Beyond the routine CBC: machine learning and statistical analyses identify research CBC parameter associations with myelodysplastic syndromes and specific underlying pathogenic variants.

AIMS: Given the time, expense and clinical expertise required for a myelodysplastic syndrome (MDS) diagnosis, there is a clear need for a cost-effective screening laboratory test that can rapidly and accurately distinguish patients with cytopenias related to MDS from other causes. METHODS: We measured conventional and research use only complete blood cell (CBC) parameters using the Sysmex XN-series haematology analyser in 102 MDS patients (70 patients with active MDS and 32 patients in remission), 43 patients with cytopenia without morphological evidence of MDS and 484 age-adjusted controls. A variety of algorithms, including random forest machine learning, were used to construct parameter-based models to predict the presence of MDS using both CBC and molecular data or CBC data alone and correlated individual pathogenic variants/genetic pathways with CBC parameters changes. RESULTS: Using the CBC parameters alone, our predictive model for active MDS showed a 0.86 receiver operating characteristic curve (ROC)/area under the ROC curve (AUC), with 0.87 sensitivity and 0.72 specificity; with the addition of the molecular and demographic status, the ROC/AUC improved to 0.93, sensitivity to 0.89 and specificity to 0.84. The most discriminatory MDS parameters were reflective of dysplastic neutrophil morphology, red cell count fragmentation and degree of platelet immaturity. Specific patterns of parameters were associated with individual gene pathogenic variants or affected pathways. CONCLUSIONS: CBC research parameters can be used as an adjunct to the haematological workup of cytopenia(s) to help screen for patients with high likelihood of MDS.

Current therapeutic approaches in the management of hemophilia-a consensus view by the Romanian Society of Hematology.

Hemophilia A (HA) and hemophilia B (HB) are rare disorders, being caused by the total lack or under-expression of two factors from the coagulation cascade coded by genes of the X chromosome. Thus, in hemophilic patients, the blood does not clot properly. This results in spontaneous bleeding episodes after an injury or surgical intervention. A patient-centered regimen is considered optimal. Age, pharmacokinetics, bleeding phenotype, joint status, adherence, physical activity, personal goals are all factors that should be considered when individualizing therapy. In the past 10 years, many innovations in the diagnostic and treatment options were presented as being either approved or in development, thus helping clinicians to improve the standard-of-care for patients with hemophilia. Recombinant factors still remain the standard of care in hemophilia, however they pose a challenge to treatment adherence because they have short half-life, which where the extended half-life (EHL) factors come with the solution, increasing the half-life to 96 hours. Gene therapies have a promising future with proven beneficial effects in clinical trials. We present and critically analyze in the current manuscript the pros and cons of all the major discoveries in the diagnosis and treatment of HA and HB, as well as identify key areas of hemophilia research where improvements are needed.

Modeling fMRI data generated by overlapping cognitive processes with unknown onsets using Hidden Process Models.

We present a new method for modeling fMRI time series data called Hidden Process Models (HPMs). Like several earlier models for fMRI analysis, Hidden Process Models assume that the observed data is generated by a sequence of underlying mental processes that may be triggered by stimuli. HPMs go beyond these earlier models by allowing for processes whose timing may be unknown, and that might not be directly tied to specific stimuli. HPMs provide a principled, probabilistic framework for simultaneously learning the contribution of each process to the observed data, as well as the timing and identities of each instantiated process. They also provide a framework for evaluating and selecting among competing models that assume different numbers and types of underlying mental processes. We describe the HPM framework and its learning and inference algorithms, and present experimental results demonstrating its use on simulated and real fMRI data. Our experiments compare several models of the data using cross-validated data log-likelihood in an fMRI study involving overlapping mental processes whose timings are not fully known.

Block-suffix shifting: fast, simultaneous medical concept set identification in large medical record corpora.

Owing to new advances in computer hardware, large text databases have become more prevalent than ever.Automatically mining information from these databases proves to be a challenge due to slow pattern/string matching techniques. In this paper we present a new, fast multi-string pattern matching method based on the well known Aho-Chorasick algorithm. Advantages of our algorithm include:the ability to exploit the natural structure of text, the ability to perform significant character shifting, avoiding backtracking jumps that are not useful, efficiency in terms of matching time and avoiding the typical "sub-string" false positive errors.Our algorithm is applicable to many fields with free text, such as the health care domain and the scientific document field. In this paper, we apply the BSS algorithm to health care data and mine hundreds of thousands of medical concepts from a large Electronic Medical Record (EMR) corpora simultaneously and efficiently. Experimental results show the superiority of our algorithm when compared with the top of the line multi-string matching algorithms.

Modeling the fMRI signal via Hierarchical Clustered Hidden Process Models.

Machine Learning techniques have been used quite widely for the task of predicting cognitive processes from fMRI data. However, these models do not describe well the fMRI signal when it is generated by multiple cognitive processes that are simultaneously active. In this paper we consider the problem of accurately modeling the fMRI signal of a human subject who is performing a task involving multiple concurrent cognitive processes. We present a Hierarchical Clustering extension of Hidden Process Models which, by taking advantage of automatically discovered similarities in the activation among neighboring voxels, achieves significantly better performance than standard generative models in terms of Average Log Likelihood.

Evaluating the C-section rate of different physician practices: using machine learning to model standard practice.

The C-section rate of a population of 22,175 expectant mothers is 16.8%; yet the 17 physician groups that serve this population have vastly different group C-section rates, ranging from 13% to 23%. Our goal is to determine retrospectively if the variations in the observed rates can be attributed to variations in the intrinsic risk of the patient sub-populations (i.e. some groups contain more "high-risk C-section" patients), or differences in physician practice (i.e. some groups do more C-sections). We apply machine learning to this problem by training models to predict standard practice from retrospective data. We then use the models of standard practice to evaluate the C-section rate of each physician practice. Our results indicate that although there is variation in intrinsic risk among the groups, there also is much variation in physician practice.

Classifying instantaneous cognitive states from FMRI data.

We consider the problem of detecting the instantaneous cognitive state of a human subject based on their observed functional Magnetic Resonance Imaging (fMRI) data. Whereas fMRI has been widely used to determine average activation in different brain regions, our problem of automatically decoding instantaneous cognitive states has received little attention. This problem is relevant to diagnosing cognitive processes in neurologically normal and abnormal subjects. We describe a machine learning approach to this problem, and report on its successful use for discriminating cognitive states such as observing a picture versus reading a sentence, and reading a word about people versus reading a word about buildings.

Machine learning for sub-population assessment: evaluating the C-section rate of different physician practices.

We apply machine learning to the problem of subpopulation assessment for Caesarian Section. In subpopulation assessment, we are interested in making predictions not for a single patient, but for groups of patients. Typically, in any large population, different subpopulations will have different "outcome" rates. In our example, the C-section rate of a population of 22,176 expectant mothers is 16.8%; yet, the 17 physician groups that serve this population have vastly different group C-section rates, ranging from 11% to 23%. The ultimate goal of subpopulation assessment is to determine if these variations in the observed rates can be attributed to (a) variations in intrinsic risk of the patient sub-populations (i.e. some groups contain more "high-risk C-section" patients), or (b) differences in physician practice (i.e. some groups do more C-sections). Our results indicate that although there is some variation in intrinsic risk, there is also much variation in physician practice.

Mining time-dependent patient outcomes from hospital patient records.

We describe REMIND, a data mining framework that accurately infers missing clinical information by reasoning over the entire patient record. Hospitals collect computerized patient records (CPR's) in structured (database tables) and unstructured (free text) formats. Structured clinical data in the CPR's is often poorly recorded, and information may be missing about key outcomes and processes. For instance, for a population of 344 colon cancer patients, important clinical outcomes, such as disease state and its evolution, are stored only as unstructured data (doctors' dictations) in the CPR. Raw evidence (extracted directly from the CPR) is not a good predictor of disease state. Yet by combining this evidence in a principled fashion (using methods from uncertain and temporal reasoning), REMIND accurately infers disease state sequences for recurrence, a complex time-varying outcome, for these patients. These outcomes can now be added back into the CPR in structured form.

[Surgical prevention of posterior capsule opacification]. / Prevenirea chirurgicala a opacifierii capsulei posterioare.

AIM OF STUDY: To evaluate a 20 years period of cataract surgery, in order to decrease the posterior capsule opacification. MATERIAL AND METHOD: 3320 eyes after cataract surgery with posterior chamber intraocular lens was examined for Soemmering ring. This appears before posterior capsule opacification. CONCLUSIONS: A very good clearing of lous cortex is very important in preventing posterior capsule opacification. If it also useful a correct hydrodissection.