Forecasting Hospital Readmissions with Machine Learning

Hospital readmissions are regarded as a compounding economic factor for healthcare systems. In fact, the readmission rate is used in many countries as an indicator of the quality of services provided by a health institution. The ability to forecast patients’ readmissions allows for timely intervention and better post-discharge strategies, preventing future life-threatening events, and reducing medical costs to either the patient or the healthcare system. In this paper, four machine learning models are used to forecast readmissions: support vector machines with a linear kernel, support vector machines with an RBF kernel, balanced random forests, and weighted random forests. The dataset consists of 11,172 actual records of hospitalizations obtained from the General Hospital of Komotini “Sismanogleio” with a total of 24 independent variables. Each record is composed of administrative, medical-clinical, and operational variables. The experimental results indicate that the balanced random forest model outperforms the competition, reaching a sensitivity of 0.70 and an AUC value of 0.78

Biomarkers in melanoma

Biomarkers are tumour- or host-related factors that correlate with tumour biological behaviour and patient prognosis. High-throughput analytical techniques--DNA and RNA microarrays--have identified numerous possible biomarkers, but their relevance to melanoma progression, clinical outcome and the selection of optimal treatment strategies still needs to be established. The review discusses a possible molecular basis for predictive tissue biomarkers such as melanoma thickness, ulceration and mitotic activity, and provides a list of promising new biomarkers identified from tissue microarrays that needs confirmation by independent, prospectively collected clinical data sets. In addition, common predictive serum biomarkers--lactate dehydrogenase, S100B and melanoma-inhibiting activity--as well as selected investigational serum biomarkers such as TA90IC and YKL-40 are also reviewed. A more accurate, therapeutically predictive classification of human melanomas and selection of patient populations that would profit from therapeutic interventions are among the major challenges expected to be addressed in the futur

Biomarkers in melanoma

Biomarkers are tumour- or host-related factors that correlate with tumour biological behaviour and patient prognosis. High-throughput analytical techniques—DNA and RNA microarrays—have identified numerous possible biomarkers, but their relevance to melanoma progression, clinical outcome and the selection of optimal treatment strategies still needs to be established. The review discusses a possible molecular basis for predictive tissue biomarkers such as melanoma thickness, ulceration and mitotic activity, and provides a list of promising new biomarkers identified from tissue microarrays that needs confirmation by independent, prospectively collected clinical data sets. In addition, common predictive serum biomarkers—lactate dehydrogenase, S100B and melanoma-inhibiting activity—as well as selected investigational serum biomarkers such as TA90IC and YKL-40 are also reviewed. A more accurate, therapeutically predictive classification of human melanomas and selection of patient populations that would profit from therapeutic interventions are among the major challenges expected to be addressed in the future

A new mathematical model for the interpretation of translational research evaluating six CTLA-4 polymorphisms in high-risk melanoma patients receiving adjuvant interferon

Adjuvant therapy of stage IIB/III melanoma with interferon reduces relapse and mortality by up to 33% but is accompanied by toxicity-related complications. Polymorphisms of the CTLA-4 gene associated with autoimmune diseases could help in identifying interferon treatment benefits. We previously genotyped 286 melanoma patients and 288 healthy (unrelated) individuals for six CTLA-4 polymorphisms (SNP). Previous analyses found no significant differences between the distributions of CTLA-4 polymorphisms in the melanoma population vs. controls, no significant difference in relapse free and overall survivals among patients and no correlation between autoimmunity and specific alleles. We report new analysis of these CTLA-4 genetic profiles, using Network Phenotyping Strategy (NPS). It is graph-theory based method, analyzing the SNP patterns. Application of NPS on CTLA-4 polymorphism captures allele relationship pattern for every patient into 6-partite mathematical graph P. Graphs P are combined into weighted 6-partite graph S, which subsequently decomposed into reference relationship profiles (RRP). Finally, every individual CTLA-4 genotype pattern is characterized by the graph distances of P from eight identified RRP's. RRP's are subgraphs of S, collecting equally frequent binary allele co-occurrences in all studied loci. If S topology represents the genetic "dominant model", the RRP's and their characteristic frequencies are identical to expectation-maximization derived haplotypes and maximal likelihood estimates of their frequencies. The graphrepresentation allows showing that patient CTLA-4 haplotypes are uniquely different from the controls by absence of specific SNP combinations. New function-related insight is derived when the 6-partite graph reflects allelic state of CTLA-4. We found that we can use differences between individual P and specific RRPs to identify patient subpopulations with clearly different polymorphic patterns relatively to controls as well as to identify patients with significantly different survival. © 2014 Pancoska et al

Sinnvoller Einsatz von Tumormarkern

Tumor markers refer to all detectable and measurable analytes which are able to indicate a solid tumor or contribute to its characterization or judgment concerning tumor spread and therapy efficacy. Among the markers, humoral circulating tumor substances, such as precursors of normal antigens, ectopically produced hormones or enzymes, ontogenetic old reactivated antigens, hybridoma-defined mucins and cytokeratins are of special interest. Up to now, no tumor specific biomarker has been detected, all markers known so far are physiological components of blood; thus, their diagnostic capacity is more related to quantity than to quality. The tumor marker concentration depends on the tumor blood supply and reflects tumor mass and tumor spread as a sum of marker expression, synthesis, release, the catabolism of the organism, as well as the marker excretion. Changes in biomarker levels without correlation to tumor load can be due to impairment of the liver and kidney function or due to invasive diagnostic methods (endoscopy, biopsy, ureteral catheter) or due to acute reactions on treatment (surgery, radio-chemotherapy). Due to problems with standardization between assays from different producers measuring the same antigen, interpretation of biomarkers of single measurements, such as PSA (prostate specific antigen), must be performed using assay specific reference ranges and interpretation of serial measurements must be performed using the identical assay. The test result has to be indicated together with the assay used (kit and producer). Among the potential indications for tumor marker determinations, the early detection or screening of a tumor is unrealistic - except PSA in prostate cancer detection. In rare cases, biomarkers can be helpful in tumor localization (HTG (human thyreoglobuline), PSA) and support of primary diagnosis, the knowledge about their prognostic relevance is increasing, the most widely used indication is therapy control and follow-up care in context with medical imaging. Provided that markers are critically selected following the localization of the tumor, that serial determinations are performed using the identical assay and that the clinical question is relevant, tumor markers contribute to a significant degree to diagnosis, prognosis, therapy control and early detection of metastatic or recurrent disease. Especially in the field of diagnostic oncology, the quality of the investigator is significantly linked to the quality of the test result

Evaluation of six CTLA-4 polymorphisms in high-risk melanoma patients receiving adjuvant interferon therapy in the He13A/98 multicenter trial

<p>ABSTRACT</p> <p>Purpose</p> <p>Interferon is approved for adjuvant treatment of patients with stage IIb/III melanoma. The toxicity and uncertainty regarding survival benefits of interferon have qualified its acceptance, despite significant durable relapse prevention in a fraction of patients. Predictive biomarkers that would enable selection of patients for therapy would have a large impact upon clinical practice. Specific CTLA-4 polymorphisms have previously shown an association with response to CTLA-4 blockade in patients with metastatic melanoma and the development of autoimmunity.</p> <p>Experimental design</p> <p>286 melanoma patients and 288 healthy controls were genotyped for six CTLA-4 polymorphisms previously suggested to be important (AG 49, CT 318, CT 60, JO 27, JO30 and JO 31). Specific allele frequencies were compared between the healthy and patient populations, as well as presence or absence of these in relation to recurrence. Alleles related to autoimmune disease were also investigated.</p> <p>Results</p> <p>No significant differences were found between the distributions of CTLA-4 polymorphisms in the melanoma population compared with healthy controls. Relapse free survival (RFS) and overall survival (OS) did not differ significantly between patients with the alleles represented by these polymorphisms. No correlation between autoimmunity and specific alleles was shown. The six polymorphisms evaluated where strongly associated (Fisher's exact p-values < 0.001 for all associations) and significant linkage disequilibrium among these was indicated.</p> <p>Conclusion</p> <p>No polymorphisms of CTLA-4 defined by the SNPs studied were correlated with improved RFS, OS, or autoimmunity in this high-risk group of melanoma patients.</p

Application of advanced reservoir characterization, simulation and production optimization strategies to maximize recovery in slope and basin clastic reservoirs, West Texas (Delaware Basin). Annual report

The objective of this project is to demonstrate that detailed reservoir characterization of slope and basin clastic reservoirs in sandstones of the Delaware Mountain Group in the Delaware Basin of West Texas and New Mexico is a cost-effective way to recover a higher percentage of the original oil in place through strategic placement of infill wells and geologically based field development. This project involves reservoir characterization of two Late Permian slope and basin clastic reservoirs in the Delaware Basin, West Texas, followed by a field demonstration in one of the fields. The fields being investigated are Geraldine Ford and Ford West fields in Reeves and Culberson Counties, Texas. Project objectives are divided into two major phases, reservoir characterization and implementation. The objectives of the reservoir characterization phase of the project were to provide a detailed understanding of the architecture and heterogeneity of the two fields, the Ford Geraldine unit and Ford West field. Reservoir characterization utilized 3-D seismic data, high-resolution sequence stratigraphy, subsurface field studies, outcrop characterization, and other techniques. Once reservoir characterized was completed, a pilot area of approximately 1 mi{sup 2} at the northern end of the Ford Geraldine unit was chosen for reservoir simulation. This report summarizes the results of the second year of reservoir characterization