A deep learning integrated Lee-Carter model

In the field of mortality, the Lee–Carter based approach can be considered the milestone to forecast mortality rates among stochastic models. We could define a “Lee–Carter model family” that embraces all developments of this model, including its first formulation (1992) that remains the benchmark for comparing the performance of future models. In the Lee–Carter model, the kt parameter, describing the mortality trend over time, plays an important role about the future mortality behavior. The traditional ARIMA process usually used to model kt shows evident limitations to describe the future mortality shape. Concerning forecasting phase, academics should approach a more plausible way in order to think a nonlinear shape of the projected mortality rates. Therefore, we propose an alternative approach the ARIMA processes based on a deep learning technique. More precisely, in order to catch the pattern of kt series over time more accurately, we apply a Recurrent Neural Network with a Long Short-Term Memory architecture and integrate the Lee–Carter model to improve its predictive capacity. The proposed approach provides significant performance in terms of predictive accuracy and also allow for avoiding the time-chunks’ a priori selection. Indeed, it is a common practice among academics to delete the time in which the noise is overflowing or the data quality is insufficient. The strength of the Long Short-Term Memory network lies in its ability to treat this noise and adequately reproduce it into the forecasted trend, due to its own architecture enabling to take into account significant long-term patterns

Longevity risk management through Machine Learning: state of the art

Longevity risk management is an area of the life insurance business where the use of Artificial Intelligence is still underdeveloped. The paper retraces the main results of the recent actuarial literature on the topic to draw attention to the potential of Machine Learning in predicting mortality and consequently improving the longevity risk quantification and management, with practical implication on the pricing of life products with long-term duration and lifelong guaranteed options embedded in pension contracts or health insurance products. The application of AI methodologies to mortality forecasts improves both fitting and forecasting of the models traditionally used. In particular, the paper presents the Classification and the Regression Tree framework and the Neural Network algorithm applied to mortality data. The literature results are discussed, focusing on the forecasting performance of the Machine Learning techniques concerning the classical model. Finally, a reflection on both the great potentials of using Machine Learning in longevity management and its drawbacks is offered

Evaluation of age-specific causes of death in the context of the Italian longevity transition

: In many low-mortality countries, life expectancy at birth increased steadily over the last century. In particular, both Italian females and males benefited from faster improvements in mortality compared to other high-income countries, especially from the 1960s, leading to an exceptional increase in life expectancy. However, Italy has not become the leader in longevity. Here, we investigate life expectancy trends in Italy during the period 1960-2015 for both sexes. Additionally, we contribute to the existing literature by complementing life expectancy with an indicator of dispersion in ages at death, also known as lifespan inequality. Lifespan inequality underlies heterogeneity over age in populating health improvements and is a marker of uncertainty in the timing of death. We further quantify the contributions of different age groups and causes of death to recent trends in life expectancy and lifespan inequality. Our findings highlight the contributions of cardiovascular diseases and neoplasms to the recent increase in life expectancy but not necessarily to the decrease in lifespan inequality. Our results also uncover a more recent challenge across Italy: worsening mortality from infectious diseases and mortality at older age

Neoadjuvant treatment in pancreatic cance. Evidence-based medicine? A systematic review and meta-analysis

Neoadjuvant treatment in non-metastatic pancreatic cancer (PaC) has the theoretical advantages of downstaging the tumor, sterilizing any present systemic undetectable disease, selecting patients for surgery and administering therapy to each patient. The aim of this systematic review is to analyze the state of the art on neoadjuvant protocols for non-metastatic PaC. A literature search over the last 10 years was conducted, and papers had to be focused on resectable, borderline resectable (BLR) or locally advanced (LA) histo- or cytologically proven PaC; to be prospective studies or prospectively collected databases; to report percentage of protocol achievement and survival data at least in an intention-to-treat (ITT) analysis. Twelve studies were eligible for systematic review. Studies included a total of 624 patients: 248 resectable, 268 BLR, 71 LA and 37 non-specified. All studies were included for meta-analysis. ITT overall survival (OS) was 16.7 months (95% CI 15.16-18.26 months); for resected patients OS was 22.78 months (95% CI 20.42-25.16), and for eventually non-resected patients it was 9.89 months (95% CI 8.84-10.96). Neoadjuvant approaches for resectable, BLR and LA PaC are spreading. Outcomes tend to be better outside an RCT context, but strong evidences are lacking. Actually such treatments should be performed only in a randomized clinical trial setting

Leveraging deep neural networks to estimate age-specific mortality from life expectancy at birth.

BACKGROUND: Life expectancy is one of the most informative indicators of population health and development. Its stability, which has been observed over time, has made the prediction and forecasting of life expectancy an appealing area of study. However, predicted or estimated values of life expectancy do not tell us about age-specific mortality. OBJECTIVE: Reliable estimates of age-specific mortality are essential in the study of health inequalities, well-being and to calculate other demographic indicators. This task comes with several difficulties, including a lack of reliable data in many populations. Models that relate levels of life expectancy to a full age-specific mortality profile are therefore important but scarce. METHODS: We propose a deep neural networks (DNN) model to derive age-specific mortality from observed or predicted life expectancy by leveraging deep-learning algorithms akin to demography’s indirect estimation techniques. RESULTS: Out-of-sample validation was used to validate the model, and the predictive performance of the DNN model was compared with two state-of-the-art models. The DNN model provides reliable estimates of age-specific mortality for the United States, Italy, Japan, and Russia using data from the Human Mortality Database. CONTRIBUTION: We show how the DNN model could be used to estimate age-specific mortality for countries without age-specific data using neighbouring information or populations with similar mortality dynamics. We take a step forward among demographic methods, offering a multi-population indirect estimation based on a data driven-approach, that can be fitted to many populations simultaneously, using DNN optimisation approaches

Impact of sarcopenia on outcomes after pancreatectomy for malignancy

Background: Sarcopenia, which is a subclinical loss of skeletal muscle mass as measured by cross-sectional imaging, is commonly observed in patients with malignancy. Few studies have examined the association between the presence of sarcopenia and outcome following surgery. The aim of this study is to evaluate the prevalence of sarcopenia and to investigate its impact on short- and long-term outcomes in patients who underwent pancreatectomy for malignancy. Materials and Methods: A retrospective review of a pancreatectomy database was performed. Skeletal muscle index (SMI) was measured on preoperative cross-sectional imaging in 144 patients undergoing pancreatectomy for cancer between 2007 and 2014. Sarcopenia was defined, according to the international consensus, as an SMI <52.4 cm2 /m2 and <38.9 cm2 /m2 for men and women respectively. The prevalence and impact of sarcopenia on morbidity, mortality, disease-free and overall survivals was assessed relative to other clinicopathological factors. Results: Mean age was 67.15 years and 51% was female. Pancreatic adenocarcinoma represents 66.7% of all cases. Pancreaticoduodenectomy was performed in 114 cases (79.2%). Margin status was R0 in 76.9%. Mean BMI was 24.85 Kg/m2 and mean SMI was 35,43 cm2 /m2 . One hundred and eight (74.5%) were sarcopenic, 37 (43.5%) were overweight/ obese and 43 (29.7%) were both (p = 0.041). Sarcopenia was significantly related to histology, sex, BMI and albumin. Overall morbidity and 90-days mortality were 50.7% and 9.1% respectively. The median follow up was 21 months. Overall and disease-free survival rate were 25,44 months and 11,84 months respectively. Sarcopenia was associated to a not statistically significant increased risk of overall morbidity, mortality and shorter disease- free and overall survivals after pancreatic surgery for cancer. Conclusions: Sarcopenia was found in 74.5% of cancer patients underwent pancreatectomy. It is an occult condition in overweight/obese patients but can be identified using CT scans. This condition, as defined by international consensus, is not associated with worse short-term and long-term outcomes after surgery

The Role of Neoadjuvant Therapy in Surgical Treatment of Pancreatic Cancer

Pancreatic cancer is a leading cause of cancer-related death worldwide, and its burden is destined to increase. Multimodal treatment is crucial to achieve a cure, but standardization is far to come. Borderline resectable disease is the most challenging situation to face. An anatomically resectable disease may hide a biologically aggressive or undiagnosed systemic disease. Whether the patient has to undergo surgery first or after locoregional or systemic therapy is still unknown. Decision-making stands on low-quality evidences since RCTs are lacking. Neoadjuvant treatment may downstage the tumor and treat an early systemic disease, selecting patients for surgery in order to achieve a margin-free resection and avoid early recurrences and useless pancreatectomies. Resectable patients without other worrisome features may benefit from a surgery-first approach, while all other nonmetastatic patients should be enrolled in trials to rule out the outcomes of neoadjuvant treatments

Complication of Gastric Cancer Surgery: A Single Centre Experience

Background/aim: Gastric cancer surgery is still characterised by high morbidity and mortality. However, in 2018 an online platform, GASTRODATA has been proposed in Europe to standardize the recording of gastric surgery complications. The aim of the study was to present a single center experience regarding incidence and grading of acute postoperative complications in a population of patients treated surgically for gastric cancer on the basis of the gastrodata online platform. Patients and methods: The present study was a single center, observational, retrospective trial held in the General Surgery Unit of the Sant'Andrea Hospital of Rome. The study included 181 consecutive patients who underwent gastric surgical resection for cancer from May 2004 to December 2020 with curative R0 purpose. Results: Thirty-three percent of patients reported at least one complication, while seventeen percent of the whole population reported a complication classified as at least grade 3 on the Clavien Dindo Classification. The most frequent complications were disorders of the respiratory system (13.3%), followed by bleeding (7.6%) and wound infections (6.2%). Deaths accounted for 3.7% of the population. Conclusion: A list of defined complications of gastrectomy, if systematically adopted in the Literature, could lead to a reduction in the wide variation of proposals for treatment and assessment. Objectively evaluating the impact of complications on outcomes can lead to quality improvement project proposals

Automatic multispectral MRI segmentation of human hippocampal subfields: an evaluation of multicentric test–retest reproducibility

Accurate and reproducible automated segmentation of human hippocampal subfields is of interest to study their roles in cognitive functions and disease processes. Multispectral structural MRI methods have been proposed to improve automated hippocampal subfield segmentation accuracy, but the reproducibility in a multicentric setting is, to date, not well characterized. Here, we assessed test–retest reproducibility of FreeSurfer 6.0 hippocampal subfield segmentations using multispectral MRI analysis pipelines (22 healthy subjects scanned twice, a week apart, at four 3T MRI sites). The harmonized MRI protocol included two 3D-T1, a 3D-FLAIR, and a high-resolution 2D-T2. After within-session T1 averaging, subfield volumes were segmented using three pipelines with different multispectral data: two longitudinal (“long_T1s” and “long_T1s_FLAIR”) and one cross-sectional (“long_T1s_FLAIR_crossT2”). Volume reproducibility was quantified in magnitude (reproducibility error—RE) and space (DICE coefficient). RE was lower in all hippocampal subfields, except for hippocampal fissure, using the longitudinal pipelines compared to long_T1s_FLAIR_crossT2 (average RE reduction of 0.4–3.6%). Similarly, the longitudinal pipelines showed a higher spatial reproducibility (1.1–7.8% of DICE improvement) in all hippocampal structures compared to long_T1s_FLAIR_crossT2. Moreover, long_T1s_FLAIR provided a small but significant RE improvement in comparison to long_T1s (p = 0.015), whereas no significant DICE differences were found. In addition, structures with volumes larger than 200 mm3 had better RE (1–2%) and DICE (0.7–0.95) than smaller structures. In summary, our study suggests that the most reproducible hippocampal subfield FreeSurfer segmentations are derived from a longitudinal pipeline using 3D-T1s and 3D-FLAIR. Adapting a longitudinal pipeline to include high-resolution 2D-T2 may lead to further improvements