽  
Vol 2011 ◽  
pp. 1-6
Author(s):  
F. K. L. Tournois ◽  
H. J. M. M. Mertens
Keyword(s):  
Endometrial Cancer ◽  
Cancer Treatment ◽  
Treatment Strategies ◽  
Patient Characteristics ◽  
National Guidelines ◽  
Treatment Regimens ◽  
Time Period ◽  
New Treatment ◽  
Figo Classification

Nowadays, the incidence of endometrial cancer is rising, especially of high-grade endometrial tumours. Recently, the FIGO classification of endometrial cancer has changed worldwide. Besides that, treatment strategies are changing. The purpose of this study was to analyse the adherence to the national guidelines of cancer treatment and to analyse patterns of disease relapse and survival. We focused on a group of patients () with endometrial cancer, in a time period in which new treatment strategies are not yet completely implemented. Because of multiple upcoming changes in patient characteristics, tumour classification, as well as treatment regimens, a more heterogeneous cohort of patients diagnosed with endometrial cancer will appear. From now on, all those changes will have their effects on the followup of conventional endometrial cancer treatment. In our opinion, it is, therefore, valuable to have the current, more homogenous, cohort clearly described.

scholarly journals A Shoreline Response Program (SRP) Decision Support Tool based on the Geographic Variability in Attenuation and Weathering of Stranded Oil.

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Ed Owens ◽  
Elliott Taylor ◽  
Chunjiang An ◽  
Zhi Chen ◽  
George Danner ◽  
...  
Keyword(s):  
Decision Support ◽  
Web Application ◽  
Treatment Options ◽  
Critical Role ◽  
Treatment Strategies ◽  
Decision Support Tool ◽  
Data Bases ◽  
Support Tool ◽  
Fine Grained ◽  
Wide Range

ABSTRACT #1141234 The coastal waters of Canada embrace a wide range of physical environments and ecosystems from the warm, sediment-rich waters of the Bay of Fundy to the nutrient-limited cold waters of the high Arctic. This range of biophysical characteristics impacts natural attenuation and weathering processes for oil stranded on shorelines. This study was conducted to: 1) identify and quantify the primary regional parameters that control shoreline oil translocation (removal) processes and pathways and 2) define the effectiveness and environmental consequences of current and potential oiled shoreline treatment strategies and tactics. A specific knowledge gap, here and elsewhere in the world, has been in understanding how the distribution and character of fine-grained sediments affect stranded oil attenuation. Fine-grained sediments (&lt;1mm) can play a critical role in natural or induced (that is, shoreline treatment) oil dispersal. Shoreline sediment samples were collected and analyzed from representative locations on Arctic, Atlantic, and Pacific Ocean beaches to provide a broad geographic characterization of mineral fines at the regional level. This knowledge is the basis for an “Oiled Shoreline Response Program (SRP) Decision Support Tool” to aid spill scientists, students, environmental resource managers, spill responders and the public in understanding the response methods and the ramifications and consequences of their shoreline treatment options without the need to digest technical papers, large reports, or data bases. This MPRI SRP Decision Support Tool is intended to be a dynamic, interactive, multi-layered, geographically and seasonally-based model for shoreline oil spill response decision analyses. A goal of this interactive model is to move away from the traditional static format of learning from explanations in text reports and publications to an interactive tool that encourages its users to explore and fully understand the significance of the different environmental factors outlined in publications and data bases. Recent advances in web technology make this possible. The development of user interface platforms such as React, libraries such as D3, and notebook forms like Observable has created a palette of technologies that together make web application patterns such as Documodels a much more streamlined development process. The power of this medium is to convey a complex subject and to enable a user to grasp keen insights and so understand the consequences of intervention decisions.

A Machine-Learning Clinical Decision Support Tool for Myocardial Infarction Diagnosis

2021 ◽  
Vol 28 ◽  
pp. S13
Author(s):  
Saarang Panchavati ◽  
Carson Lam ◽  
Anurag Garikipati ◽  
Nicole Zelin ◽  
Emily Pellegrini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Clinical Decision Support Tool

scholarly journals corRECTreatment: A web-based decision support tool for rectal cancer treatment that uses the analytic hierarchy process and decision tree

2015 ◽  
Vol 06 (01) ◽  
pp. 56-74 ◽  
Author(s):  
O. Dicle ◽  
S. Sökmen ◽  
C.C. Çelikoğlu ◽  
A. Suner ◽  
G. Karakülah
Keyword(s):  
Rectal Cancer ◽  
Decision Support ◽  
Cancer Treatment ◽  
Treatment Options ◽  
Decision Support Tool ◽  
Analytic Hierarchy ◽  
Web Based ◽  
Support Tool ◽  
Rectal Cancer Treatment ◽  
The Analytic Hierarchy Process

SummaryBackground: The selection of appropriate rectal cancer treatment is a complex multi-criteria decision making process, in which clinical decision support systems might be used to assist and enrich physicians’ decision making.Objective: The objective of the study was to develop a web-based clinical decision support tool for physicians in the selection of potentially beneficial treatment options for patients with rectal cancer.Methods: The updated decision model contained 8 and 10 criteria in the first and second steps respectively. The decision support model, developed in our previous study by combining the Analytic Hierarchy Process (AHP) method which determines the priority of criteria and decision tree that formed using these priorities, was updated and applied to 388 patients data collected retrospectively. Later, a web-based decision support tool named corRECTreatment was developed. The compatibility of the treatment recommendations by the expert opinion and the decision support tool was examined for its consistency. Two surgeons were requested to recommend a treatment and an overall survival value for the treatment among 20 different cases that we selected and turned into a scenario among the most common and rare treatment options in the patient data set.Results: In the AHP analyses of the criteria, it was found that the matrices, generated for both decision steps, were consistent (consistency ratio<0.1). Depending on the decisions of experts, the consistency value for the most frequent cases was found to be 80% for the first decision step and 100% for the second decision step. Similarly, for rare cases consistency was 50% for the first decision step and 80% for the second decision step.Conclusions: The decision model and corRECTreatment, developed by applying these on real patient data, are expected to provide potential users with decision support in rectal cancer treatment processes and facilitate them in making projections about treatment options.Citation: Suner A, Karakülah G, Dicle O, Sökmen S, Çelikoglu CC. corRECTreatment: A web-based decision support tool for rectal cancer treatment that uses the analytic hierarchy process and decision tree. Appl Clin Inf 2015; 6: 56–74http://dx.doi.org/10.4338/ACI-2014-10-RA-0087

scholarly journals Computational Barthel Index: an automated tool for assessing and predicting activities of daily living among nursing home patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Janusz Wojtusiak ◽  
Negin Asadzadehzanjani ◽  
Cari Levy ◽  
Farrokh Alemi ◽  
Allison E. Williams
Keyword(s):  
Activities Of Daily Living ◽  
Barthel Index ◽  
Daily Living ◽  
Decision Support Tool ◽  
Patient Characteristics ◽  
Supervised Machine Learning ◽  
Department Of Veterans Affairs ◽  
Model Quality ◽  
Support Tool ◽  
Nursing Home Patients

Abstract Background Assessment of functional ability, including activities of daily living (ADLs), is a manual process completed by skilled health professionals. In the presented research, an automated decision support tool, the Computational Barthel Index Tool (CBIT), was constructed that can automatically assess and predict probabilities of current and future ADLs based on patients’ medical history. Methods The data used to construct the tool include the demographic information, inpatient and outpatient diagnosis codes, and reported disabilities of 181,213 residents of the Department of Veterans Affairs’ (VA) Community Living Centers. Supervised machine learning methods were applied to construct the CBIT. Temporal information about times from the first and the most recent occurrence of diagnoses was encoded. Ten-fold cross-validation was used to tune hyperparameters, and independent test sets were used to evaluate models using AUC, accuracy, recall and precision. Random forest achieved the best model quality. Models were calibrated using isotonic regression. Results The unabridged version of CBIT uses 578 patient characteristics and achieved average AUC of 0.94 (0.93–0.95), accuracy of 0.90 (0.89–0.91), precision of 0.91 (0.89–0.92), and recall of 0.90 (0.84–0.95) when re-evaluating patients. CBIT is also capable of predicting ADLs up to one year ahead, with accuracy decreasing over time, giving average AUC of 0.77 (0.73–0.79), accuracy of 0.73 (0.69–0.80), precision of 0.74 (0.66–0.81), and recall of 0.69 (0.34–0.96). A simplified version of CBIT with 50 top patient characteristics reached performance that does not significantly differ from full CBIT. Conclusion Discharge planners, disability application reviewers and clinicians evaluating comparative effectiveness of treatments can use CBIT to assess and predict information on functional status of patients.

Sign in / Sign up

Export Citation Format

Share Document